Ship Target Recognition Using Low Resolution Radar

I. INTRODUCTION

Ship Target Recognition Using Low Resolution Radar and Neural Networks M. R. INGGS A. D. ROBINSON University of Cape Town

The classification of ship targets using low resolution down-range radar profiles together with preprocessing and neural networks is investigated. An implementation of the Fourier-modified discrete Mellin transform is used as a means for extracting features which are insensitive to the aspect angle of the radar. Kohonen’s self-organizing map with learning vector quantization (LVQ) is used for the classification of these feature vectors. The use of a feed-forward network trained with the back-propagation algorithm is also investigated. The classification system is applied to both simulated and real data sets. Classification accuracies of up to 90% are reported for the real data, provided target aspect angle information is available to within an error not exceeding 30 deg.

Manuscript received August 15, 1994; revised August 22, 1996. IEEE Log No. T-AES/35/2/04294. Authors’ address: Radar Remote Sensing Group, Dept. of Electrical Engineering, University of Cape Town, Private Bag, Rondebosch, 7700, South Africa, E-mail: ([email protected]).

c 1999 IEEE 0018-9251/99/$10.00 ° 386

In a maritime environment, there are numerous uses for the identification of individual vessels. These include; coast guard control, sea rescue, the regulation of shipping channels, and naval warfare. There are a variety of approaches for characterizing naval vessels using radar. A very simple and rapid way is through the use of radar range profiles which are essentially one-dimensional radar images. With this approach, it is possible to segment the data to correspond to individual scattering centers along the down-range line of sight of the target. This works well if the radar resolution is such that the various reflecting features on the target are point-like or dilute. Clearly the aspect angle of the target and the presence of clutter and/or other radar interferences make the recognition problem more difficult. This work investigates the use of preprocessed radar range profiles as target signatures for identification purposes, and the classification of these signatures using Kohonen’s learning vector quantization (LVQ) neural network. The work was inspired by a paper by Bufa [4]. Bufa used the Fourier Mellin (FM) transform for preprocessing, and then converted the output to an equivalent binary word. Some fairly standard techniques of binary word recognition were then carried out to implement the recognition. Some preliminary work to this project [16, 17] investigated Bufa’s method and some adaptions. However these are not reported here since the use of neural networks produced far more robust results. Although many neural network algorithms are able to operate directly on the raw data by developing their own complex mapping functions (e.g., back-propagation), we decided instead to retain the use of the FM transform. Neural-based classifiers have, in general, been shown to produce superior results when used in conjunction with known transforms, than when left to develop their own complex mapping functions [9, 10]. This was confirmed decisively in some initial tests performed on simulated data. It was felt that this approach would not compromise the many advantages offered by a completely neural architecture (such as speed), since the FM transform is in fact well suited to a parallel implementation. The radar system used to capture the data is described and some typical data (Section II) are shown. This is followed by two sections which discuss the nature of the classification problem (Section III) and the usefulness of preprocessing (Section IV). Section V shows some of the problems encountered in the real data, i.e., clutter, interference, and target scintillation. A means of avoiding bad data is described. The classification of real data is addressed

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classic matched filter. The net result is that the higher sampling rate is justified and probably results in some of the finer detail of the target structure being recorded. III. BRIEF ANALYSIS OF THE CLASSIFICATION PROBLEM Fig. 1. Relation between radar range profile and target.

Fig. 2. 3-dimensional plot of typical scan from cargo ship.

in Section VI with success rates reported for five classes of ship target. The results are discussed in Section VII and some conclusions drawn in Section VIII. II.

BRIEF DESCRIPTION OF THE RADAR SYSTEM

The pulsed, noncoherent radar is typical of the systems currently in use worldwide for short-range ship navigation. In the mode employed for the data capture, each pulse (9.3 GHz) has a length of 80 ns, giving a relatively low range resolution of 12 m. The pulse repetition frequency (PRF) is 3600 Hz and the scan rate is 2.2 s. Sampling is in the time domain at 25 MHz, using eight bits. The digitization system is able to sample any range-azimuth domain set by the user. The IF and detector system is very close to linear in terms of power. Fig. 1 shows a typical downrange profile of a ship target. The interpulse interval has been made shorter than in practice to give an idea of the formation of successive profiles. A typical example of one azimuth versus range scan is given in Fig. 2. The characteristic bell shape in azimuth is due to the antenna gain modulation. For the radar system used, the antenna beamwidth was less than a degree. It is important to note that at a given time, the pulse illuminates only a single strip of length equal to the pulse resolution of the radar (12 m in this case). The digitized sample represents the instantaneous vector sum of the the scatterers currently being illuminated. In the system used, range samples are taken at 40 ns intervals, about two per resolution cell of the radar pulse. The radar bandwidth is not carefully defined in this kind of radar, but is probably wider than the reciprocal of the pulsewidth, the

The principal factor that distinguishes the ship classification problem from other classification problems (such as aircraft recognition), is that noncooperative determination of the target orientation with respect to the radar is particularly difficult. This is because slow-moving ships can execute rapid course alterations. Even worse, when the target is stationary, this task becomes impossible. It is therefore essential that the classification system not rely on accurate target aspect angle information. A ship generally has a large length to width ratio, and can be considered to be a linear array of reflectors. In order to quantify how the range profile will change with aspect angle, we note the following. 1) If the aspect of the target changes, the relative ranges of points will change. The result will be range migration, i.e., the two points which were once in the same range bin are no longer. If the points are located along one axis in a straight line, (such as on ship targets), then this range migration corresponds to a linear scaling of the independent variable of the range profile, i.e., the line of sight range profile becomes compressed or stretched. The profile has greatest dimension when the radar signal and the length axis of the ship are aligned (zero aspect angle). 2) Even if the aspect does not change enough to cause migration, changes in the relative range of the different scatterers of a fraction of the radar wavelength can cause the radar profile to fluctuate due to interference effects. The wavelength of the radar is 3 cm. This means that a change in the down-range spatial distribution of any two scattering centres in a range bin by just 0.75 cm can result in a change from complete destructive interference to complete constructive interference. This is the well-known phenomenon of speckle. 3) Magnitude fluctuations also arise due to the fact that the scattering features are generally anisotropic in nature. This applies especially to large, flat reflectors, which only reflect strongly over a narrow range of aspect angle. Also, over large aspect ranges, one can also expect certain features to become occluded, and new ones to emerge. IV. PREPROCESSING: THE FOURIER-MELLIN TRANSFORM As discussed in the previous section, a change in the target aspect angle causes the range profile to

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Fig. 3. Spread of F-MDMTs of simulated tanker profiles (aspect angle from 0—67.5 deg, SNR = 10 dB).

fluctuate according to three basic principles. Only the first (a scaling of the range profile), can be dealt with in an intelligent manner. This is achieved through the use of the Mellin transform, which reduces any linear scaling of the input function to a phase component. It is therefore possible to simply discard this scale information by taking the magnitude of the Mellin transform. In addition, it is also necessary to discard any time-shift information. This is usually achieved through the use of the Fourier transform. This approach (with some modifications), was developed in detail in a paper by Zwicke and Kiss [15] (in which it was called the Fourier-modified discrete Mellin transform (F-MDMT)) and is therefore not repeated here. One addition of our own is worth mentioning. It concerns the choice of “spectral components,” which happens to be completely arbitrary. For the classification problem studied here, it was found that components all the way up to 6.25 ¼ continued to provide good discrimination between classes, while still exhibiting small in-class variations. This effectively increased the Hamming distance between classes, thereby reducing overlap at the decision surfaces separating each class. An example of the F-MDMT in action on simulated data is illustrated in Figs. 3 and 4. Despite the within-class variation, a comparison of the first 40 spectral components of Figs. 3 and 4 reveals several components that provide good discrimination, notably component 5, and components 25 to 35. The ships were modeled by assuming point reflectors spaced by dimensions corresponding to the major superstructural elements of the ship type. It was found that these simple models produced realistic looking data (compared with real measurements of similar ships). V.

DESCRIPTION OF THE DATA

The signal-to-noise ratio (SNR) of the range profiles was generally high (+15 dB), and radar 388

Fig. 4. Spread of F-MDMTs of simulated tug profiles (aspect angle from 0—67.5 deg, SNR = 10 dB).

Fig. 5. Poor quality scan. Corrupted with sea clutter and radar interference.

interference and sea clutter were identified as the major degrading factors as far as scan quality is concerned. Typically this was limited to one or two distinct azimuth bins (illustrated in Fig. 5). The probability density function for the cross section is typically Rayleigh distributed. Magnitude fluctuations in the radar profiles were found to correspond closely to Swerling’s first model for calculating detection probabilities [12], i.e., the echo pulses received from a target on any one scan are of constant amplitude throughout the entire scan but are independent (uncorrelated) from scan to scan. This assumption of course ignores the effect of the antenna beam shape on the echo amplitude. For an example of the scan-to-scan magnitude fluctuations, Fig. 6 shows several scans for a cargo ship, which was supposedly stationary over the measurement period. Consecutively numbered scans are separated in time by 2.2 s. Significant fluctuations caused by small changes in the orientation of the target over this time period can be clearly observed. This low correlation between scans is most likely the major limiting factor as far as classification accuracy is concerned. The only way in which these magnitude fluctuations can be reduced is by improving the resolution of the radar. The number of profiles within a scan actually containing a target echo depends on the distance,

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VI.

Fig. 6. Interference between scattering centers for cargo (tanker) ship. Consecutively numbered scans separated by 2.2 s. One range profile selected from each scan.

size, and orientation of the target. In theory, only one profile is required since the individual pulses are well correlated. Ideally, the profile with the highest SNR should be selected. This will occur when the target is completely enveloped by the radar beam. A simple approach would be to select the profile with the highest mean value. However this method has a tendency to select a profile containing radar interference. The approach used here is to perfrom a simple iterative test, based on the fact that each uncluttered bin is almost idential to its neighbor (besides a slight increase/decrease in energy content). The test is as follows. 1) Select the bin with the highest energy content. 2) Compare this profile with its immediate neighbor by performing a simple subtraction operation. 3) If they are not identical (within an arbitrarily defined tolerance), move two bins along and try again. A proposed enhancement to the above approach involves the use of compound identification [5] whereby several profiles are fed into the classifier, and the average of the identifications is used to arrive at a single “best” identification. The identification of a single profile can be considered a “vote” for one of the targets, and the compound identification then corresponds to choosing the target with the most votes. For the preliminary development described here, compound identification was not implemented.

CLASSIFICATION

Kohonen’s self-organizing map (SOM) with LVQ [7] was selected for the classification task. In the initial development phase, simulated data were used to compare the map’s performance to that of the feed-forward network (also known as the multilayer perceptron (MLP)) with back-propagation. These were utilized due to their large popularity in the literature and easy access to information and software. The selection of SOM as the means of classification was motivated more from the fact that Kohonen had just released his software packages and the authors were keen to evaluate them. The work reported here shows that they performed well, but there is definitely scope for more careful investigation as to the best means of achieving classification. Although the feed-forward network is capable of forming more nonlinear mapping functions, a complex decision surface in the decision space was found to be unnessessary. The feature vectors for each class did not differ markedly from the multivariate Gaussian assumption made in the design of the Bayes classifier, indicating that the FM transform performs the feature extraction task quite effectively. Since the decision surface formed by the SOM tends towards a very close, although piecewise linear, approximation to the theoretically optimal Bayes decision surface [7], Kohonen’s classifier was well suited to the classification problem. In addition, it offered the advantage of significantly reduced training times (50—100 times faster). All three LVQ algorithms were used and tested. The old LVQ2 algorithm was based on the idea of differentially shifting the decision borders towards the Bayes limits, while no attention was paid to what might happen to the location of the reference vectors in the long run if this process were continued. There are at least two detrimental problems with this approach [7]. The LVQ3 was developed in an attempt to deal with these problems, and for the work described in this work, an average improvement in classification accuracy of 2% was observed compared with LVQ2. Five targets were selected. They were named Targets 1, 2, 3, 4, and 5. Separate training and testing data sets were selected in order to test as rigorously as possible the ability of the classifier to generalize. Where possible, training and testing was performed at different aspect angles. In all cases separate scans were used. This was considered particularly important. Because of the poor scan-to-scan correlation discussed above, presenting the classifier with unseen scans was considered a harsher test than presenting it with unknown target azimuth angles.

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TABLE I Results for Four Targets Over Aspect Angle Range of 70 Deg Target

Entries

Accuracy

1 2 3 4

44 45 18 44

100.00% 77.78% 100.00% 95.45%

Average

TABLE II Results for Four Targets Over Aspect Angle Range of 70 to 120 Deg

92.05%

Target

Entries

Accuracy

1 2 4 5

110 66 55 22

86.36% 81.82% 98.18% 81.82%

Average

87.35%

A. Classification. Part 1 Generally, the optimal approach was first to apply the optimized-learning rate LVQ1 algorithm (Appendix A) for fast initial convergence, and then to fine tune the network by applying the LVQ3 algorithm, with a progressively decreasing window size. Usually one or two iterations of the “balance” algorithm (Appendix B) were applied before training in order to achieve a better initial approximation of the class borders. In order to prevent “overlearning,” i.e., the codebook vectors become too specifically tuned to the training data so that the ability of the network to generalize for new data suffers, the learning process was stopped after some optimal number of steps. This varied according to the input data and was determined by trial and error. Previous research on the Mellin transform indicated that classification would only be feasible up to §30 deg of broadside [15]. Beyond this point noise due to the cross-ship component and to the occlusion of various reflecting structures becomes excessive. Table I summarizes the classification results for four of the five targets over azimuth variations within this range. The fifth target was omitted because of insufficient data over the specified range. The training history of the classifier is given in Appendix C. B. Classification. Part 2 Although previous research suggested that classification would not be possible for angles close to broadside [15], an analysis of the target scans revealed that discrimination might be feasible, especially for the larger targets whose width dimensions are several times that of the resolution of the radar. At broadside aspects, the profiles can be expected to display a different set of features due to the different distribution of scattering centers. Accordingly, it is necessary to consider a given vessel over this new range of aspect angles as a distinct target. For example, Target 1 over aspects 0 · µ · 70 and 300 · µ · 360 is considered Target 1a, and the same vessel over aspects 70 · µ · 120 and 240 · µ · 300 is considered Target 1b. Table II summarizes the classification results for four of the five targets over azimuth variations within the stipulated range. The 390

third target was omitted because of insufficient data over this azimuth range. The training history of the classifier is given in Appendix D. Classification over the azimuth range 120 · µ · 240 was not attempted because of insufficient data. However there is no reason why the results should differ from those obtained in Section VIA. Once again each vessel should be considered a distinct target over this aspect range. VII.

DISCUSSION

Because the data sets were limited, the results obtained above should only be considered a rough approximation to the sort of accuracies one can expect from a fully operational radar target classifier. It is likely that a larger data set consisting of more targets will yield poorer classification accuracies because the low resolution of the radar is not conducive to good target discrimination. This applies especially to vessels of a similar size. Increasing the resolution of the radar is a way which might improve classification accuracies. An approach currently under investigation at the University of Cape Town [18, 19] involves the use of synthetic range profiles (stepping the radar over several frequencies to enhance resolution). However, there is a danger that the increasing amount of data might just increase the chances of confusion, since there will be less smoothing of the speckle phenomenon. The small wavelength and long pulse length of the radar system leads to poor scan-to-scan correlation. To combat this speckle effect, some sort of averaging operation is required [12]. A good idea would be to apply the compound identification method described earlier (this time on a scan-to-scan basis). Hudson and Psaltis have recently applied this method to the problem of aircraft speckle [5]. They found that the accuracy of their classifier improved from 84% to 100%. Although classification time would be limited by the scan rate, this is not a serious drawback for ship target recognition because the low velocities of the targets does not make instant classification a high priority. It was not possible to investigate this

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approach here, because it would have required a data set much larger than the one currently available. The data set will be enlarged in the near future to test this approach. The neural methods used here are not necessarily optimal. Approaches currently under investigation include the following. 1) Investigating the ability of neural networks to facilitate automatic knowledge acquisition (unsupervised learning) and continuous system refinement, with particular attention to the Fuzzy Min-Max neural network [11]. 2) Developing a neural-based implementation of the F-MDMT [10]. VIII.

CONCLUSIONS

Ship target recognition over a wide range of aspect angles has been demonstrated. The success is due to a combination of robust feature extraction and the soft threshold nature of neural networks. These results have been achieved with a low resolution, noncoherent navigation radar. Some further work into the neural classifiers used is discussed above. In addition, a much larger data base of different ship types needs to be assembled and the identification system tested for potential within class and across class confusions. The modest requirements in terms of computer and radar hardware of this system show great potential for providing a recognition system to a variety of users such as coast guards, search and rescue organizations, and harbor masters. APPENDIX A. OPTIMIZED LVQ1 ALGORITHM Let the the number of codebook vectors placed into the input space be given by mi (free parameter vectors). Let x(t) be a sample of input and let mi (t) represent sequences of the mi in the discrete-time domain. Let c = argmin[length(x ¡ mi )]

(2)

(3)

if the classification of x is incorrect, mi (t + 1) = mi (t)

for i 6= c

®c (t ¡ 1) : 1 + s(t)®c (t ¡ 1)

APPENDIX B. THE BALANCE ALGORITHM The balance algorithm is an attempt to get the average distances between the adjacent codebook vectors (which depend on their numbers per class) the same on both sides of the class borders. Since the class borders are represented piecewise linearly by segments of mid planes between codebook vectors of neighboring classes, then, at least if the class distributions were symmetric, this would mean that the average shortest distances of the codebook vectors should be the same in every class. In the algorithm balance, the medians of the shortest distances between the initial codebook vectors of each class are first computed. If the distances turn out to be very different for the different classes, new codebook vectors may be added to, or old ones deleted from the deviating classes, and a tentative training cycle based on the optimized-learning rate LVQ1 algorithm is run once. The optimal number of iterations actually used must be determined by trial and error. APPENDIX C. CLASSIFIER TRAINING HISTORY FOR PART 1 Codebook Vectors = 8 Initializing Options (Balance) = 1 OLVQ1 Training Cycles = 2440 LVQ3 Training Cycles = 200 (1st run) Initial Alpha = 0.03 Window Width = 0.3 Epsilon = 0.1

(1)

if x is classified correctly, mc (t + 1) = mc (t) ¡ ®c (t)[x(t) ¡ mc (t)]

®c (t) =

LVQ3 Training Cycles = 200 (2nd run) Initial Alpha = 0.02 Window Width = 0.2 Epsilon = 0.1

define the nearest mi to x, denoted by mc . To obtain the optimized LVQ1 algorithm, the basic LVQ1 algorithm is modified in such a way that an individual learning rate ®i (t) is assigned to each mi . We then get the following discrete-time learning process, mc (t + 1) = mc (t) + ®c (t)[x(t) ¡ mc (t)]

where

(4)

APPENDIX D. CLASSIFIER TRAINING HISTORY FOR PART 2 Codebook Vectors = 4 Initializing Options (Balance) = 1 OLVQ1 Training Cycles = 2000 LVQ3 Training Cycles = 500 (1st run) Initial Alpha = 0.03 Window Width = 0.3 Epsilon = 0.1 LVQ3 Training Cycles = 600 (2nd run) Initial Alpha = 0.03 Window Width = 0.1 Epsilon = 0.1

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LVQ3 Training Cycles = 200 (3rd run) Initial Alpha = 0.01 Window Width = 0.05 Epsilon = 0.1

[8]

[9]

ACKNOWLEDGMENTS The authors are indebted to a number of people: Messrs. Piet Botha, Simon Norval, and Johan Theron of IMT, Simon’s Town, for providing the data from their facility; Mr. Roy Blatch (then in the South African Navy) integrated the equipment and provided the early data sets; Tanya Douglas, Anton Krantz, and Richard Remmington all contributed to various phases of the investigations reported here.

[10]

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Bhanu, B. (1986) Automatic target recognition: State of the art survey. IEEE Transactions on Aerospace and Electronic Systems, AES-22, 4 (July 1986), 364—378. Deng, C., and Haykin, S. (1993) Classification of radar clutter using neural networks. IEEE Transactions on Neural Networks, 2, 6 (Nov. 1993), 589—600. Farhat, N. H. (1989) Microwave diversity imaging and automated target identification based on models of neural networks. IEEE Proceedings, 77, 5 (May 1989), 670—681. Guirong, G., Wei, Z., and Wenxian, Y. (1989) An intelligence recognition method of ship targets. Institute of Electrical and Electronic Engineers, 3 (1989), 1088—1096. Hudsen, S., and Psaltis, D. (1993) Correlation filters for aircraft identification from radar range profiles. IEEE Transactions on Aerospace and Electronic Systems, 29, 3 (July 1993), 741—748. Jouny, I., Garber, E. D., and Ahalt, S. C. (1993) Classification of radar targets using synthetic neural networks. IEEE Transactions on Aerospace and Electronic Systems, 29, 2 (Apr. 1993), 336—343. Kohonen, T. (1990) The self-organizing map. IEEE Proceedings, 78, 9 (Sept. 1990), 1464—1478.

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Kwan, H. K., and Lee, C. K. (1993) A neural network approach to pulsed radar detection. IEEE Transactions on Aerospace and Electronic Systems, 29, 1 (Jan. 1993), 9—21. Roth, M. W. (1990) Neural network technology for automatic target recognition. In Proceedings of the SPIE, 1294 (Apr. 18—20, 1990), 65—76. Kabrisky, A., Rogers, S., Ruck, D., and Tarr, G. (1990) Artificial neural networks for automatic target recognition. Proceedings of the SPIE, 1294 (Apr. 18—20, 1990), 2—12. Simpson, P. K. (1992) Fuzzy min-max neural networks–Part 1: Classification. IEEE Transactions on Neural Networks, 3, 5 (Sept. 1992), 776—787. Skolnik, M. I. (1962) Introduction to Radar Systems. New York: McGraw-Hill, 1962. Sneddon, I. (1972) The Uses of Integral Transforms. New York: McGraw-Hill, 1972. Wilson, G. B. (1990) Radar classification using a neural network. Proceedings of the SPIE, 1294 (Apr. 18—20, 1990), 200—210. Zwicke, P. E., and Kiss, I., Jr. (1983) A new implementation of the Mellin transform and its application to radar classifiaction of ships. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMA-5, 2 (Mar. 1983), 192—199. Douglas, T. S. (1992) Ship target recognition system. University of Cape Town Radar Remote Sensing Group, Nov. 1992. Krantz, A. (1993) Target recognition system. University of Cape Town Radar Remote Sensing Group, June 1993. Inggs, M. R., Hurwitz, J., and Langman, A. (1993) Synthetic range profile measurements of aircraft. In Proceedings of the South African Communications and Signal Processing Conference (COMSIG-93), 1993, 204—209. Inggs, M. R., van Zyl, M. W., and Knight, A. (1993) A simulation of synthetic range profile radar. In Proceedings of the South African Communications and Signal Processing Conference (COMSIG-93), 1993, 1—6.

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Michael Inggs graduated in 1972 with a B.Sc. (Hons) in physics and applied mathematics from Rhodes University, South Africa, and in 1979 with a Ph.D. from London University. He has been an Associate Professor in the Department of Electrical Engineering, University of Cape Town since 1988. Before that he was with the SA Naval Dockyard (Simon’s Town), ESD (Halfway House), Raytheon Data Systems (Norwood, MA), RF Technology Centre (Leatherhead, UK), Imperial College (London) and Decca Radar Research Laboratories (Hersham, UK). His work has concentrated on microwave components and signal processing applied to radar and communications. Currently he supervises a group of postgraduate students working in the fields of imaging radar, radar target recognition and ground penetrating radar.

Anthony Robinson received the B.Sc. degree in electrical engineering at the University of Cape Town, South Africa, in 1993. He is currently working towards an M.Sc. degree at the same university. His field of research is radar target recognition, with particular emphasis on neural and fuzzy-based solutions. INGGS & ROBINSON: SHIP TARGET RECOGNITION USING LOW RESOLUTION RADAR AND NEURAL NETWORKS

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