Tracking time differences of arrivals of multiple sound sources in the presence of clutter and missed detections

Abstract

Acoustic line transect surveys are often used in combination with visual methods to estimate the abundance of marine mammal populations. These surveys typically use towed linear hydrophone arrays and estimate the time differences of arrival (TDOAs) of the signal of interest between the pairs of hydrophones. The signal source TDOAs or bearings are then tracked through time to estimate the animal position, often manually. The process of estimating TDOAs from data and tracking them through time can be especially challenging in the presence of multiple acoustically active sources, missed detections, and clutter (false TDOAs). This study proposes a multi-target tracking method to automate TDOA tracking. The problem formulation is based on the Gaussian mixture probability hypothesis density filter and includes multiple sources, source appearance and disappearance, missed detections, and false alarms. It is shown that by using an extended measurement model and combining measurements from broadband echolocation clicks and narrowband whistles, more information can be extracted from the acoustic encounters. The method is demonstrated on false killer whale (Pseudorca crassidens) recordings from Hawaiian waters.

Vocal behavior of false killer whale (Pseudorca crassidens) acoustic subgroups

“…Recordings from the first and the last hydrophone of the towed array system were used for the analysis in order to maximize the sensor separation and thus the spatial resolution (Gruden et al, 2021). Sensor separation for each encounter is listed in Table reftable: encounters.…”

“…Sensor separation for each encounter is listed in Table reftable: encounters. The data were pre-processed as described in Ref (Gruden et al, 2021). to obtain cross-correlograms and examples can be seen in Supplementary Material 1 .…”

“…Prior to constructing cross-correlograms for this study, the data were pre-processed as follows. To construct a click-based crosscorrelogram, the data were first bandpass filtered using a 4th-order Butterworth filter with cut-off frequencies of 8 and 30 kHz (Gruden et al, 2021). To construct a whistle-based cross-correlogram, the data were first processed with a three-stage click removal process which included a 4th-order Butterworth bandpass filter with cut-off frequencies of 2.5 and 12 kHz (Gruden et al, 2021).…”

“…To construct a click-based crosscorrelogram, the data were first bandpass filtered using a 4th-order Butterworth filter with cut-off frequencies of 8 and 30 kHz (Gruden et al, 2021). To construct a whistle-based cross-correlogram, the data were first processed with a three-stage click removal process which included a 4th-order Butterworth bandpass filter with cut-off frequencies of 2.5 and 12 kHz (Gruden et al, 2021). Crosscorrelograms in both cases were then computed based on the generalized cross-correlation smoothed coherence transform (GCC-SCOT) with a 1 s long sliding window and 50% overlap (Gruden et al, 2021).…”

“…To construct a whistle-based cross-correlogram, the data were first processed with a three-stage click removal process which included a 4th-order Butterworth bandpass filter with cut-off frequencies of 2.5 and 12 kHz (Gruden et al, 2021). Crosscorrelograms in both cases were then computed based on the generalized cross-correlation smoothed coherence transform (GCC-SCOT) with a 1 s long sliding window and 50% overlap (Gruden et al, 2021). Cross-correlograms were normalized and measurements were extracted by applying a threshold of 3.7 (Gruden et al, 2021).…”