Passive Detection and Localization of Transient Signals from Marine
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PassiveDetection andLocalization ofTransient Signals from Marine Mammals usingWidely SpacedBottom Mounted Hydrophonesin Open Ocean Environments Susan 3arvis and David Moretti NavalUndersea Warfare Center Division NewportEngineering,Test and EvaluationDepartment 1'I 76 Howell St., Newport, R.l. 02841-1708 Abstract Oneobjective of the MarineMammal Monitoring on NavyRanges project is to useexisting Navy undersearange infrastructure to develop a toolset for passivedetection and localization of marine mammals.The Office of NavalResearch funded the M3Rproject as part of the Navy'seffort to deter- mine the effects of acoustic emissionson marine mammalsand threatened/ endangeredspecies. A necessaryfirst step in this effort is the creationof a baselineof behaviorwhich requires long-term monitoringof marinemammals. Such monitoring, in turn, requiresthe abilityto detectand localize the animals,This paper will present algorithms for passivedetection and localizationof transient signalsdeveloped as part of the M3Rtoolset. It wiII also present results of the application of these tools to detection and tracking of various toothed whales at the Atlantic UnderseaTest and Evaluation Center AUTEC!, Andros Island, Bahamas. Introduction Navy undersearanges such as the Atlantic UnderseaTest and EvaiuationCenter AUTEC!use arrays of widely spaced bottom mounted hydrophonesto acousticallytrack underseaand surfacevehicles. Traditionally,the vehiclesare equipped with acousticpingers that emit knownidentification signals at knownrepetition rates. Increasingly, range instrumentation infrastructure Figure 1! is being appliedto non-traditionaltracking problems, The Marine Mammal Monitoring on NavyUndersea Ranges M3R!program has developed a set of signal processingtools to detect and track marine mammalsusing Navy range facilities [11. Under the M3Rprogram, algorithms were developed to automatically detect and track two classes of whale vocalizations clicks and whisties. Both of these classesof signals are transient in nature. The tool set was recently tested at AUTECover a two- weekperiod. Over five hundredsquare nautical miles of oceanwere simultaneously monitored via 68 broad-bandhydrophones. Several species of toothedwhales where automatically detected and trackedin real-time.The positionalaccuracy of the M3Rtracking tools was confirmed by visual sightings by a surfacecraft and by manual analysisof the hydrophone data. Proceedingsfrom the international workshop on the Applications of PassiveAcoustics to Fisheries 123 Discussion Visualsightings [2] and auralanalysis of hydrophonerecordings [3] indicatethat manyspecies of toothed whalesare presentat AUTEC.The mostcommonly seen and heardare spermwhales, dol- phinsand short finned pilot whales,all of whichare present nearly year round. Consequently,the M3Rtools weredeveloped to detect and trackthese common species. For purposes of algorithm development, the vocaiizations produced by the whales were characterizedas clicks and whistles. Clicksare, in general,any impulsive,broad-band signal. However, sperm whale clicks were of partic- ular interest.Sperm whale clicks are very distinct,have high sourcelevel, and occurin regularpat- terns [4] or "clicktrains" Figure2!, Whistles were more broadly defined as any narrowbandevent that sweepsin frequencyover time Figure3!. Thereare three distinct partsto the whale localizationproblem. First the vocalizationsor whale callsmust be automaticallydetected. In orderto determinethe positionof the animalin three dimensions,a givencall mustbe detectedon a minimumof four hydrophones.The second part of the problemis the associationof the detectionsreceived on varioushydrophones with eachother. Thatis, one must be able to determinethat the call received on hydrophone A at time tA is the samesignal that wasreceived by hydrophone B attime tB. Finally, associated sets of arrivaltimes areinput into a multilaterationalgorithm to solvefor position.A three-dimensionalhyperbolic posi- tioning model [5] is usedto determinethe vocalizinganimal's location in X,Y, and Z aswell asthe time of emission of the call. M3Remploys a real-timefrequency domain energy detector for whalecall detection. A spectrogram of the incomingacoustic data from each of thehydrophones isformed using 512-point fast Fourier transforms FFT! with a rectangularwindow and fifty percentoverlap. The resultant spectrogram hasa frequencyresolution of approximately51 Hzand a timeresolution of approximately9.8 ms. Eachtime-frequency bin of the spectrogramis comparedto a timevarying threshold, D f t!. The thresholdis set to be m dBabove the time! averagepower within frequencybin f.The output of the detector,Q~ t!,f for eachhydrophone, is a binaryvalued "detection spectrogram" which contains a 1 in eachtime-frequency bin that exceededD f t! anda 0everywhere else Figure4!. The detection spectrogramindicates, in real-time,the presenceof whalevocalizations as well asproviding infor- mationon their frequencycontent. As evidentin Figures2 and3, the signalstructure of sperm whale!clicks is very differentfrom the signalstructure of whistles.Therefore, separate detection associationalgorithms were developed for eachsignal type. A seriesof clicks from a single sperm whale exhibits nearly the same inter-click interval on ail receiv- ing hydrophones.Calls from eachadditional animal exhibit their own uniquepattern. In fact,inter- clickinterval patterns were found to be an effectivemeans of both differentiatingbetween individ- ualwhales and associatingpatterns of detectionsamong hydrophones [6]. In the first stepof the M3Rclick association algorithm the time-frequencydetection spectra from all hydrophoneswere reduced to binary "click maps".Click maps contain a 1 for time indices where broad band events occurredin the detectionspectrum and 0 forall other times Figure 5!. Conceptually,the next step is to crosscorrelate the clickmaps from severalhydrophones with a masterhydrophone to Findthe difference in time of arrival between each hydrophone and the master. However,care must be Proceedingsfrom the international Workshop on the Applications of PassiveAcovstics to Rsheries 124 taken in implementing the crosscorrelation in order to properly associateeach click detection amongthe hydrophones.Figure 6a showsan exampleof a clickmap from a singlehydrophone containingclicks from two individuals,Figure 6b showsthe clickmap from a secondhydrophone overthe sametime period.The question is which clicksin figure6b belongto whichindividual? TheM3R click association algorithm uses the notionof a "scanningsieve" [7] to matchdetection pat- ternsbetween hydrophones. The sequence of cilickdetections i.e. click map! within the scanning sieveon the masterchannel is comparedto the clicksmaps from surroundinghydrophones "scanned"signals!. The scanning sieve time window always starts on a click detection, and is moved acrossthe scannedsignal one click detection at a time, That is,the resultant correlation valueat anytime delayrepresents the numberof matchesbetween the masterchannel pattern startinga specificclick and the scannedchannel pattern starting at a specificclick, The delay of the rnaxirnum correlation value represents the difference in time of arrival where the first clicks in the scanningsieve and the scannedsignal were best aligned. The output of the scanningsieve process are sets of time difference of arrival TDOA! for each click detection received on the master channe! Figure7!. TheTDOA data from eachhydrophone are then histogrammedto estimatethe number of separablesources Figure 8!, Onlydetections associated with significantpopulations are used Figure9!. Theseassociated TDOA sets are then sentto the AUTECmultilateration tracking algo- rithm which calculates3D position. Whistle vocalizations do not typically follow known repetition patterns. An individual can emit a single short whistle or groups of sweepsthat last severalseconds or both. However,the time-fre- quency characteristicsof the calls in whatever sequencethey may occur remain the same on all receivinghydrophones. To determine the TDOAof signalsamong the hydrophones,the detection spectrogramsQ f t! of the availablehydrophones are cross-correlated against a masterchannel, M [8].The cross correlation C tg between the i-th channel and the masterchannel is calculatedover a time window of approximately6 to 10 seconds.That time windowis then advancedby onehalf its duration and Ci tg is updated, Ci t,x!=ZZQ~ f,t!Qi f,t+~! Thetime delayassociated with the peakof the correlationfunctions indicates the TDOAfor a signal relativeto the masterhydrophone Figure 10a!. If whistlesfrom multiplewhales are presentwithin a crosscorrelation time window,multiple correlation peaks will be evident Figure10b!. Note that if both clicksand whistlesare present at the sametime, sperm whale clicks will dominatethe detec- tion spectra.Correlation peaks due to whistlesignals will be obscured.Therefore, for practicalpur- poses,broad-band click events should be removedfrom the detectionspectra prior to crosscorrela- tion. Whilecross correlation of detectionspectra indicates times of signalarrival and the presenceof multiplewhales, it doesnot associatethe time delaysof the correlationpeaks with an individual acrossthe hydrophonechannels. However, as mentionedearly, the sequenceof whistlesfrom an individualis the sameon all receivinghydrophones. Figure 11 shows the time differencesof arrival relative to a master hydrophone of the correlation peaksfor five hydrophones. Notice that there are Proceedingsfrom the internationalWorkshop