Naval Underwater Systems Center New London, CT 06320-5594 USA
In 1984, several individuals, including some of those on the organizing committee of a then recent NATO ASI (ref (1», met in Germany to discuss future directions in the processing of underwater acoustics signals. What followed was a series of meetings in Canada and the United States to plan the 1988 NATO ASI on Underwater Acoustic Data Processing (ref (2». With representatives from several countries discussing future requirements, certain common themes began to emerge. As the organizing committee searched for a common framework for the meeting, the concept of relying on the sonar equation emerged. Our efforts focused on soliciting and selecting a representative set of tutorial and research work and loosely organizing it about the sonar equation: transmission, the medium, signal/post processing and display.
A feeling was that incremental changes would continue but that if one were to look at the future in terms of major changes one would have to look beyond incremental changes. In particular, an order of magnitude reduction was postulated (that is, a factor of 10 decrease) in acoustic signal levels; in decibels (dB), this factor of ten decrease in power levels represented a 10 dB reduction in signal levels to be processed. Such a postulated decrease was based on a "What if" type of question by the scientific organizing committee. That is, what if the signals that we are trying to extract from the underwater acoustic environment decreased in level by 10 dB. One could have, of course, asked the question: What if the signal power levels decreased by 1 dB or what if 100 dB? The concensus to investigate a 10 dB signal level reduction is a recognition of the concept that when one seeks an order of magnitude improvement in the state of the art in processing, factors that were second order concerns become first order concerns. Hence from a scientific point of view, by posing the problem as a 10 dB change in signal level, one implicity brings all of the previously avoided second order concerns to the forefront. The rate at which such a change in signal levels might theoretically come into being at sometime in the future would not initially detract from the need to investigate the next level of detail in solving problems uncovered in the processing of underwater acoustic signals. There are, of course, some other obvious thoughts about why not to look at 1 dB and 100 dB reduction in Signal levels. One might expect minor adjustments or engineering changes in fielded underwater acoustic equipments to accomodate incremental changes. Changes on the order of 100 dB might well be viewed as analogous to arbitrarily qUiet Signal ing sources and hence, for all practical purposes, acoustically invisible, at least in the passive sense. Such dramatic reductions, in many cases probably are unobtainable except in a theoretical sense or perhaps one in which the signal source has no requirement to propel itself through the
water radiating acoustic energy. In the limit as signal radiation is reduced to zero (as, for example, as it approaches large reductions, perhaps well before 100 dB) one suspects that an heroic search for non traditional and non acoustic means of sensing will ensue in parallel with an intensive investigation of active sonar and investigations of multiple platform integration of diverse clues in a highly integrated and coordinated manner.
Against this backdrop, members of the organizing committee, projected the number of sensors for the next generation (that being the generation necessary to deal with a factor of 10 signal reduction). The number would be expected (at least by back of the envelope calculations) to be approximately 10 times more that currently available if the processing continued as previously accomplished.
2. TECHNICAL PROBLEM The technical problem is depicted in Figure 1. On the left side
of the figure acoust ic energy either radiated or reflected from some underwater object begins to propagate towards receIVIng devices (hydrophones) on the right side of the figure. (Of course, in the active sonar case the signal encoding and transmission through the medium and the object reflectivity characteristics are important.) Acoustic energy propagates well through water and therefore, has been used over the years as a primary means of extracting information about underwater objects. The acoustic energy from the underwater object travels via multiple acoust ic ray paths and may interact with the ocean surface and with a layered ocean bottom. Of course, acoustic energy radiated or reflected from objects not of interest also is present in the ocean environment. Incoming sound rays from signal and noise sources are received at numerous receiving hydrophones. In general, the hydrophones can be moving over the fini te time observation interval. It is the output from these moving hydrophones that is processed in the field of underwater acoustic signal processing using sophisticated equipment and computer based algorithms.
3. UNDERWATER ACOUSTIC MEDIUM In general, there are three dominant propagation modes that depend
on the distance between the acoustic source and the receiving hydrophones. The three modes are: the direct path (present in close range), the bottom bounce mode (present in intermediate range) and the convergence zone(s) (present at longer ranges where multiple acoustic ray paths converge to reinforce the presence of acoustic energy from the radiating/reflecting source). High frequencies with their short wavelengths can resolve small objects but these high frequencies attenuate quickly with range. Alternatively, low frequencies propagate long ranges. Simplisticly then, all other things being equal lower frequencies propagate further and are therefore more prone to discovery. Figure 2 shows low, medium and high propagation loss model outputs versus range to make the point that a factor of four change in frequency can mean a 20 dB change in propagation loss at long range. (Inputs to such propagation loss models typically include a fixed sound speed profile versus depth, that depends on the time of year and the location in the ocean. In general, such propagation loss plots should randomly vary owing to a variety of factors including time and spatial variation of the sound speed profile.) Also present in the transmission of underwater sound is a preponderance of multiple paths that
TECHNICAL PROBLEM
AIR
OCEAN INCOMING SOUND
~ RAYS
~ ACOUSTIC SOURCE /' ~ OF RADIATED OR REFLECTED ENERGY 0 o 0
RECEIVING 0 0 o 0
0 o 0 HYDROPHONES
-.... ~ :::: '/
OCEAN
LAYERED BOTTOM -FIGURE (1) THE UNDERWATER ACOUSTIC TECHNICAL PROBLEM
PROPAGATION LOSS o -r--.---.- -- - --T- -----,-----T--T---,--------,-----,
20
40
-: 60 :t. -.... 80 iii .., ;;;100 U) o ....I 120 Q. o g: 140
160
180
20 dB
T
DEEP SOURCE DEEP RECEIVER
1st CZ
ONE PARTICULAR OCEAN AREA ONE PARTICULAR TIME OF YEAR
FIGURE (2) ARTISTS SKETCH OF PREDICTED PROP LOSS VS RANGE
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split the acoustic energy. These multiple paths (or multipaths) must be recombined if one is to fully exploit the available source of acoustic energy. It is also possible to exploit the geometrical properties of multipath present in the bottom bounce mode by investigating the apparent apperture created owing to the different path arrivals. In the simpliest case of first order bottom bounce transmission there is (from source to receiver) a bottom bounce ray path (B), a surface interaction followed by a bottom interaction (SB), a bottom followed by a surface (BS) and a fourth path that first hits the surface, then the bottom and finally the surface before being received (SBS). These four ray path are sometimes denoted: B, SB, BS, SBS. The exploitation of these paths is discussed in these proceedings, ref (2).
There are, of course, sensitivities to the ability to exploit the environment due to a variety of factors, including: the loss of acoustic coherence due to boundary interaction as a function of grazing angle, the presence of surface, bottom and volume reverberation, signal spreading owing to the modulating effect of surface motion, biologics as a function of time, and statistics of the noise in the medium.
4. HYDROPHONES OUTPUTS Hydrophones sensors receive radiated and reflected acoustic energy
that arrives through the multiple paths of the ocean medium from a variety of sources and reflectors. The wavefront arrival sensed can be distorted, for example, "crinkled", and may only be partially coherent from sensor to sensor. The ambient noise may have unusual vertical directivity and in some environments the noise due to ice motion may provide unusual interference. Included in these are: fish, shipping (surface and subsurface), active transmissions (before and/or after reflection and under ones own contro 1 and not under ones own contro 1 ) . Act i ve sonars reflect energy off the ocean surface and the bottom including the sub bottom layered structure. Unwanted back scatter like the headlights of a car driving in fog can cause degraded processing gain without proper processing. Passive sonars rely on acoustic energy that is transient in nature or more or less continuous in nature. The continuously radiated energy when analyzed in the frequency domain with appropriate processing techniques can have a wide extent in frequency (broadband) or a much narrower extent (narrowband). Active and passive characteristics of mines, weapons, decoys, reverberation, interference and ship wrecks can all be received by underwater sensors.
5. PROCESSING FUNCTIONS The four primary processing functions for underwater acoustic
devices is to detect, localize, classify, and analyse motion. The first function is to detect the presence or absence of an object. The second function is to localize the position of an object. The source position is estimated in range, bearing and depth. The statistical uncertainity of the estimates is important. In the passive case the ability to estimate range is extremely limited by the geometry of the measurements. Range estimation accuracy is not as difficult in the active sonar case. The third function of importance is classification. This classification function determines the type of object that has radiated or reflected acoustic energy. For example, is the object a fish or is it a mine? The action one takes, of course, is highly dependent upon this important
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function. The amount of radiated or reflected signal power relative to the background noise (that is, the signal to noise power ratio, or SNR) necessary to achieve good classification may be higher for classification than for detection. It is also possible that the type of processing to be done to classify is different than the type of processing to detect. Processing methods that are developed on the basis of detection might not have the requiste SNR to perform the classification function well. The fourth function of underwater acoustic processing is to perform contact motion analysis (that is, to estimate parameters of course and speed). Generally, non linear filtering methods including Kalman-Bucy filters are applied and rely upon a state space model for the contacts motion. For example, the underlying model of motion could assume a straight line course and constant speed of the contact of interest. When the acoustic source of interest behaves like the model then results consistent with the fundamental theory can be expected.
6. METHODS FOR IDENTIFYING FUTURE TRENDS In looking for the future trends in underwater acoustic
processing, a natural question arises: if current underwater acoustic processing devices meet the requirements for which they were designed (wi th, of course, proper training, operat ions and maintenance and wi th engineering changes and evolutionary planned upgrades), then how does one decide what R&D should be pursued for revolutionary advances? One answer to this question comes in two parts: first, it is important to maintain an awareness of non acoustic technologies and how to incorporate them into underwater acoustic signal processing. The second part of the answer to this question comes from exercising the sonar equation.
To amplify the answer consider Figure 3. In the top of the figure acoustic signals come into a block denoted signal and post processing the output of that block or "black box" goes to a decision maker who also gets input from non acoustic signals. If the decision maker is on a surface ship these may be optical signals obtained by looking out of a window (porthole) of the ship. The non acoustic techniques may, at some time in the future, include blue green lasers of the type discussed in a recent issue of Scientific American. In the bottom of Figure 3 the signal and post processing block if "fed" information directly that in the depiction at the top of the page went directly to the decision maker. Now the signal and post processing box can improve the quality of information sent on the the decision maker by employing all of the information that would not otherwise have been available. Figure 3 also, implicity, makes one aware that the purpose of signal and post processing is to support a decision making process. In the fishing industry for example, the sonar search for fish might be aided by radio input about the general location and type of fish being caught by ships at certain fishing sites. If sonar is not the primary way to provide the decision maker with the needed information on underwater objects then the underwater acoustic processing outputs should be "fed" into a nonacoustic processing box. Until such time as nonacoustic (or so called, unsound) methods overtake acoustic methods, a standard tool for analysis is the sonar equation.
The sonar equation has been the unifying structure about which this 1988 NATO ASI is organized. The major terms in the sonar equation are measured in decibels (dB). The terms are shown in Figure 4. L
s indicates source level. In the passive sonar case source level is a term
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CURRENT:
ACOUSTIC SIGNAL AND POST DECISION SIGNALS PROCESSING MAKER
t NON ACOUSTIC SIGNALS
FUTURE:
ACOUSTIC SIGNAL AND POST DECISION SIGNALS PROCESSING MAKER
t t NON ACOUSTIC SIGNALS
FIGURE (3) BLOCK DIAGRAM OF CURRENT AND FUTURE SYSTEMS
DEFINITION OF TERMS
LS = SOURCE LEVEL (dB)
LN = NOISE LEVEL (dB)
NDI = DIRECTIVITY INDEX (dB)
NTS = TARGET STRENGTH (dB)
NRD = RECOGNITION DIFFERENTIAL (dB)
FIGURE (4) MAJOR TERMS IN THE SONAR EQUATION
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not under one's control; in the active sonar case, it is a term that in theory can be made arbitrarily large. Of course, as it becomes larger the sound levels can become unpleasant; moreover, louder transmit sources are less covert, thereby subjecting the transmit platform to counter detection. Ln is the noise level due to all sources. NDl is the
directivity index or DI and is in some sense a measure of the ability of a receiving array to discriminate against the unwanted noise. NTS is the
target strength. Underwater objects wi th large values of TS are more easily detectable than those with small values of TS. In general TS varies as a function of aspect angle, that is, the direction at which impinging acoustic energy reaches the underwater object and also the reflection angle. NHD is the recognition differential of the processing
system. It is the processing system output SNR required for a 50% probabil i ty of detection. It may typically be zero dB. The figure of merit (FOM) is computed as shown in Figure 5. Systems are designed so that the FOM exceeds the propagation loss. The amount above the FOM is called the signal excess. If two potential advisaries systems are compared the one with the largest signal excess is said to hold the acoustic advantage; the amount of that advantage can be quantified by in decibels but its impact will vary with the propagation conditions where the advantage is employed. In the active sonar case one must take into account the target strength and the two way propagation loss.
7. IMPLYING BROAD TRENDS FROM THE SONAR EQUATION Using the sonar equation, we might ask what is the impact if our
range requirements increased, if the target strength decreased (in the active sonar case), and if the radiated signal level decreased (in the passi ve case). In a very broad sense these so called "What if" questions, whether or not they actually occur, drive us to think about the future potential areas of future requirements. In particular, the sonar equation dictates that we should strive to increase the directivity index, DI, and increase the active sonar source level (and, incidentally, the source bandwidth). Our understanding of comparing FOM with propagation loss drives us toward lower frequencies. More specifically, we see that it is cri tical to increase array size and the number of receiving hydrophones sensing uncorrelated noise. Also, we will need processing to counter sensor motion and medium effects experienced over large appertures and we will need processing methods capable of coping with the increased operator load comensurate with the increased number of beams resultant from improved DI. In the active scenario increased source levels will mean processing methods to develop orthogonal waveforms and processing to reduce mutual interference and exploit third party transmissions. Decreased frequency and increased bandwidth will cause fundamental limits to be approached.
8. SPECIFIC FUTURE TRENDS IN UNDERWYATER ACOUSTIC SIGNAL PROCESSING The broad trends in processing are shown in Figure 6. It is
expected that there will exist a requirement for faster signal processors with more complex archi tectures to handle the larger number of input sensors. New software, in particular, compilers and graph partitioning algorithms, will be required to exploit these sophisticated signal
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FIGURE OF MERIT (FOM)
PASSIVE
FOM p = Ls - (LN - Nol) - NRO
ACTIVE
DESIGN SYSTEMS SO THAT FOM EXCEEDS PROPAGATION LOSS (ONE WAY LOSS FOR PASSIVE; TWO WAY LOSS FOR ACTIVE)
FIGURE (5) SONAR/ FIGURE OF MERIT (FOM) EQUATION
WHAT IF
• RANGE REQUIREMENTS INCREASED
• TARGET STRENGTH DECREASED (ACTIVE)
• RADIATED SIGNAL LEVEL DECREASED (PASSIVE)
BROAD TRENDS
• INCREASE DIRECTIVITY INDEX (NUMBER OF RECEIVING HYDROPHONES)
• INCREASE ACTIVE SOURCE LEVEL AND BANDWIDTH
• DECREASE FREQUENCY
FIGURE (6) BROAD TRENDS IN UNDERWATER ACOUSTIC SIGNAL PROCESSING
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processors. There will exist processing requirements for sensor stabilization of moving sensors and beamforming in correlated noise. A closer coupling of signal processing with environmental models of received signals and noise will be required. Also present will be processing methods for exploitation of propagation modes, including those at lower frequencies and longer ranges as well as traditional shorter range, higher frequency sonars, all operating in a wide range of environments from the tropics to the artic. There will be a need for sophisticated target and medium splitting recombination algorithms. Active sonar will need processing for orthogonal waveforms, reverberation rejection and normalization as well as interference rejection and cancellation. A requirement wi 11 exist for partial automation to reduce operator load caused by the significantly increased number of beamformer outputs and for full automation in unmanned devices such as autonomous underwater vehicles. Finally, processing methods must be developed to improve automatic classification to handle the significant increase in incoming sensor data.
9. CONCLUSIONS In conclusion a major future trend in underwater acoustic
processing and theme of this NATO ASI is automatic signal and data processing. This theme was selected by the organizing committee after numerous meetings and discussions over a four year period. What has clearly emerged, is that if one addresses the situation from the classical perspective of detection, localization, classification and contact motion analysis and postUlates a series of "what if" questions, including: "What if the objects whose signals we seek to process become increasingly covert?", then the inescapable conclusions are to draw us to investigate proposed systems of greater sensitivity and commensurate increased processing and operator loading demands. All of this drives one to the conclusion that greater automation will be required to handle the future requirements. The interested reader is referred to other contributions in this 1988 NATO ASI series, ref (2).
10. ACKNOllLEDGMENTS Other members of the organizing committee included:
Chan, Canada; Dr. G.A. Lampropoulos, Canada; Prof. J.W.R. United Kingdom; Prof. C. van Schooneveld, The Netherlands; Heinz Urban, West Germany; Dr. Norman Owsley, USA; and Dr. Walker, Canada.
11. REFERENCES
Prof. Y. T. Griffi ths,
Dipl. Ing. Robert S.
1. Urban, H.G. (Ed.), Adaptive Methods in Underwater Acoustics, D. Reidel Publishing Co., Dordrecht, Proceedings of a 1984 NATO ASI, 1985.
2. Chan, Y.T. (Ed.), Underwater Acoustic Data Processing, Kluwer Academic Publishers, Dordrecht, Proceeding of a 1988 NATO ASI, 1989.
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DISCUSSION
Comment: P. Schultheiss
You emphasized the need for automation in sonar signal processing including both active and passive classification. You also emphasized the need to investigate long ranges, low frequencies and active systems. Would you comment on the role of automation in passive classification?
Reply: C. Carter
As the directivity index improves, radiating and reflecting objects not of interest will use up processing resources and load down the operator with a potentially overwhelming number of tasks. The application of automated passive classification techniques, proposed in this NATO ASI will be critical and play a major role in removing processed signals not of interest, thereby, freeing the operator to devote resources to objects of interest.
Comment: N. Owsley
You mentioned improving automatic classification as part of the procedure for averting problems associated with false classification, can the problems be avoided?
Reply: C. Carter
If we expect signal processing to perform classification well, then we must include that thinking in the initial design process; that is to say, if requirements are written for detection and tracking it should not be surprising that the equipment builders build something that does not classify well. In addition, classification systems, like detection systems can be characterized by performance curves. In detector performance we plot probabil i ty of detect ion versus probabi I ity of false alarm. Classification systems can be characterized by probability of saying an object is in a class given that it actually is in that class versus the probabi 1 i ty of saying it is in a class given that is in a different class . As with detector performance, one can select where one operates on these performance curves but there always will exist a fundamental tradeoff between correct classification and incorrect classification. In that sense future problems, even tragedies can only be minimized by incorporation of classification early in the design process, they cannot be eliminated.
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Comment: C. van Schooneveld
When you speak of the increased need for automation do you mean the routine data logging type or the sophisticated signal and data processing kind?
Reply: C. Carter
Future sonar signal processing methods wi 11 require both, though not all will require extensive Research and Development. Increasing the array directivity implies a greater number of processed signals not of interest, will reach the sonar signal processing equipment operator. Automated techniques will be required. Some of the methods of artificial intelligence to be discussed will be applied to the post processing function of classification. With many methods employed the human operator will be free to perform those functions that only he or she can.
