G. William Slade. Orban Microwave Products. Wijgmaal, Belgium. Abstractâ We have developed an ultra-wideband radar-based sensor system that, by using ...
Rapid object discrimination sensor using ultra-wideband short range bistatic FMCW radar G. William Slade Orban Microwave Products Wijgmaal, Belgium Abstract— We have developed an ultra-wideband radar-based sensor system that, by using machine-learning techniques to recognize subtle differences in material properties, can signal the presence of unwanted contaminating objects in rapidly moving, randomly scattering material streams in industrial processing situations where simple amplitude/phase anomaly detection is not sufficient as a basis for detection. Fortunately, we can exploit subtle differences in material properties to separate the unwanted objects from the main material stream using an ultra-wideband frequency-modulated radar to probe for late-time resonances of the unwanted objects. The raw baseband radar-return data must be processed rapidly, since decision times are on the order of 10-100ms. This rapid decision time and the continuously changing clutter background presents challenges because we cannot use traditional background clutter subtraction methods nor can we use classical long-term averaging constant false-alarm rate and thresholding filters. Instead, we couple a signal classifier to a hidden Markov model to provide a maximum a-posteriori probability that an unwanted object was encountered in a certain time-frame. Here, we briefly describe the hardware we developed as well as some of the algorithms we tested. We then present the results of some preliminary laboratory tests of real materials under realistic conditions.
I. I NTRODUCTION A. The problem The purpose of this work is to develop a sensor that reliably detects the presence of foreign objects within a reasonably uniform (or uniformly random) stream of background material. A prime end-use example is the detection of rocks or metallic objects in a stream of dry crop material entering a harvesting machine or, perhaps, a stream or bale processing system, in order to avoid damaging processing systems or presenting safety hazards. Since damaging objects can closely resemble the dimensions and color of the background material, as well as possibly being buried within the material mat, visual discrimination methods are not likely to be very reliable. Acoustic detectors [1] also suffer from unreliability if the foreign objects are hidden within the stream of processed material. Moreover, acoustic systems rely on the use of mechanical shakers that consume significant power, thereby increasing processing costs. We employ low-power microwave beams to perform a simple form of tomographic probing of the material stream to “sniff out” possible foreign objects[2]. These microwave signals are straightforward to generate and pose no risk to
personnel (as opposed to X-ray probing, for example). The transmitter and receiver are arranged such that the wave power passes through the material to be probed (see Fig. 1).
Material to be probed
Transmit antenna
Detection system
Wave transmitted thru material and belt
Transport belt
Receive antenna
Fig. 1.
Illustration of the sensor transmit and receive antennas.
If the clean processed material is fairly uniform or of low density and is dimensionally much smaller and than the expected foreign objects, then a na¨ıve constant-carrier technique where amplitude and phase provide evidence of shadowing and clear Doppler signatures (as long as dielectric contrast is sufficient). Briefly stated, this is a case where the baseline clutter from the background material is low so the embedded foreign objects are straightforwardly visible by registering anomalies that deviate from a defined background. The foreign objects become more challenging to detect when the background material is dimensionally similar to the embedded foreign objects, such as in the case of corn ears (the background material) and rocks (the foreign objects, Figure 2). Anomalous returns of phase and amplitude are simply not visible in the background clutter. In order to discriminate between the foreign object and the lumpy background material, we exploit the differences in latetime response of the materials to ultrawide-band signal excitation [3]. As with the Doppler and shadowing methods, we still rely on dielectric contrast effects, but we add information on the internal resonances and losses of the probed materials, as shown in Fig. 3. In the case of corn ears and rocks, the rocks are dense and low loss whereas the ears of corn contain significant moisture, thereby possessing significant loss (and hence very weak on
Fig. 2. Situation where foreign object detection is difficult using simple shadowing and Doppler methods: maize and rocks.
Fig. 4. Illustration of the differences between the late-time signatures of no obstruction (blue) and a piece of granite-like rock similar to that seen in Fig. 2
Fig. 3. Different objects in the material stream will exhibit different resonance behavior, affecting the received signal spectrum.
no resonances). The nearly lossless rocks, depending on their shapes and materials, will resonate appreciably as the incident wave experiences some internal reflections within the rock boundaries. Figure 4 highlights the typical difference in the late-time response for a nearly lossless chunk of granite and the “no obstruction” situation. The rock exhibits a clear ringing effect as a result of internal reflections, which lasts well beyond the initial, incident pulse excitation. Unlike aircraft or other vehicles, which have well defined shapes, the foreign objects we are trying to detect have unpredictable shapes and variable material parameters (e.g. dielectric permittivity, conductivity). We cannot rely on signature templates for discriminating the targets from the clutter. We handle the inherent randomness of the targets we wish to resolve by assuming the observed return signals can be clustered into two classes: 1) returns exhibiting resonances/shadowing and 2) returns that do not. We then develop a simple hidden Markov model that yields the probability of finding the foreign object or not, once having made the observation of the radar return signal. II. D ESCRIPTION
OF HARDWARE
The sensor system hardware is based on a 4-8GHz frequency modulated, continuous wave bistatic radar. The fre-
quency range was chosen such that losses in moist material would not be too onerous for satisfactory detection using signal levels around 0dBm. The sweep range permits feature resolution of approximately 37mm (approx. 1.5in). The microwave transceiver uses a wideband VCO (Hittite HMC586LC4B), which relies on a wideband PLL to track a swept reference clock (for generating the frequency modulation). The block diagram is seen in Fig. 5.
Fig. 5.
The 4 to 8 GHz transceiver with wideband synthesizer.
A digitally synthesized linear ramp is provided by a flexible direct digital synthesis system. This provided us with a platform for generating different ramp speeds, frequency sweeps and ramp types. The receiver output is an audio frequency signal (0-20kHz) that is sampled at 40000 samples/second and pre-processed in a DSP system that applies some filtering and
transfers the resulting data to a PC for further processing and transferred to a graphical user interface and stored for further off-line processing. III. D ETECTION ALGORITHM Extracting the foreign object signatures from the high clutter background requires extracting the appropriate information from the radar return signals. For this, we employ some techniques commonly found in the field of artificial intelligence: Classifiers and Markov models [4]. A schematic outline of the detection algorithm is shown in Fig. 6 Raw
spectral
FFT
signal
Classifier
We use a hidden Markov model [4] whose hidden states are “late-time resonance found” and “late time resonance not found” given that foreign objects are present or not present. Each model is a two-state Markov chain (Figure 7) whose transition probabilities are found using the Baum-Welch [4] or some other maximum-likelihood estimation algorithm based on training data.
Fig. 7. The two-state Markov model for detection probability of late-time resonance.
terms
Transition model 1 Decision
Yes/no
Transition model 2
Fig. 6.
C. The state-tracking model
The major components of the detection algorithm.
A. The FFT The FFT is a straightforward implementation of a 2562048 bin transform implemented in a DSP, which decomposes the radar returns into its spectral components. Most of the information we need for detection is contained in the spectral component magnitude. The phase is ignored, since it tends to be uniformly distributed and yields little useful information about the presence of foreign objects. B. The classifier To test the proof of concept, the Support Vector Machine (SVM) [5], [4] provided a straightforward method of separating the clusters associated with late-time resonances and those without. The method yields results similar to those given by Linear Discriminant Analysis [6] and there are a number of very efficient algorithms available that can handle large data sets. As we shall see from the results (presented below), the SVM produced reasonable classifications despite the statistical variability in the training examples. Note that other clustering techniques may be more appropriate, especially for classifying data that resides in more than two clusters [4]. The classifier is trained using a fully supervised set of experiments, i.e. we presented the detector with a wide variety of known training examples before attempting detection on a set of (different) unknown situations (where it was not known at the start where the foreign objects were placed).
The transition probabilities in the figure are Pnn = P r(n|n, F ), Pnr = P r(r|n, F ), Prn = P r(n|r, F ) and Prr = P r(r|r, F ), where r is “resonance found” and n is “no resonance found”. Each probability is conditioned on the previous state as well as whether a foreign object is present (the conditioning variable F ). We then apply a modified version of the Viterbi algorithm [4], [7] to generate a most likely state trajectory given each of the two models. The Viterbi algorithm also conveniently provides a maximum a posteriori estimate of the probability Md AP (s|F ) of the path, given the model parameters. The bold variable s is the Viterbi extracted sequence of (r, n) over an observation period. By virtue of the maximum-entropy principle [4], the best model will exhibit the highest probability over the observation period. Hence, we base a yes/no decision on the likelihood ratio Md AP (s|F = object) > C, Md AP (s|F = no object)
(1)
where C is chosen to optimize the tradeoff between true positives and false alarms. IV. T EST EXAMPLES We tested the detector system using two types of background material: corn ears and moist grass. The foreign objects were all randomly shaped granite-like rocks with a range of sizes from 5cm to 20cm diameter. Both the grass and the corn produce a high-background clutter environment thereby providing a challenging environment for extracting rock signatures. In order to simulate the motion of material moving at substantial speed (3-5m/s), the background material and the foreign objects were placed on a large turntable, with the foreign objects spaced at various intervals (schematically shown in Figure 8). The FMCW transceiver parameters are given in the Table I. The corn background produced the output signals in Fig. 9.
Fig. 8.
Illustration of material placement on turntable for motion test.
TABLE I
Fig. 10. Illustration of output signal for rocks in damp grass. The time sequence of probabilities for “rock” (red), “no-rock” (green) and the likelihood that we have found a rock (magenta). Again, note that rock likelihood curve has been shifted for visibility.
FMCW RADAR PARAMETERS . Frequency span Sweep type Num. freq. steps/ms Sweep period ∆f Audio sample rate
4.3-7.5GHz Up-sweep 1000 4ms 800kHz 40ksps
V. S UMMARY
AND CONCLUSIONS
In this brief article, we presented a hardware/software system that uses an ultrawideband 4-8GHz FMCW radar and a hidden-Markov model to extract the signatures of foreign objects from a non static high-clutter background. The hidden Markov model provided detection for objects from 5cm to 20cm diameter in various orientations given the 48GHz FMCW frequency excursion. Smaller objects would be resolvable with wider frequency sweeps. Note that reliable detection requires training data on expected background material and some representative examples of the expected foreign objects. Having training data for both the “normal” and “object present” situations provides the classifier and Markov models with the expected “distance” needed for making valid decisions. More details of these training methods and classifiers will be presented at the conference. R EFERENCES
Fig. 9. Illustration of output signal for rock in corn ears. The time sequence of probabilities for “rock” (red), “no-rock” (green) and the likelihood that we have found a rock (magenta). Note that rock likelihood curve has been shifted for visibility.
Each peak in the lower magenta curve represents a possible rock detection. Choosing an appropriate threshold yields a good detection rate with minimal false alarms. The same type of time sequence is observed for grass as a background material in Fig. 10. Note that two possible false negatives appear at peaks 4,5. Given the highly unpredictable nature of the foreign object shapes and compositions, a significant probability of error is inescapable. The goal, however, is to intercept as many undesired objects as possible in an attempt to minimize repair costs over time.
[1] M. J. Digman et al., “Acoustic stone detection for a feederhouse on an agricultural combine,” US Patent 6269618, http://www.google.com/patents/US6269618. [2] J. Straeter, et al., “Agricultural vehicle utilizing a hard object detection assembly,” US Paten Application US 20120322519 A1, http://www.google.com/patents/US20120322519. [3] R. Toribio and J. Saillard, “Identification of radar target in resonance zone:E-pulse techniques,” Progress in Electromagnetics Research PIER43, pp.39-58, 2003. [4] D. Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press, 2012. [5] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Third Edition, Prentice Hall, 2009. [6] S. Mika, et al., “Fisher discriminant analysis with kernels,” Neural Networks for Signal Processing, Proc. IEEE Sig. Processing Soc. Workshop, pp. 41-48, Aug. 1999. [7] A. Viterbi, Error bounds for convolutional codes and an asymptotically optimum decoding algorithm, IEEE Trans. Information Theory, vol. 13, no. 2, Apr. 1967, pp. 260-269.
