Pattern recognition processing in underwater acoustics

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0031 3203/83 $3.00+ .00 Pergamon Press Lid i~ 1983 Pattern Recognition Sot)ely

PATTERN RECOGNITION PROCESSING IN U N D E R W A T E R ACOUSTICS* C. H. CHr!N Electrical and Computer Engineering Department, Southeastern Massachusetts University, N. Dartmouth, MA 02747, U.S.A. (Received 1 March 1983; in revised form 16 March 1983; received for publication 29 April 1983)

Abstract---Pattern recognition application to underwater acoustics is a relatively less explored area, even considering some reports which may not be available in the public domain. In this paper three problem areas of pattern recognition processing in underwater acoustics are reported. The first problem is threedimensional target motion analysis. A reference map is generated and partitioned into a number of cells corresponding to possible target locations. The pattern matching idea is then used to estimate the target range, velocity and bearing. The second problem is concerned with multipath ranging. An image processing technique is used to extract the significant tracks from the correlograms to provide a continuous estimate of time delay or range under a multipath environment. The third problem deals with submarine transient signals. A spectral analysis is made to determine spectral features for detection and event classification. The use of entropy distance measure for waveform segmentation is then examined. Major computer results are presented along with a discussion of unresolved issues for each problem area. Target motion analysis Pattern matching Multipath ranging Image processing Track enhancement Path identification Underwater transient Spectral analysis Event classification

1. INTRODUCTION In recent years, pattern recognition and image processing have found increased use in underwater acoustics as digital signal processing is playing a major role in this field. For a detailed survey on the role of digital signal processing the readers are referred to Knight et al. I1) In this paper, new research work is reported on three problem areas using pattern recognition processing. The first problem is three-dimensional target motion analysis (TMAI with bearing and multipath delay measurements. Pattern recognition is considered in the context of TMA. Two approaches are examined. The first approach uses pattern matching to identify the target location, employing the conventional optimal filtering. The second approach employs extended Kalman filtering but with initial parameters determined by pattern matching. The second problem area is called muhipath ranging pattern recognition. The sequences of crosscorrelations and autocorrelations form correlograms from which delays of identifiable multipaths must be determined. Tracks in the correlograms must be enhanced by image processing so that continuous estimation of delays and thus ranges can be obtained. For both problems stated above, pattern recognition and image processing are shown to provide very significant improvement in

TMA capability in a multipath environment. The third problem is concerned with the characterization and classification of underwater acoustic transient signals. The work is much needed to provide a fundamental understanding of submarine signatures for ship silencing and vulnerability assessment. Only some preliminary results on spectral feature analysis will be presented.

2. PATTERN RECOGNITION IN TARGET MOTION ANALYSIS

In the passive sonar system, one is primarily concerned with estimating both the temporal and spatial structure of an observed signal field. The passive sonar bases its detection and estimation on sounds which emanate from the target itself, including machinary noise, flow noise and transmissions from its active sonar, etc. In addition to the direct path, there are multipaths in the soundwave propagation. Two typical propagation paths along with the target/observer geometry are shown in Fig. 1. With the use of signal processing algorithms, travel time difference between various paths can be measured. The multipath delays can be used to augument bearing and other sensor information to enable a three-dimensional TMA. As the target signal is detected, two processors are used to obtain the desired information. First, the signal processor is used to extract all possible information such as bearing, time dealy, frequency, etc. Secondly. * Partially supported under IPA with Naval Underwater the data processor is used to extract from the signal Systems Center. 627 ~'h !o:,--~

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information includes, range, bearing, speed, course and depth. Two approaches to using pattern recognition in data processing are examined. The first approach uses a pattern matching method. A reference map of the pitch plane is generated by partitioning it into a number of small cells (Fig. 2). Each cell corresponds to a pattern class. The feature set consists of four multipath delays and the bearing measurement. Each input feature set is identified to the most likely cell with the smallest Euclidean distance. The cell identified by pattern matching provides estimates of horizontal range and target depth, which along with bearing completely determines the target location at that time instant. With each time increment the feature values of all cells in the reference map must be updated. The approach is simple and effective. Any additional information can be easily integrated into the feature measurement in the pattern recognition processing. In the second approach the conventional extended Kalman filter is used, but the initial parameter values

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Time (miLLiseconds) Fig. 5 (cont.) (d) Center autocorrelation. (e) After sensor autocorrelation. Figure 3 shows two geometries of own ship/target encounter. The sampling rate is 1 sample every 20 seconds. For the first encounter, the initial range is 2617 yards and the target is a straight-running vehicle with a speed of 5 knots and a course of 169.7 °. Own ship runs three 240-second legs at a constant speed of 8 knots and respective courses of 140 °, 186.8 ° and 125.6°.. A 0.4°/second turning rate is employed for all own ship maneuvers. Also own ship and the target operate at the constant depths of 294 and 618 feet, respectively. For the second encounter, the initial range is 5000 yards and the target is a constant velocity vehicle with a speed of 5 knots and a course of 0 °. Own ship runs four 240-second legs at a constant speed of 5 knots and respective courses of 60 °, 147°, 6° and 126°. A 3°/second turning rate is employed for all own ship maneuvers. Own ship and the target are both operating at the same depth of 1000 feet. In generating the reference maps for the pattern matching method, the size of the cell is selected as 40 x 40 yards. There are 25 x 100 cells. To improve the classification accuracy, each cell is subdivided into 8 × 8 subcelis of size 5 x 5 yards each. The classification is performed in two steps. In the first step, a cell is identified. Then, in the second step, a subcell of the cell is identified. The use of subcells greatly improves the resulting classification accuracy. Assume that the standard deviations of time delay noises are the same and equal to a, = 5 msec. Also, the standard deviation of bearing noise is al~ = 0.5 °. For

Geometry No. 1, the azimuth range errors and the velocity errors are given in Tables 1 and 2 for both approaches. For Geometry No. 2, the errors are shown in Tables 3 and 4. For all the errors reported, the pattern matching method of approach clearly outperforms the extended Kalman filtering method. It is noted that the filtering method converges very fast, because of the use of initial parameters determined by the recognition procedure. Without such initial values, the filter convergence behavior is much worse. Thus, the pattern recognition idea has greatly increased the three-dimensional TMA capability. As the interest in developing a fully automatic passive ranging system increases, the use of pattern recognition is expected to become essential to the implementation of such a system. 3. M U L T I P A T H

RANGING

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It is well known that multipath ranging can provide good estimates of target motion, range and target depth solutions. The multipath solutions depend critically on the correct estimate of multipath structure, i.e. the identification of paths from the crosscorrelation and autocorrelation data. Muitisensors certainly provide more information than a single sensor. Still, there is much ambiguity in determining the multipaths. Satisfactory procedures to identify automatically the paths are yet to be developed. This is the area in which pattern recognition can help. A pattern matching

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[l:l] Fig. 6. Diagrams of peak locations for 85 records (compressed) of the first multipath data file. (a) Forward---centercrosscorrelation. (b) Center-after crosscorrclation. (c) Forward autocorrelation. (d) C~tcr autocorrelation. (e) After autocorrelation.

technique was previously proposed in an unpublished work, but not implemented, for muitipath ranging• The idea is to match the correlograms generated from actual sea data with those predicted by an ocean model, in a manner similar to fingerprint identification. The predicted correlograms can be obtained from the NUSC (Naval Underwater Systems Center) generic sonar model. The difficulty here is that the number of possible multipath combinations to be matched can be very large. For this reason, image processing techniques are used in this study to extract a few significant tracks from the correiograms. This procedure will be examined in detail in this section. The detailed mathematical analysis is available in Chen and Kok. TM The linear array employed in multipath ranging

consists of three sensors called forward, center and after sensors. To illustrate the range estimate, consider two sensors as shown in Fig. 4. R t and R 2 denote the horizontal ranges between target and first sensor and target and second sensor, respectively. L is the distance between the two sensors. If Rt, R 2 >> L, then R 2 ~ R t + L cos0 where 90 ° - 0 is the bearing of the target from the array• The delay z can be measured from correlation records and is given by Cz

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Processing in underwater acoustics

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of water depth. In the case of multipaths, R 1 and R 2 are the total path lengths, but the range estimation is more complicated unless the type of multipath is known. Path identification is the determination of the type of multipath. For a linear array with three sensors in the presence of multipaths and multi-targets, developing procedures to estimate range and bearing is the subject of current study. Figure 5 shows a typical set of five correlation records, including two crosscorrelations and three autocorrelations for the forward, center and after sensors. There are 2048 points for each crosscorrelation record with 700msec duration, and 1024 points for each autocorrelation record with 350 msec duration. Two such multipath data files will be studied. Each file consists of 85 sets of correlation records. Time delays correspond to the peaks in the cor-

relation records. It is obviously impossible to use all local maxima in magnitude as peaks. Our peak selection algorithm selects, somewhat arbitrarily, 10 peaks of the envelope of each correlation waveform and stores them in the output file. Thus, each output file contains 85 records with lO points in each record. These output files are treated as images and processed with methods used in image processing. For the first multipath data file, the selected peaks are displayed in Fig. 6(a) and (b) for cross correlations and Fig. 6(c), (d) and (e) for autocorrelations. These figures are compressed horizontally due to the limited displaying window of the AED 512 graphic terminal. For forward-center and center-after crosscorrelations, they are compressed from 2048 points to 512 points. For the three autocorrelations, 1024 points are compressed to 512 points. Figure 7 shows a portion of each

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diagram in Fig. 6 without compression. For crosscorrelation, only the central parts (from the 770th to the 1270th points} are shown. For autocorrelations, the first 512 points are shown. A few tracks are visible in Figs. 6 and 7. These tracks represent the primary time delays for determining the target parameters. The procedure of extracting tracks from Fig. 7 is as follows. First, we pick a point, say Pt, and determine whether it is on a track. We search for neighboring points in the next R records in the window shown in Fig. 8. If a point P2 can be found inside the window, then P1 is considered to be a candidate on a track. Now a similar window is set up for point P2 and we try to find a neighboring point of P2. This procedure is repeated until the end of the data file. If more than one

Fig. 9. Track enhancement of Fig. 7 data.

Processing i,~ underwater acoustics

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Fig. 10. Diagrams of peak locations for 85 records (compressed) of the second multipath data file.

point is located inside the window of PI, then we choose the point whose record number is the smallest. If it happens that more than one point of this record is inside the window, then the nearest point will be chosen as P2. Therefore. rules for selecting the neighboring point P2 are based on the nearest record and the nearest delay time. Two conditions which will cause the procedures to stop are the last record and an empty window. If P1, P2 ..... P, have been found and the procedure stops at P,,. then the qualification of PI, P2 . . . . . P,, to form a track is decided by a threshold we choose. If N is the threshold value, then P}. P2 ..... Pv form a track when n > N, otherwise PI. P2 ..... P,, will be deleted entirely. Using the algorithm described above, tracks are extracted from Fig. 7 and shown in Fig. 9, the tracks are now considerably enhanced, although there are PR l~:b-H

some gaps, which can be filled in by interpolation. Each track represents a continuous variation of time delay or range. These tracks must be identified with multipaths. Initially, range, bearing and depth of the target are all unknown variables. Different combinations of these variables can produce the same delay time. Therefore, the tracks in Fig. 9 are not in one-to-one correspondence to the parameters of the target(s) to be estimated. Typically, there are ten significant multipaths, including various bottom bounce and surface bounce waves. For two sensors, there are 100 possible combinations, only a small number of which are significant. A priori knowledge about range and depth can be used in making a final decision about the most likely path combinations. Detailed procedures are available in Chen and Kok. ~3} The existence of multiple targets can be detected from multiple

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Fig. 11. Track enhancement of Fig. 10 data.

range estimates. For Fig. 9, two targets have been detected and their ranges are estimated with good accuracy. For the second muitipath data file, the compressed diagrams of peak locations are shown in Fig. lO(a) and (b) for crosscorrelations and Fig. 10(c), (d) and (e) for autocorrelations. The enhanced tracks are shown in Fig. 11. Only one target is detected and the range is estimated accurately. 4. U N D E R W A T E R TRANSIENT S I G N A L ANALYSIS AND R E C O G N I T I O N

Underwater acoustic transient signal events are caused by mechanically induced or flow radiated noises. Figure 12(a) and (b) is two sections of underwater transient data, with and without events, respectively. Each section here consists of 2048 points,

with a sampling rate of l0 kHz. The log-spectra for four 512-point sections are shown in Fig. 13 (upper two curves with event and lower two curves without event). Problem areas for this kind of data are the classification of various events and the detection of the occurrence of events in the presence of background noise. The spectral characteristics of event and nonevent (i.e. noise only) are quite similar, as illustrated in Fig. 13, other than the gain factor. This makes the classification and detection difficult if Fourier spectral features are used. As the signal is nonstationary, short sections of 256 points each are now employed in Fourier analysis and Burg's maximum entropy spectral analysis. The Burg's spectra are determined by algorithms in Chert. c'*~ A comparison of F F T spectra and Burg's spectra are shown in Fig. 14. Figure 14(a) and (b) are for two sections of one event and Fig. 14(c) and (d) are for two sections

Processing in underwater acoustics

Fig. 12. Two sections of an underwater transient event. (al A section with event, tbl A section with noise only.

Fig. 13. Log-spectra of four 512-point sections of the transient data.

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of a second event. It is noted that the Burg's spectra match quite well with the FFT spectra, especially in spectral peaks. The Burg's spectra enhance the major harmonic components. These components are different for various events and between event and nonevent portions of the signal. Thus, spectral features measured in signal powers of several frequency bands, as determined from the Burg's spectra, are potentially effective for detection and classification. Waveform segmentation is required to determine when an event starts and ends. Among several techniques examined, the use of entropy distance measure LS~ shows the best segmentation performance. Consider two adjacent sections of 60 data points, each with the first section as reference and the second one as

test section. The entropy distance is d = - lnL where ;. is the likelihood ratio of the joint likelihood functions of the two sections under the hypothesis that the reference and test sections have identical autoregressive models, against the alternative that they have different model parameters. The entropy distance is determined sequentially for 60-point sections and those sections whose entropy distance exceeds some threshold are identified. For such sections, every 10-point subsection is examined again using the entropy distance. Then, the subsection with the largest entropy distance is further examined by using the entropy distance to determine the occurrence of a segment boundary. The procedure performs well, as illustrated in Fig. 15. Fig. 151a) is the entropy distance of a set of submarine

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transient signals and Fig. 15(b) shows the correctly identified segment boundaries of an event. A second order autoregressive model has been used in the computer study, which appears to be adequate for the data considered. Both feature extraction and waveform segmentation of the submarine transient signals require further study. 5. CONCLUDINGREMARKS This paper and the two preceding papers clearly illustrate some important issues of using pattern recognition, image processing and artificial intelligence methods in underwater acoustics. Some useful solutions have been provided. Continued research effort is much needed to explore many unresolved problem areas.

REFERENCES 1. W. C. Knight. R. G. Pridham and S. M. Kay, Digital signal processing for sonar, Proc. IEEE 69, 11, 1451-1506 (19811. 2. C. H. Chen. Application of signal processing and pattern recognition to underwater acoustics, NATO Advanced Research Workshop on Issues in Acoustic Signal/Image Processing and Recognition, San Miniato, Italy, August 1982. 3. C. H. Chen and A. L. Kok, A signal/image processing study of the multipath ranging problem, Technical Report EE,TR-82-9, Southeastern Massachusetts University (1982). 4. C. H. Chen, Nonlinear Maximum Entropy Spectral Analysis Methotlsfor Sional Recognition. John Wiley & Sons, London (1982). 5. U. Appel and A. V. Brandt, Adaptive sequential segmentation of piecewise stationary time series, (Special Issues on Applied Time Series Analysis,C. H. Chen, ed.) Inf. Sci. d. 29, I-5 (1983).

About the Author--CHt-H At CHI!Nreceivedhis B.S.E.E.degree from the National Taiwan Universityin 1959, the M.S.E.E. degree from the University of Tennessee in 1962 and the Ph.D. degree, also in Electrical Engineering,from Purdue Universityin 1965. From March 1965to September 1968he worked for ADCOM, Inc., Cambridge, MA, U.S.A., and AVCO Systems Division, Wilmington, MA, U.S.A., in the areas of communications systems and statistical data processing. Since September 1968, Dr. Chen has been teaching and doing research in pattern recognition, image and signal processing and communication theory at the Southeastern Massachusetts University,N. Dartmouth, MA, U.S.A.,where he is now Professor of Electrical and Computer Engineering,and was also Department Chairman from 1978 to 1981. He is the author of the books Statistical Pattern Recognition (Hayden Book Company, 1973), Digital Waveform Processing and Recognition (CRC Press, Inc., 1981) and Nonlinear Maximum Entropy Spectral Analysis Methodsfor Signal Recognition (John Wiley & Sons, 1982). He is editor of the books Pattern Recognition and Arfiticial Intelligence (Academic Press, 1976), Computer-aidedSeismic Analysis and Discrimination (ElsevierScientific Publishing, 1978), Pattern Recognition and Signal Processing (Sijthoff& Noordhoff, 1978)and SeismicSignal Analysis and Discrimination (ElsevierScience Publishing, 1982). Dr. Chen is a senior member of the Institute of Electricaland Electronic Engineersand a member of Eta Kappa Nu, the American Societyfor Engineering Education, the American Statistical Association and the Pattern Recognition Society.