IEEE JOURNAL OF OCEANIC ENGINEERING, VOL. 26, NO. 4, OCTOBER 2001
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Detection of Objects Buried in the Seafloor by a Pattern-Recognition Approach Andrea Trucco, Member, IEEE
Abstract—Systems able to retrieve objects embedded in the seafloor are of crucial importance for many different tasks. In this paper, an experimental assessment of a detector applying the “classify-before-detect” paradigm is proposed. The evaluation is based on real data acquired, during two sea trials, by two different sonar systems using low grazing angles and placed far from a target object. The “classify-before-detect” paradigm is a pattern-recognition approach to designing a classifier aimed at distinguishing between two classes (i.e., target presence and target absence), just like a detector. This approach has been selected and developed as it is very well suited to exploiting the available statistic and spectral a priori information on the target echo. In short, some features are extracted from the Wigner–Ville distribution and the bispectrum of partially overlapped short segments of the acquired echo signals. The dimensionality of the problem is reduced by the principal-component analysis, and the reduced feature vector is sent to a supervised statistical classifier, e.g., the multivariate Gaussian one. The ideal training set is composed of pure reverberation signals and the responses of the target in free field at different aspect angles. However, also not ideal training sets were tested. Although the signal-to-reverberation ratio was close to 0 dB, the above method yielded very satisfactory results in terms of detection and false-alarm probabilities, also in comparison with other more traditional detection methods. Index Terms—Acoustic detection, buried objects, feature extraction, pattern recognition, sonar systems.
I. INTRODUCTION
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N THE recent past, some techniques based on pattern-recognition theory [1], [2] have been proposed and exploited for several underwater tasks, like classification of the seafloor [3], [4], detection and classification of submarines and mines by active sonar systems [5], [6], detection and classification of transient signals collected by passive sonar systems [7], [8], classification of fish [9] and vegetation [10], detection and classification of buried objects [11]–[13]. Essentially, the signal to be classified is projected into favorable spaces by one or more projection methods, and a vector of features is extracted from these spaces and moved to the classifier, which determines the class to which the signal belongs, possibly on the basis of a preliminary training stage. This general procedure can be used in many different ways and combinations, according to the nature of the signals and the final aims. Fast Fourier transform [8], [11], auto-regressive modeling [8], [11], short-time Fourier transform Manuscript received March 27, 2000; revised December 13, 2000 and May 29, 2001. This work was supported in part by the European Commission project, Detection of Embedded Objects (DEO 1996–1999) under Grant MAST3-CT950009. The author is with the Department of Biophysical and Electronic Engineering (DIBE), University of Genoa, I-16145 Geneva, Italy, (e-mail:
[email protected]). Publisher Item Identifier S 0364-9059(01)07890-6.
[7], [9], wavelet transform [8], higher order moments [3], [10], spectra [12], and fractal analysis [4], [13] are only some examples of projection techniques most often applied. Also concerning the classifier module, some different approaches have been proposed and successfully tested, such as the traditional Bayesian classifiers [5]–[7], nonparametric techniques (e.g., the nearest neighbor classifier) [7], [10], [12], [13], neural networks [3], [7], [8], [11], and Markov models [8], [9]. The aim of the paper is to prove that a method based on pattern-recognition theory can be very useful in the automatic detection of objects buried in the seafloor by processing the signals collected by an active sonar system placed far from the bottom and working at a very low grazing angle. The detection of embedded objects is a particularly difficult task of crucial importance for the retrieval of toxic wastes, mines, archaeological finds, pipelines, and cables. Acoustic systems play a significant role in the detection of objects partially or deeply buried in the seafloor, but, despite the extensive research already performed [11]–[17], a completely satisfactory system has not been designed yet. In [11], a dolphin-like acoustic pulse and a biomimetic signal processing are used to emulate the performance of the dolphin in this task. A digitized dolphin click with a center frequency of 120 kHz was transmitted and the echoes were projected into two time-frequency spaces. Starting from these two spaces, two neural networks derived independent detections and independent classifications of the target. The final classification was obtained by combining the two preliminary ones by means of a probabilistic network followed by an expert system. This system has been used to classify cylinders of different materials, about 10 cm high, buried in mud at a depth of several centimeters, placing the sonar system about 1 m far from the bottom. In [12]–[14], a sonar source, with a center frequency of 50 kHz, is moved along horizontal paths over the seafloor interface, at normal incidence, in order to acquire three-dimensional (3-D) data on the subbottom area. Wavelet packets are used to reduce the noise affecting such data, higher order spectra (HOS) are used both to improve the range resolution and for a first classification step, and Fourier descriptors are used for an improved classification step [12], [14]. Also, the features extracted from the entropy of the wavelet-packet coefficients and from a fractal analysis have been investigated and compared in object classification [13]. In [15], an experiment is described that was aimed at detecting a long cylinder, 2.5 cm in diameter, buried under about 30 cm of sand, by using a focused transducer with a center frequency of 70 kHz placed at a height of 23 cm above the bottom. A good detection opportunity can be obtained by using the matched-filter output. The methods described in the above mentioned papers share the following characteristics.
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They have been proven valid only for short distances (less than 2 m) between the acoustic transducers and the seafloor surface, and the grazing angle is normal or close to normal. In [16] and [17], some images of the subbottom are shown from which it is possible to deduce the potential presence of buried objects, like pipelines and mines. Such images (subbottom profiles) are typically obtained by using acoustic sources working at low frequency (5–15 kHz), placed some tens of meters far from the seafloor, but always at normal incidence. Moreover, no methods of automatic detection are developed in all the aforesaid papers. The need for working at a normal or quasi-normal grazing angle limits the practical exploitation of an acoustic system aimed at finding embedded objects. First of all, for safety reasons, it is not always possible to move above an area potentially containing an object. Moreover, lower grazing angles result in a very interesting advantage, i.e., the observation of a large area of the seafloor without need for moving the sonar source. One has simply to rotate and incline it. These considerations guided a research project (funded by the European Commission) called DEO (Detection of Embedded Objects, Project no. MAST3-CT95-0009 DEO), in which the capabilities of a sonar system to detect buried objects at distances of several tens of meters and working at very low grazing angles (even subcritical) were experimentally assessed. Obviously, the ratio between the power of the target echo and the power of the bottom reverberation signal (called, signal-to-reverberation ratio, SRR) is sharply reduced and the system encounters greater difficulties in detecting the echo target (if any) against the clutter produced by the seafloor. The large amount of data collected at sea during the DEO project have been exploited in different ways [18]–[20]. In [18], an investigation of the sound penetration into the sediment versus the frequency and the grazing angle is presented. The characteristics of the echo of a buried target with respect to reverberation are analyzed, showing that the amplitude of the acquired signal provides good detection opportunities, but only when one works above the critical angle. In [19], the wavelet transform is used to analyze the acquired signals, and it is shown that the main physical features of the free-field target remain valid also when the target is buried. Although the analyses made in [18] and [19] can be very useful in target detection and classification, an automatic procedure was not proposed. In this paper, the data acquired during the DEO project are used to test the effectiveness of the “classify-before-detect” paradigm [5], [7], [21] in detecting the presence of buried objects. This means to develop a classifier that assigns a signal to one of two possible classes, i.e., a classifier that acts just like a traditional detector. This paradigm has been chosen because, at low SRR values, traditional energy detectors are inadequate since they take into account only amplitude information. If one had an a priori knowledge of the general structure of acoustic signatures, one could use this information to design a more effective detector. The “classify-before-detect” paradigm attains this goal by taking into account a great number of features (the choice of which can also be empirical) to increase the process robustness. Such a paradigm utilizes more favorable decision spaces containing discriminating features rather than just the amplitude decision space.
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Although the approach proposed here is not completely new from a methodological standpoint [5], [7], [11], [21], to the best of the author’s knowledge, this paper provides innovative contributions as it presents the first method able to automatically detect buried objects that are observed at subcritical grazing angles and that are placed at distances of some tens of meters (both characteristics are great advantages in operational terms). A similar attempt was made only in [20], where the author assessed (partially using the same data) the performance of a detector composed of an adaptive prewhitening filter followed by a bank of matched filters. However, unlike the method proposed here, the one described in [20] strictly requires that the acoustic response of the target to be detected, measured in free-field conditions, be known a priori. A brief performance comparison between the two methods will be made (when applicable) in Section IV. Moreover, in the same section, it will be shown that the simple matched filtering, often effective for detection under normal incidence, does not behave well with the signals and the geometry considered here. Another important feature of this paper lies in the fact that the proposed method has been validated on real data acquired at sea during two experiments performed under different conditions and by different equipment. The paper is organized as follows. Section II defines the main operations inherent in the “classify-before-detect” paradigm, i.e., data projection, feature extraction, and supervised classification. Section III describes how to perform such operations for the detection of a buried target, with reference to data concerning a first sea trial. In Section IV, the obtained results are reported and discussed, and some comparisons with more classical methods of detection are made. Section V provides some additional results and discussions regarding a second sea trial for which the a priori information was very limited. Finally, some conclusions are drawn in Section VI. II. CLASSIFY-BEFORE-DETECT PARADIGM The classify-before-detect paradigm [5], [7], [21] is a general approach to the detection of signals that are partially known and deeply embedded in noise. The basic sequence of operations is the following: signal conditioning, data projection, feature extraction, feature analysis, and statistical classification. These operations can be carried out in many different ways, depending on the applications addressed. In the following, brief descriptions of such general operations are given, together with some details about the specific solutions that have been adopted for the detection of buried objects, as shown in Fig. 1. Signal conditioning is the operation most related to the nature of specific data. Thus, it will be faced in Section III, where the practical implementation is described. A. Selected Techniques for Data Projection In general, target echoes (if any) and reverberation are completely overlapped. Therefore, it is important that features be derived from projection spaces where the signals belonging to one class are represented compactly and separately from those of another class. To this end, data-projection techniques are applied to acquired signals (after suitable conditioning) to focus
TRUCCO: DETECTION OF OBJECTS BURIED IN THE SEAFLOOR BY A PATTERN-RECOGNITION APPROACH
Fig. 1.
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Flowchart of the classify-before-detect approach in the specific implementation developed for the detection of buried objects.
on different signal attributes that are not directly visible in the waveforms. The spectrum is the most common way to project data from which to extract features to be used for classification purposes. However, the spectrum is not the best tool for analyzing the signals considered in this paper for their finite and relatively short duration. As the frequency content of the signals to be examined evolves over time, a joint time-frequency analysis is necessary [22], [23]. This can be achieved by means of a time-dependent spectrum. Although many alternatives exist to compute a time-dependent spectrum [22], [23], each with specific advantages and drawbacks, in the present study, the Wigner–Ville distribution (WVD) has been adopted. The WVD has been widely applied to sonar systems [22]–[25] (with particular reference to the analysis of object scattering), thus making it possible to obtain very good time and frequency resolutions. According to the , of a Wiener–Khintchine theorem, the energy spectrum, , is given by the Fourier deterministic finite-energy signal, transform of the signal correlation function, (1)
which is the WVD. Thanks to its property, the WVD can be thought of as a signal energy distribution in the joint time-frequency domain [22], [23], [25]. It is interesting to note that the integration of with respect to time returns the signal energy spectrum (4) In estimating a spectrum (time-dependent or not), the distribution of the energy of a given signal among its frequency components is determined, whereas the phase relations among such components are suppressed. However, there are some practical situations (like those involved in the study of underwater acoustic scattering) where one has to look beyond the spectrum to extract the presence of phase relations. HOS [26]–[28], defined in terms of higher order statistics, can be useful to this end, as shown, for instance, in [29], [30]. The extraction of the same information (i.e., potential phase relations [29]) by means of the short-time Fourier transform is surely less practical and immediate. In (2), the second-order correlation function of a deterministic finite-energy signal has been defined. By analogy, one can introduce the third-order correlation, called bicorrelation function, in the following way: (5)
(2) is called “instantaneous correlawhere the function is obtained by means tion.” The conventional correlation of an averaging operation that removes all time information. By analogy to (1), the time-dependent spectrum can be obtained by the Fourier transform of the instantaneous correlation:
The bispectrum (the first of the HOS) can be defined as the two-dimensional (2-D) Fourier transform of the bicorrelation function:
(6) In [28], it is proved that the bispectrum of a finite-energy signal, as defined in (6), can be equivalently computed as follows: (7)
(3)
is the Fourier transform of where complex conjugate operation.
, and indicates the
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In this paper, according to the above-mentioned reasons, the use of the WVD and the bispectrum to project data onto favorable domains (i.e., time–frequency domain and frequency–frequency domain) is proposed and assessed on the basis of experiments. In the following, it will be shown how these techniques have been applied to acquired data, and an attempt at results interpretation will made. However, many other techniques for signal analysis are possible and suitable. One cannot exclude that they may provide equal or better results. The choice proposed here is supported by other works where the WVD and the HOS are jointly used to analyze transient signals [31]–[33]. In such works, the WVD and the HOS are not applied as distinct tools but are integrated into the Wigner higher order moment spectra (WHOS), which attempt to represent the temporal variation of the higher order spectra (e.g., the bispectrum) of a signal. The WHOS have a time-multifrequency support domain that is not simple to investigate. 2-D slices can be computed by considering only a 2-D domain (in particular, the Wigner bispectrum [32], [33]). In this paper, is often sliced by imposing the analysis of the acquired data is performed on partially overlapped short segments. For each data segment the bispectrum is computed, mainly to evidence the presence of phase-coupling effects. In this specific case, the evolution of the bispectrum over a short data segment does not seem to be important. Consequently, the WHOS are not applied.
B. Feature Extraction and Analysis The statistical classification of a signal requires the extraction features (i.e., numerical values derived from of a vector of the projections of the signal or signal segment to be classified) in such a way that the signal is mapped into a single point in an -dimensional feature space. Typically, the search for suitable features (those able to discriminate between signals corresponding to the absence of a target and signals corresponding to the target presence, and possibly not dependent upon the aspect angle of the target itself) does not result in a perfect set of features. Some may be more important than others and, in addition, several of them do not provide useful information because they are sensitive to noise, redundant, or intrinsically insignificant. This problem is faced and solved by the next step, i.e., feature features extracted for the analysis. The specific choice of the problem dealt with here will be addressed and discussed later on, as it requires a knowledge of the experiments and of the general characteristics of the signals. Feature analysis involves feature normalization and reduction in problem dimensionality. Normalization removes biases associated with different scaled features (in terms of measure units and numerical values) and prevents possible numerical ill-conditioning [2], [7], [21], [34]. Once a training set (i.e., a large set of signals for which the ground truth is known and which bematrix, long to different classes) has been composed, an , can be arranged, where is the cardinality of the training is the value of the th feature of the th set, and the element signal. Such a matrix should be normalized to achieve a new matrix, , in which each feature is characterized by zero mean
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and unit variance, by using the following equation: (8) where and are the mean and the standard deviation of the row of the matrix , respectively. Reduction in problem dimensionality is useful to decrease the potential redundancy of features and the high computational load required by the large dimension of the full feature vector. This operation can be performed in several ways [1], [2], [10], [21], [34] but, unfortunately, there is no optimal way. One of the most widely used methods is the principal-component analysis (PCA), which generates a reduced feature vector (composed of elements, ) by applying an feature-reduction matrix to the full feature vector, thus avoiding a high computational load. The feature-reduction matrix is computed on the basis of the eigenvectors and the eigenvalues of the data-covariance matrix, as described in [1], [2], [35]. The reduced feature vector is a linear combination of the full vector and holds a considerable fraction of the original information, measured in terms of covariance energy. C. Supervised Classifiers Supervised classification starts with a training phase, in which the training set is exploited to prepare a classifier that, later on, in the operational phase, will assign each feature vector to a specific class [1], [2]. The selection of a good classification scheme from among the many techniques proposed in the literature depends mainly on the probability density functions (PDFs) of features and on the number of feature vectors available for the training phase. Also, the computational load and the required memory space are important selection parameters. In this paper, the multivariate Gaussian classifier (MGC) is adopted. It is one of the standard Bayesian classifiers and involves a low load and reasonable memory requirements in both the training and operational phases [1], [2], [21]. This parametric classifier assumes that the joint feature PDF of each class is multivariate Gaussian. It is characterized by the mean vector and the covariance matrix . Even though the assumption about the feature PDFs may seem too restrictive to model reality, which sometimes exhibits non-Gaussianity and multimodality, an investigation of such PDFs in the addressed case has pointed out that they show unimodal characteristics with a reasonable class separation. To classify an input feature vector, the MGC computes the Mahalanobis distance from each class and chooses the nearest class. The Mahalanobis distance, , of a feature vector from the class is the following: (9) It differs fundamentally from the Euclidean distance in that the statistical properties of data are explicitly considered [1], [2]. Let be the total number of classes. The class label, , chosen for an input vector is computed as follows: (10)
TRUCCO: DETECTION OF OBJECTS BURIED IN THE SEAFLOOR BY A PATTERN-RECOGNITION APPROACH
To sum up, the full feature vectors of the training set are used to compute the mean and the standard deviation of each feature ( and , respectively). After the normalization step, the feature-reduction matrix is computed. Finally, after a reduction in the dimensionality of the training set, the mean vector and the covariance matrix of each class ( and , respectively) are estimated. During the operational phase, to classify a full feature vector, each element of the vector is normalized using the related values and . Then, the vector dimension is reduced using the feature-reduction matrix and the reduced vector is obtained. Finally, the Mahalanobis distance of from each class is computed using and , and the nearest class is chosen. The MGC has proven to provide excellent performances in many applications, in particular, in the classification of active sonar data [21]. The authors of [21] made an in-depth comparison of many parametric, nonparametric, and boundary-decision classification schemes, characterized by different complexities and computational loads. Eventually, they selected the MGC as the best classifier, suited for real-time implementations. III. DEVELOPING A DETECTOR OF BURIED OBJECTS The classify-before-detect approach was implemented and tested on real data collected during two sea trials (here referred to as Sea Trial A and Sea Trial B) that were different in the equipment, the seafloor areas, the buried targets, and the geometrical placements. A. Experimental Setup in Sea Trial A Sea Trial A was performed by Nato Saclantcen, La Spezia, Italy. In a preliminary experiment [36], the acoustic response of a water-filled thin-shell steel cylinder (2-m long and 0.5 m in diameter, with flat end-caps), which had been placed in a basin 40 m far from a sonar source, was acquired by a hydrophone placed between the sonar source and the cylinder. Like in previous investigations [12]–[14], the sonar source was a parametric array, TOPAS PS 040, emitting Ricker pulses with a secondary frequency between 4 and 10 kHz. For each frequency, many pings were emitted and the backscattered signals were acquired at some different aspect angles of the cylinder. During the trial at sea [37], the same cylinder was buried off the Island of Elba, Italy, at a site where the water column was about 10-m deep and the seafloor was a 1.8-m thick homogeneous layer of fine and compact sand. The cylinder was buried in the sand at a depth of about 0.8 m. After a few months, the sediments (moved during the burying phase) recovered their original status. The parametric sonar transmitter and a 16-element vertical array (used as a receiver) were mounted on top of a tower. The tower was 8-m height and could move on a rail placed parallel to the buried cylinder and 18 m far from it. The total distance of the sonar to the buried target was about 20 m, depending on the position of the tower along the rail. Moving along the rail, the sonar could see the cylinder at different aspect angles. The receiving array inter-element spacing was 94 mm. Using the described experimental setup, data were acquired for different configurations. Fixed configuration: at a given rail position, the pan and tilt angles of the transmitter were mechan-
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ically tuned to point toward the buried target, and many pings were emitted. Moving configurations: after fixing the tilt or pan angle so as to point toward the target, the other angle was linearly moved, while a sequence of pings of the same type was emitted and the backscattered signals were recorded. Thus, unlike the fixed configuration, the moving configurations allowed only one of the emitted pings to be pointed exactly toward the target. The latter configurations are called “moving-pan” and “moving-tilt” configurations, respectively. It is very important to note that, in both the fixed and moving configurations, the grazing angle (measured at the target position) was equal to about 24 . It was lower than the fine-sand critical angle ( 26 to 28 ). B. Data Preprocessing and Feature Extraction The data related to the fixed configuration were preprocessed by beamforming [24] at a steering angle equal to the tilt angle set by Saclantcen for the experiments [37]. Also, the data obtained by moving-pan experiments were preprocessed by beamforming at a constant steering angle equal to the tilt angle. Instead, the data provided by moving-tilt experiments were preprocessed by beamforming at a steering angle that, ping by ping, was equal to the current tilt angle, under the assumption of a linear increment of the tilt. After beamforming, the estimated SRR ranged between 0 and 2 dB. For each beam signal, a sequence of successive signal blocks was moved to the detector. In greater detail, each signal was segmented into 2-ms long intervals partially overlapped, the dc component was removed, and, to prevent the echo amplitude from providing any potential help in detecting, each block was normalized to 1. It should be noted that the normalization process keeps the advantages of the beamforming array gain but drops out the effect of the beamforming spatial directivity [24]. The spatial directivity of the beamforming operation is exploited only inside each single block. For instance, the echo coming from a surface reflection, strongly abated by a beamforming pointed toward the bottom, will have the same amplitude as the bottom echo after amplitude normalization (under the assumption that the two echoes are in different blocks). Instead, the array gain resulting from beamforming is retained: it improves the signal-to-noise ratio (SNR) at the output of beamforming over the SNR of a single array element [24]. However, the noise considered here does not include any surface or bottom reverberation signals. Due to the normalization process, the proposed detector was tested in an unfavorable, strongly pessimistic condition. The features making up the full vector are derived from the two projection spaces previously described. To this end, it is quite important to have a general idea of the main differences between target echoes and bottom-reverberation signals. From theoretical predictions and experimental analyses carried out during the DEO project, it has been deduced [18], [38] that: • A target echo is mainly composed of one or more (depending on the aspect angle of the target) distorted replicas of the insonification pulse that are bounded over time, whereas bottom reverberation appears as a longer signal much less compact over time.
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(a)
(b)
(c)
(d)
Fig. 2. (a) WVD of the echo of a partially buried-target. (b) WVD of a pure reverberation signal. (c) Bispectrum of the echo of a partially buried-target. (d) Bispectrum of a pure reverberation signal.
• As also shown in [18], a target echo has a frequency response that is approximately constant over the insonification band (despite the aspect angle may cause some differences), whereas the response of a sandy bottom presents some oscillations and tends to increase with the frequency. • A trace of the water nonlinearity exploited by the parametric source in generating the Ricker pulse at secondary frequency is still present in target echoes (phase-coupling effect), whereas it is less evident or sharply altered in bottom reverberation. When a target is buried, its echo is overlapped with bottom reverberation, and the above characteristics, are not so evident but still present [38]. However, working at subcritical grazing angles, some changes have been observed [18]. The target-echo duration increases, and the frequency response of the target decreases as the frequency increases. Whereas the first effect makes the target echo closer to bottom reverberation, the second favors the separation of the two signals. Other characteristics, like the resonance generated by the target cavity, have not been taken into account as it is acknowledged that they vanish at subcritical angles [38]. These characteristics and considerations justify the following behaviors: In the WVD, energy is concentrated under some clear peaks, over both the time and frequency axes. By contrast, the WVD of a bottom-reverberation signal is, in general, more spread over the time-frequency plane, in particular, energy is mainly distributed over the upper
fraction of the frequency band. An example of this fact is shown in Fig. 2(a) and (b). The bispectrum of a target echo is quite similar to that of the Ricker pulse, i.e., it often shows the same important peaks, whereas the bispectrum of a bottom-reverberation signal exhibits, in general, many more low peaks, which are more spread over the bifrequency plane. The peaks of the bispectrum of the Ricker pulse are less evident in the bispectrum of a reverberation signal. An example of this fact is shown in Fig. 2(c) and (d). The features to be extracted have been chosen to capture the different behaviors described. The Appendix describes the full feature vector, which contains 43 elements: 29 features from the WVD, and 14 features from the bispectrum. A first portion of the features extracted from the WVD of a signal segment [feature nos. 1) to 12)] tries to evaluate the smoothness of the energy distribution and of its projections onto the axes, the number of important peaks, their proximities, the distance from the absolute maximum peak, the uniformity of the peak magnitudes, the mean position of the peaks on the frequency axis, and the time and frequency extension of the absolute maximum peak. Feature nos. 13) to 17) are used to compare the peak magnitudes and the shape descriptors over specific bands, and to measure the similarity of the actual WVD to two a priori known distributions. The signal energy contained in adjacent frequency bands is computed [feature nos. 18) to 24)] by deriving the energy spectrum from the WVD. This spectrum is not related to
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Fig. 3. Class separation in the feature space. Buried target at the broadside aspect angle, target floating in free water at different aspect angles, sea bottom reverberation, reverberation inside a basin, reflection from the basin floor. The feature reduction has been obtained by the PCA technique.
the whole acquired signal but only to the 2-ms segment under examination. A few statistical descriptors of the spectrum shape are also considered, and two comparisons of the spectral magnitudes over specific bands are made [features nos. 25) to 29)]. Concerning the bispectrum, its absolute value is considered. A first portion of the extracted features [features nos. 30) to 37)] tries to evaluate its smoothness, the number of peaks, the absolute maximum peak, the contributions of the bispectrum in a given circular-ring domain, and the similarity of the current bispectrum to two a priori known bispectra. The remaining features [nos. 38) to 43)] are related to the observation of the prin) with recipal bispectrum diagonal (i.e., the slice at spect to its smoothness and the positions, spreading and extent of the peaks. IV. RESULTS AND DISCUSSION All the results reported in this section refer to Sea Trial A, in which the insonification process was obtained by the emission of a Ricker pulse with a carrier frequency of 8 kHz and a timeproduct equal to about 1.125. bandwidth A. Class Separation and Training-Set Composition A first result concerned the separation of the two classes (H and H indicate the target absence and the target presence, respectively) in the feature space. In Fig. 3, a 3-D feature space has been used to show the points related to a large collection of signals gathered during both experiments (i.e., in the basin and at sea). Echoes of the buried target at broadside position, echoes of the target floating in free water at different aspect angles, sea bottom reverberation, reverberation inside a basin, and reflection from the basin floor have been included. Unlike reverberation, the last class consists of specular reflections produced by the concrete floor of the basin, and acquired using a bistatic
sonar configuration. Observation of the plot in Fig. 3 suggests some remarks. • Reverberation (from the sea bottom or inside the basin), basin reflection, and free-field target echoes are well-split, and the chosen features are successful in separating them. • Bottom reverberation and basin reverberation are quite similar and partially overlapped. • According to the physics of the experiments, the echoes of the buried target are placed between free-field target echoes and bottom reverberation. The closeness to bottom reverberation and a partial overlapping with it make buried-target detection a nontrivial task. In addition, the distance from reverberation is equal to or shorter than that from free-field echoes. This fact confirms an SRR close to 0 dB. To arrange the training set, about 1000 feature vectors related to signals belonging to the two classes were collected. In particular, the class H of the training set has been composed by using only the responses of the target floating in free water, at many aspect angles, considering the signals gathered during the preliminary experiment in the basin. Therefore, no buried-target echoes were utilized to make up the training set. The practical consequence is that, to find a buried object, it is not necessary to know a priori the echoes of the object embedded in the seafloor, but one need only have the free-field responses of the target and some samples of bottom reverberation. Finally, by exploiting the training set, the PCA technique fixed a feature-reduction matrix able to decrease the number of features from 43 to 26, including more than 95% of the total covariance energy. B. Fixed-Configuration Results Three rail positions were considered during the fixed-configuration experiments, namely, P1, P2, and P3. At rail position P1,
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Fig. 4. Detection in Sea Trial A for a fixed configuration and rail position P1. The expected detection time was 26 ms. Dark boxes show the signal segments for which the classifier gave H outputs.
Fig. 5. Detection in Sea Trial A for a fixed configuration and rail position P2. The expected detection time was 26.2 ms. Dark boxes show the signal segments for which the classifier gave H outputs.
the cylinder was observed at a broadside aspect angle (i.e., aspect angle 0 ), at rail position P2, the aspect angle was 6 , and at rail position P3, the aspect angle was 9.5 . Figs. 4–6 show the detection results obtained for all the pings emitted from the three rail positions, respectively. In Fig. 4, all the pings show a detection over the 2-ms interval centered at the expected time, i.e., 26 ms. Only few of them show a second detection (about 4 ms later), which is due to the reflection from the sea surface. It has been shown [18] that, at low grazing angles, more energy could be scattered from buried targets in directions closer to the vertical one rather than in the monostatic direction (also depending on the target aspect angle), thus enforcing the sea surface reflection. Fig. 6 presents a situation similar to that in Fig. 4. The time intervals over which detection occurred are the same,
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Fig. 6. Detection in Sea Trial A for a fixed configuration and rail position P3. The expected detection time was 26.3 ms. Dark boxes show the signal segments for which the classifier gave H outputs.
but only a few pings show the first detection, whereas most of them also show the second detection. At the intermediate rail position (see Fig. 5), most of the pings show both a first and a second detection, but the two time intervals are 3 ms delayed, as compared with those in Figs. 4 and 6. Thus, the first detection is late in comparison with the expected time, i.e., 26.2 ms. Such results are summarized and compared with geometrical expectations in Table I. On the basis of the large number of and Pr , emitted pings, two detection probabilities, Pr were evaluated. The first takes into account only those pings for which the target detection occurred in due time (i.e., the expected time is within the obtained detection interval), whereas the second takes into account all the pings for which a detection occurred, at the expected or later time instants. It is worth noting that, at all the three rail positions, the detection probawas not lower than 98%, and that no false alarms bility Pr were experienced. Concerning the detection time, one can observe that the expected time is spread over an interval of 0.3 ms, with a geometrical dependence on the rail position, whereas the obtained time is spread over a longer interval (about 3 ms), without a clear relation with the rail position. The reason for this discrepancy is twofold: the discrete length of the time interval (2 ms) and the uncertainties in the distance measures during the experiments. In particular, the experiments were performed in open-sea environments, repeating several times the equipment deployment over a period of some days, hence under different weather and sea conditions. A potential question may be raised as to the validity of the results obtained for fixed configurations. In other words, is the detected item really the cylinder? An affirmative answer can be given, considering that, although the seafloor reverberation started at about 22 ms, no earlier detection (i.e., before the expected time) was performed at all. Moreover, the results obtained for moving configurations (see next subsection) prove that only the correct target was detected and not a generic bottom reflection.
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TABLE I DETECTION IN SEA TRIAL A FOR A FIXED CONFIGURATION AND THREE RAIL POSITIONS. Pr IS THE PROBABILITY RELATED TO THE DETECTION AT THE IS THE PROBABILITY RELATED TO THE DETECTIONS AT BOTH THE EXPECTED AND LATER TIME INSTANTS EXPECTED TIME, AND Pr
Final considerations concern the grazing angle and the pulse frequency. Some fixed-configuration experiments were performed by SACLANTCEN that were similar to those of Sea Trial A, but at higher grazing angles than the critical one [18], [37]. The same detection scheme was applied to the related data, and the achieved results were of equal quality, as compared with the results previously reported. Also, the pulse frequency is not a critical parameter, as similar results were obtained by using a Ricker pulse with a frequency of 5 or 8 kHz. These facts point out that the proposed detection scheme is quite general and has not been specifically designed for a given grazing angle or frequency. C. Moving-Configuration Results Concerning the moving configuration experiments, the obtained results are shown in Fig. 7 and compared with the geometrical position of the target in Table II. Both moving-pan [Fig. 7(a)] and moving-tilt [Fig. 7(b)] experiments were carried out using the sonar tower at rail position P1. The results are quite satisfactory. In both cases, a clear and bounded detection box is present, that contains the target position on the time-angle plane, as one can see in Fig. 7 and Table II. In the moving-pan case, only one detection box is present with an angular extension lower than 3.5 , whereas, in the moving-tilt case, two small detection boxes are also present on the two sides of the main box that contains the target position. The overall angular extension of the three boxes is about 5 . In both cases, the agreement between the expected and actual detection times is very good. As occurred for fixed configurations, no false alarms were experienced. D. Comparison with Other Methods Concerning the detection problem, the matched-filtering approach is often adopted for its optimal performance in detecting signals embedded in additive white Gaussian noise. Although the echo of an embedded target is only partially known, matched filtering has been used [15] in systems working close to the bottom, at incidence angles close to normal ones. For the sake of comparison, Fig. 8(a) shows the output signals from a filter matched to the target response (recorded in free field at broadside position) and applied to the data of the moving-pan experiment. Although the right detection peak is present at due position, some false-alarm peaks occurred at wrong angular and/or time positions. The performance of the product that is matched filter is obviously bounded by the close to 1.
Fig. 7. Detection in Sea Trial A for a moving configuration and rail position P1, i.e., broadside aspect. Dark boxes show the signal segments for which the classifier gave H outputs. (a) Moving-pan experiment: expected detection was at 8 , 26 ms. (b) Moving-tilt experiment: expected detection was at 21.6 , 26 ms.
0
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To improve the performance, it is necessary to abate reverberation by taking into account that its spectrum is not white and varies over time. To this end, the author tried to insert of
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TABLE II DETECTION IN SEA TRIAL A FOR MOVING CONFIGURATIONS AND RAIL POSITION P1. COMPARISONS BETWEEN EXPECTED AND OBTAINED ANGLES OF DETECTION, AND BETWEEN EXPECTED AND OBTAINED DETECTION TIME INSTANTS
the prewhitening filter. In this case, a bank of filters matched to the target responses at different aspect angles replaced the single matched filter. As preliminarily shown in [20], the detection probability, Pr , was equal to 0.94, 0.97, and 0.92 at rail positions P1, P2, and P3, respectively. If compared with the results obtained by the classify-before-detect approach and given in Table I, the matched filters provided similar, slightly poorer performances. The main disadvantage of the matched-filter technique is that it needs a knowledge of the free-field response of the target. The use of the response of the target embedded in bottom sediments does not yield any constructive result. Instead, the classify-before-detect method allows the free-field target response or the response of the embedded target to be used in training the system, depending on the data that are a priori available. The first option provides better results, but the system works properly also with the second option, as will be shown in the next section. Therefore, the classify-before-detect method is more flexible and can be successfully applied in different operating conditions. V. RESULTS WITH A NONIDEAL TRAINING SET A. Experimental Setup in Sea Trial B
Fig. 8. Output signals in the moving-pan experiment (rail position P1) obtained by a filter matched to the free-field response of the target at the broadside aspect angle. Expected detection was at 8 , 26 ms. (a) Input data were the same as in Fig. 7(a). (b) Input data were processed by an adaptive prewhitening filter.
0
an adaptive prewhitening filter before the matched filter [20]; such a prewhitener is based on the modeling of reverberation as an autoregressive (AR) process. The results obtained on the moving-pan data are depicted in Fig. 8(b), where a single peak is present at the right angular and time positions, in accordance both with the results obtained by the classify-before-detect method [see Fig. 7(a)] and with the geometrical expectations. Also, data coming from fixed configurations were processed by
Sea Trial B was jointly performed by Loughborough University (LU), U.K, and TNO-FEL, The Netherlands. A water-filled thin-shell steel cylinder (1-m long and 0.25 m in diameter, with flat end-caps) was buried off Loch Duich, Scotland, in a muddy seafloor, at a water depth of about 43 m [39]. The sonar source was a parametric array built by LU [40]. It emitted Ricker pulses with a secondary frequency between 4 and 10 kHz. The source was submerged 10 m and pointed toward the cylinder, which was about 50 m far from it. The resulting incidence angle was about 41 . A receiving array, composed of 20 elements 25 cm apart, was vertically deployed close to the sonar source (see a top-view in Fig. 9), in such a way that its center was at a depth of 15.5 m and 48 m far from the target. The resulting grazing angle was about 35 . For each type of pulse and frequency, many pings were emitted and the backscattered signals were acquired for different aspect angles of the cylinder. The target was deployed using a two-point suspension that allowed it to be lifted, dropped, and horizontally rotated. The target was buried by dropping it to the bottom, at a given aspect angle, and waiting for a natural submersion in the soft muddy sediments. It was observed that the submerging process stopped after half the target had disappeared in the bottom, thus resulting in a partial
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Fig. 9. Top-view of the experimental setup used in Sea Trial B. The water column was about 43 m, the sonar source was 10-m deep, and the array center was 15.5-m deep.
burying. Therefore, the main experimental differences between Sea Trial A and Sea Trial B are that, in Sea Trial B, the target was farther and only partially buried, the transmitter and receiver antennas were several meters apart, and the grazing angle was higher than the critical one. B. Training-Set Composition Besides the experimental differences, the main divergence from Sea Trial A is the absence of free-field target echoes. Available data contain target echoes that were acquired at many aspect angles but always from the partially buried target. Thus, such echoes are sharply affected by reverberation. The low SRRs of the target echoes do not make it possible to compose an efficient training set, and may negatively affect the results by increasing the probability of false alarms. To arrange the training set, about 100 feature vectors related to signals belonging to the two previously described classes were collected. In particular, as regards class H , the target echoes due to the emission of many pings for each of seven different aspect angles were included. Because of the low SRR, it is difficult to select the signal interval containing the target echo, and some errors may be incurred. Therefore, it is not ensured that all the signals representing class H will really contain a target echo. As previously done for Sea Trial A, the number of features was then reduced from 43 to 26 with the PCA technique, and the data included in the training set were not used to test the detector; in this way, about 165 pings remained available for the testing phase.1 . C. Results, Confirmation Rule, and Discussion All the results reported in this subsection refer to an insonification process obtained by the emission of a Ricker pulse with a carrier frequency of 4 kHz. As a sample of the obtained results, Figs. 10–12 show the behaviors of the system in 5 cases: Fig. 10 refers to aspect angles of 4 and 10 , Fig. 11 refers to aspect angles of 43 and 77 , and Fig. 12 describes a case of pure reverberation (i.e., absence of target). When a target is present, the highest row of each panel displays the expected position of the target echo (lasting over 3 ms due to uncertainty in geometrical positioning). Figs. 10 and 11 show a large number of detections but, unfortunately, Fig. 12 1More sophisticated techniques for performance testing (e.g., the so-called “leaving-one-out” [1]) are difficult to apply as many of the pings composing the actual testing set cannot be included in the training set due to the impossibility of manually recognizing the target-echo position
Fig. 10. Detection in Sea Trial B. The expected detection interval is shown in the highest row. Dark boxes show the signal segments for which the classifier gave H outputs. A confirmation rule should be applied (see text). (a) Aspect angle: 4 . (b) Aspect angle: 10 .
presents several false alarms. This is confirmed by an examination of the full set of available testing data which provide a 60% detection probability against a 9.5% false-alarm probability.2 These values are far from those of Sea Trial A due to the low quality of the signal used to compose class H of the training set. To reduce the number of false alarms without significantly affecting the probability of detection, a simple rule-of-thumb was applied: one can consider a detection valid only when it is obtained for at least two pings over the same time interval. This means that it is necessary that two or more pings should confirm a detection obtained over a given time interval, before one can take it into consideration. As the time length of each signal segment was 2 ms and the segments overlapped 50%, the time interval considered here is 1-ms long. It is clear that this rule is very general, applicable in any multi-ping detection 2The detection probability considered here is equivalent to Pr of Sea Trial A as it takes into account both detection at due time and detection at later time. The false-alarm probability refers both to detection when no target is present and to detection performed before the real target echo.
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Fig. 12. Detection in Sea Trial B related to pure reverberation signals. Dark boxes show the signal segments for which the classifier gave H outputs. A confirmation rule should be applied (see text).
VI. CONCLUSIONS
Fig. 11. Detection in Sea Trial B. The expected detection interval is shown in the highest row. Dark boxes show the signal segments for which the classifier gave H outputs. A confirmation rule should be applied (see text). (a) Aspect angle: 43 . (b) Aspect angle: 77 .
system, and not specially tailored to coping with the examined data set. This does not prevent a more sophisticated rule from providing better results. The application of the confirmation rule to the full testing set provided a 56% detection probability against a 3% false-alarm probability. Although the detection probability slightly decreased, this result represents an improvement as the false-alarm occurrences are fewer than half of those previously reported. Assuming the application of the confirmation rule, one can notice that Figs. 10(a) and (b), and 11(a) show detection probabilities exceeding 60%, whereas, in Fig. 11(b), the object presence is completely missed. At the 7 aspect angles available, this is the only case in which the target detection was not achieved. Fig. 12 shows two of the pings contributing to the false-alarm probability. Finally, one can observe that the number of available signals for the training set was small, i.e., about 10 times less than for Sea Trial A. The fact that results of fair quality were obtained under conditions so far from ideal ones suggests that a better training set (i.e., more populated and with clear target echoes) should provide results similar to those yielded by Sea Trial A.
In this paper, a method for the detection of buried objects has been presented that is based on pattern recognition theory, and that works on signals gathered by an active sonar system. The acoustic source is a parametric array, and echoes are collected by an array of sensors. After beamforming, signals are split into partially overlapped blocks, normalized, and projected by using the WVD and the bispectrum. From these projections, a vector of many features is extracted for each signal block, normalized, reduced, and fed into an MGC statistical classifier. Such a classifier has been trained to distinguish between just two classes: target absence and target presence. The proposed detection method has been extensively assessed on data acquired in two quite different sea trials made at subcritical and supercritical grazing angles and using deeply and partially buried cylinders at different aspect angles. Also sediments were of different type (i.e., fine compact sand, and soft mud). Despite the low SRRs, the quality of results was generally very high and slightly better than that of the results obtained by prewhitening and matched filtering. Moreover, unlike matched filtering, the proposed detector yielded fair performances even in the absence of target responses in free field. Future works will be devoted to investigate potential improvements coming from: 1) The replacement of the PCA with the multiple-discriminant analysis [1] (i.e., another classical approach for the reduction of the dimensionality of the problem that seeks a projection that best separates the data in a least-squares sense) and 2) The projection of the acquired signals by using a multi-resolution analysis like the wavelet transform [19]. APPENDIX A list of features making up the full vector is provided: they are divided according to the related projection spaces. Minor differences between the features for Sea Trial A and the features for Sea Trial B are described in features nos. 16), 17), 36), and
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37). Note that, in the following, the term “skewness” indicates the third central moment divided by the cube of the standard deviation, and the term “kurtosis” indicates the fourth central moment divided by the fourth power of the standard deviation. Features Extracted from the WVD 1) Standard deviation of the WVD projection on the time axis. 2) Standard deviation of the WVD projection on the frequency axis. 3) Standard deviation of the whole WVD. 4) Number of WVD values that exceed 40% of the absolute WVD maximum. 5) Standard deviation of the time bins of the values exceeding 40% of the absolute maximum. 6) Standard deviation of the frequency bins of the values exceeding 40% of the absolute maximum. 7) Mean of the frequency bins of the values exceeding 40% of the absolute maximum. 8) Maximum frequency bin interval of the values exceeding 40% of the absolute maximum. 9) Maximum time bin interval of the values exceeding 40% of the absolute maximum. 10) Standard deviation of the WVD integration over time at each frequency bin, considering only the values exceeding 40% of the absolute maximum. 11) Standard deviation of the WVD integration over frequency at each time instant, considering only the values exceeding 40% of the absolute maximum. 12) Sum of the distances (on the time-frequency plane) between the positions of the values exceeding 40% of the absolute maximum and the position of the absolute maximum itself. 13) Ratio between the maximum in [0 kHz, 10 kHz] and the maximum in [15 kHz, 25 kHz]. 14) Mean of the skewness values computed between 5 and 8 kHz for each time bin. 15) Mean of the skewness values computed between 8 and 12 kHz for each time bin. 16) Correlation between the current WVD and the WVD of the target echo at the broadside aspect angle (target floating in free water for Sea Trial A; target partially embedded for Sea Trial B). 17) Correlation coefficient between the current WVD and the WVD of the target echo at an intermediate aspect angle (target floating in free water for Sea Trial A; target partially embedded for Sea Trial B). Features Extracted from the Integration of the WVD over Time, i.e., from the Energy Spectrum 18) 19) 20) 21) 22) 23)
Spectrum value at 0 Hz. Spectrum integration in [0 kHz, 3 kHz]. Spectrum integration in [3 kHz, 6 kHz]. Spectrum integration in [6 kHz, 9 kHz]. Spectrum integration in [9 kHz, 12 kHz]. Spectrum integration in [12 kHz, 15 kHz].
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24) 25) 26) 27) 28)
Spectrum integration in [15 kHz, 50 kHz]. Spectrum standard deviation. Spectrum skewness. Spectrum kurtosis. Absolute difference between the spectrum at 0 Hz and its average in [6 kHz, 12 kHz]. 29) Absolute difference between the spectrum averages in [3 kHz, 6 kHz] and [6 kHz, 12 kHz]. Features Extracted from the Bispectrum (Its Absolute Value is Considered) 30) Standard deviation of the bispectrum. 31) Mean of the sample values inside a given circle centered in the origin. 32) Absolute maximum of the bispectrum. 33) Number of bispectrum samples exceeding 20% of the absolute maximum. 34) Number of samples that are contained inside a given circular ring and exceed 20% of the absolute maximum. 35) Ratio of the value of feature 34) to the value of feature 33). 36) Correlation coefficient between the current bispectrum and the bispectrum of the target echo at the broadside aspect angle in free field (target floating in free water for Sea Trial A; target partially embedded for Sea Trial B). 37) Correlation coefficient between the current bispectrum and the bispectrum of the target echo at an intermediate aspect angle (target floating in free water for Sea Trial A; target partially embedded for Sea Trial B). 38) Standard deviation of the bispectrum along the diagonal. 39) Index of the bispectrum maximum along the diagonal. 40) Mean of the indexes of the samples along the diagonal that exceed 20% of the absolute maximum. 41) Standard deviation of the indexes of the samples along the diagonal that exceed 20% of the absolute maximum. 42) Standard deviation of the indexes of the samples along the diagonal that exceed 90% of the absolute maximum. 43) Index of the first sample along the diagonal that exceeds 20% of the absolute maximum.
ACKNOWLEDGMENT The author wishes to thank Ing. A. Costa, Ing. F. Crespi, Ing. S. Di Serio, and Ing. M. Dolla for their assistance in implementing the algorithms and assessing their performances, and feels special thanks are due to the people of SACLANTCEN (in particular, Dr. A. Maguer, Dr. S. Fioravanti, and Dr. A. Tesei), who performed experiments and provided data related to Sea Trial A, and to the people of LU and TNO-FEL (in particular, Prof. B. Woodward, Dr. P. A. Lepper, Dr. A. M. G. H. Saanen, and Dr. J. C. Sabel), who performed experiments and provided data related to Sea Trial B. The author is also very grateful to Prof. G. Tacconi for stimulating discussions and helpful suggestions, and the anonymous reviewers, whose constructive comments have surely contributed to improving the quality and the readability of this paper.
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REFERENCES [1] R. O. Duda, P. E. Hart, and D.G. Stork, Pattern Classification, 2nd ed. New York: Wiley , 2001. [2] J. T. Tou and R. C. Gonzalez, Pattern Recognition Principles. Harlow, U. K.: Addison-Wesley, 1974. [3] D. Alexandrou and D. Pantzartzis, “A methodology for acoustic seafloor classification,” IEEE J. Ocean. Eng., vol. 18, pp. 81–86, Apr. 1993. [4] D. R. Carmichael, L. M. Linnet, S. J. Clarke, and B. R. Calder, “Seabed classification through multifractal analysis of sidescan sonar images,” Proc. Inst. Electron. Eng. Radar, Sonar, and Navigation, vol. 143, pp. 140–148, June 1996. [5] F. B. Shin and D. H. Kil, “Robust mine detection and classification with target physics-derived features and classifiers,” in Proc. IEEE Int. Conf. Oceans ’96, Fort Lauderdale, FL, Sept. 1996, pp. 1204–1207. [6] P. M. Baggenstoss, “Improved echo classification in shallow water,” in Proc. IEEE Int. Conf. Oceans ’96, Fort Lauderdale, FL, Sept. 1996, pp. 1197–1203. [7] F. B. Shin and D. H. Kil, “Full spectrum signal processing using a classify-before-detect paradigm,” J. Acoust. Soc. Amer., vol. 98, pp. 2188–2197, Apr. 1996. [8] A. Kundu, G. C. Chen, and C. E. Person, “Transient sonar signal classification using hidden Markov models and neural nets,” IEEE J. Ocean. Eng., vol. 19, pp. 87–98, Jan. 1994. [9] M. P. Santana and M. J. Rendas, “Acoustic fish classification,” in Proc. 3rd Eur. Conf. Underwater Acoustics, Heraklion, Greece, June 1996, pp. 711–716. [10] R. Bozzano and A. Siccardi, “A high frequency approach for seabed vegetation characterization,” in Proc. Int. Conf. High Frequency Acoustics in Shallow Water, Lerici, Italy, July 1997, pp. 57–64. [11] H. L. Roitblat, W. W. L. Au, P. E. Nachtigall, R. Shizumura, and G. Moons, “Sonar recognition of targets embedded in sediment,” Neural Netw., vol. 8, no. 7/8, pp. 1263–1273, 1995. [12] D. Boulinguez and A. Quinquis, “A new way of identifying buried objects,” IEEE Ocean. Eng. Soc. Newslett., vol. XXIV, pp. 16–20, 1999. , “Entropy and fractal analysis for underwater object classifica[13] tion,” in Proc. 5th Euro. Conf. Underwater Acoustics, Lyon, France, July 2000, pp. 1103–1108. [14] D. Boulinguez, A. Quinquis, and M. Brussieux, “Classification of buried objects using a parametric sonar,” in Proc.IEEE Int. Conf. Oceans‘98, Nice, France, Sept. 1998, pp. 1264–1268. [15] R. C. Evans and T. G. Leighton, “The detection of cylindrical objects of low acoustic contrast buried in the seabed,” in Proc. 16th Int. Congr. Acoustics , Seattle, WA, June 1998, pp. 1369–1370. [16] D. N. Lambert, D. J. Walter, D. C. Young, S. R. Griffin, and K. C. Benjamin, “Developments in acoustic sediment classification,” in Proc. Int. Conf. Oceans’98 , Nice, France, Sept. 1998, pp. 26–31. [17] P. Morén and J. Pihl, “Sub-bottom characterization using a parametric sonar,” in Proc. Int. Conf. Oceans’98, Nice, France, Sept. 1998, pp. 1828–1832. [18] A. Maguer, W. L. J. Fox, B. Zerr, A. Tesei, E. Bovio, and S. Fioravanti, “Buried mine detection and classification,” in Proc. Underwater Defence Technology Conf., Nice, France, June 1999, pp. 27–30. [19] M. Tran Van Nhieu, M. Gensane, S. Fioravanti, A. Tesei, A. Maguer, B. Woodward, and P. A. Lepper, “Detection of a buried water-filled cylindrical shell by the wavelet transform technique,” in Proc. 5th Eur. Conf. Underwater Acoustics, Lyon, France, July 2000, pp. 1091–1096. [20] A. Trucco, “Experimental results on the detection of embedded objects by prewhitening filtering,” IEEE J. Oceanic Eng., vol. 26, pp. 783–794, Oct. 2001. [21] F. B. Shin, D. H. Kil, and R. F. Wayland, “Active impulsive echo discrimination in shallow water by mapping target physics-derived features to classifiers,” IEEE J. Ocean. Eng., vol. 22, pp. 66–79, Jan. 1997. [22] S. Qian and D. Chen, “Joint time-frequency analysis,” IEEE Signal Processing Mag., vol. 16, pp. 52–67, Mar. 1999. [23] L. Cohen, “Time-frequency distributions—A review,” Proc. IEEE , vol. 77, pp. 941–981, July 1989. [24] R. O. Nielsen, Sonar Signal Processing. Boston, MA: Artech House, 1991. [25] N. Yen, “Time and frequency representation of acoustic signals by means of the Wigner distribution function: Implementation and interpretation,” J. Acoust. Soc. Amer., vol. 81, pp. 1841–1850, 1987. [26] C. L. Nikias and M. R. Raghuveer, “Bispectrum estimation: A digital signal processing framework,” Proc. IEEE, vol. 75, pp. 869–891, July 1987.
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Andrea Trucco (S’93–A’99–M’00) was born in Geneva, Italy, in 1970. He received the Laurea (M.Sc.) degree in electronic engineering, and the Ph.D. degree in electronic engineering and computer science, from the University of Genoa, Geneva, Italy, in June 1994, and June 1998, respectively. In 1999, he was appointed Assistant Professor at the Department of Biophysics and Electronic Engineering (DIBE), University of Genoa, where he teaches signal theory and electrical communications, and the optical communications course, is responsible for research activities in acoustics, antenna arrays, and underwater signals of the Signal Processing and Telecommunications Group at DIBE. His main research interests are array synthesis, coherent and noncoherent algorithms for acoustic imaging, acoustic image improvement and 3-D reconstruction, interferometric sonar and radar simulation methodologies. He is also involved in the scientific activities related to several research projects funded from the MAST (Marine Science and Technology) program of the European Commission, the Italian Space Agency, and some industrial companies. Dr. Trucco is an Associate Editor of the IEEE JOURNAL OF OCEANIC ENGINEERING, a referee for important international journals, serves on the Scientific Committee of several international conferences, and has been a Guest Editor of a special issue of the Computer Vision and Pattern Recognition journal. From 1987 to 1990, Dr. Trucco was three times a finalist for the Philips Award for European Young Scientists and ranked third twice. In 1995, he won the Student Paper Competition organized by the Ninth International Symposium on Unmanned Untethered Submersible Technology. In 1997, he won the Student Paper Competition organized by the MTS/IEEE Oceans’97 International Conference. He is a member IAPR.
