Passive Acoustic Localization Research Articles

Deep Learning-Based Low-Frequency Passive Acoustic Source Localization

This paper develops benchmark cases for low- and very-low-frequency passive acoustic source localization (ASL) using synthetic data. These cases can be potentially applied to the detection of turbulence-generated low-frequency acoustic emissions in the atmosphere. A deep learning approach is used as an alternative to conventional beamforming, which performs poorly under these conditions. The cases, which include two- and three-dimensional ASL, use a shallow and inexpensive convolutional neural network (CNN) with an appropriate input feature to optimize the source localization. CNNs are trained on a limited dataset to highlight the computational tractability and viability of the low-frequency ASL approach. Despite the modest training sets and computational expense, detection accuracies of at least 80% and far superior performance compared with beamforming are achieved—a result that can be improved with more data, training, and deeper networks. These benchmark cases offer well-defined and repeatable representative problems for comparison and further development of deep learning-based low-frequency ASL.

Where's Whaledo: A software toolkit for array localization of animal vocalizations.

Where's Whaledo is a software toolkit that uses a combination of automated processes and user interfaces to greatly accelerate the process of reconstructing animal tracks from arrays of passive acoustic recording devices. Passive acoustic localization is a non-invasive yet powerful way to contribute to species conservation. By tracking animals through their acoustic signals, important information on diving patterns, movement behavior, habitat use, and feeding dynamics can be obtained. This method is useful for helping to understand habitat use, observe behavioral responses to noise, and develop potential mitigation strategies. Animal tracking using passive acoustic localization requires an acoustic array to detect signals of interest, associate detections on various receivers, and estimate the most likely source location by using the time difference of arrival (TDOA) of sounds on multiple receivers. Where's Whaledo combines data from two small-aperture volumetric arrays and a variable number of individual receivers. In a case study conducted in the Tanner Basin off Southern California, we demonstrate the effectiveness of Where's Whaledo in localizing groups of Ziphius cavirostris. We reconstruct the tracks of six individual animals vocalizing concurrently and identify Ziphius cavirostris tracks despite being obscured by a large pod of vocalizing dolphins.

Passive acoustic localization using dictionary learning

Ray-trace simulations are used to demonstrate the use of Dictionary Learning to improve passive localization of a source monitored with a vertical linear acoustic array. The learned dictionary, generated using historical sound velocity profile (SVP) data from a region of interest, is an over complete, sparse representation of the SVP training set. By minimizing an objective function that measures the differences between multipath reception intervals detected at a receiver for propagation through candidate and baseline SVP profiles, we show that an optimal SVP match to the “unknown” baseline can be reconstructed by an efficient dictionary search. Ray traces back-propagated through the reconstructed SVP according to beamformed receive angles computed with the baseline SVP are then found to well estimate the source position as the centroid of a cluster of multipath signal intersections. The accuracy for small unit-sparsity dictionaries of size up to 50 was evaluated on a randomly sampled, 30 SVP testing set obtained from an area close to that of the training set, demonstrating mean location errors less than 5% in both distance and depth. Five representative profiles spanning the range of observed sound speed variations were used to assess localization performance at source depths from 50 to 450 m and at source ranges from 2000 to 4500 m. Compared to using the average sound speed profile to estimate source position, using the learned dictionary produced a general performance increase in position accuracy.

A Track-before-Detect Strategy for Multi-sensor Passive Acoustic Localization

This paper focuses on the multi-sensor passive acoustic localization problem. The traditional solution for the problem is to estimate the parameter of the received signal, such as the time difference of arrival (TDOA). Then the position of the target is solved by the estimated parameter. This solution is suboptimal because estimations are carried out at each sensor separately, and the fact that the received signals of different sensors originate from the same target is ignored. In this paper, we propose a track-before-detect (TBD) method. The proposed method adopts a particle filter that defines its likelihood function as the product of the output of the cross-correlation (CC) between the signal of different sensors conditioned on the particle’s state. This likelihood function is designed to ensure that the processing gain of multi-sensors can be fully obtained. Moreover, the proposed method circumvents the challenging measurement-to-track association problem faced by the method based on the estimated parameter.

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

Understanding the vocal behavior of cetaceans is an important component of many passive acoustic applications. This study quantifies the vocal behavior of acoustic subgroups of false killer whales (Pseudorca crassidens) from the Hawaiian Archipelago. The acoustic subgroups (N = 523) exhibit diverse vocal behavior that varies between encounters. Overall, 29% of acoustic subgroups only echolocate, 16% only whistle, and 55% emit both types of vocalizations. These results contribute important information for developing automated passive acoustic cetacean tracking, localization, and classification techniques, and thus, support future cetacean monitoring and assessment efforts.

Passive acoustic localization and tracking of Rice’s whales (Balaenoptera ricei) in the northeastern Gulf of Mexico

The endangered Rice’s whale (Balaenoptera ricei) is endemic to the Gulf of Mexico and is also the Gulf’s only resident baleen whale. Limited knowledge of this species’ ecology combined with the high industrialization of this region raises serious conservation concerns. As part of an ongoing project to study the Rice’s whale in its core habitat, an array of moored passive acoustic monitoring sites has been near-continuously deployed in the northeastern Gulf of Mexico since May 2021. While detecting Rice’s whale calls in these data provides valuable insight into the species’ spatiotemporal distribution, further analyses to localize and track individual whales would allow better characterization of their acoustic behavior and movement patterns, possibly allowing for density estimation. Here, we present a method for automated passive acoustic localization and tracking of calling Rice’s whales. The two key components of this approach are (1) the use of opportunistic sound sources to time-synchronize recordings across sites given clock-drift between acoustic recorders and (2) the implementation of automatic multi-target tracking techniques based on the Gaussian mixture probability hypothesis density filter. Analyses of the first four months of data show potential for measuring basic behavioral parameters such as calling rate, source level, and average swim speed.

The Queue’s Automated Creation of Doctor’s Calls by Patients in the Hospital with Visualization via the Mobile Application

The spread of the COVID-19 virus is challenging society to provide medical care to a growing number of patients in the hospital. It is important to determine the patient from whom an urgent appeal was received earlier and to automate the creation of the calls’ queue. Determining the direction of the sound source, which is the patient’s voice, can be realized using the passive acoustic location method. In this case, it is necessary to place sound sensors in the wards with patients. In such a case, these sensors it is expedient to build in the lighting equipment executed in the shape of Platonic polyhedra. The microcontroller system, located inside such a spatial structure, ensures the transmission of sound to a server computer system. The above system alternately records the receipt of urgent appeals, analyzes the location of the sound source, and sends the relevant data to the doctor’s smartphone. The mobile application visualizes information about the location of the patients who need consultation or help. The proposed solution for intelligent analysis of voice appeals by inpatients may also be useful for post-stroke, post-infarction, and other bedridden patients who are unable to call medical staff otherwise than a voice.

Underwater target passive acoustic localization method based on Hanbury Brown–Twiss interference

PurposeAcoustic signals of the underwater targets are susceptible to noise, reverberation, submarine topography and biology, therefore it is difficult to precisely locate underwater targets. This paper proposes a new underwater Hanbury Brown-Twiss (HBT) interference passive localization method. This study aims to achieve precise location of the underwater acoustic targets.Design/methodology/approachThe principle of HBT interference with ultrasensitive detection characteristics in optical measurements was introduced in the field of hydroacoustics. The coherence of the underwater target signal was analyzed using the HBT interference measurement principle, and the corresponding relationship between the signal coherence and target position was obtained. Consequently, an HBT interference localization model was established, and its validity was verified through simulations and experiments.FindingsThe effects of different array structures on the localization performance were obtained by simulation analysis, and the simulations confirmed that the HBT method exhibited a higher positioning accuracy than conventional beamforming. In addition, the experimental analysis demonstrated the excellent positioning performance of the HBT method, which verified the feasibility of the proposed method.Originality/valueThis study provides a new method for the passive localization of underwater targets, which may be widely used in the field of oceanic explorations.

Recent Advances in Passive Acoustic Localization Methods via Aircraft and Wake Vortex Aeroacoustics

Passive acoustic aircraft and wake localization methods rely on the noise emission from aircraft and their wakes for detection, tracking, and characterization. This paper takes a holistic approach to passive acoustic methods and first presents a systematic bibliographic review of aeroacoustic noise of aircraft and drones, followed by a summary of sound generation of wing tip vortices. The propagation of the sound through the atmosphere is then summarized. Passive acoustic localization techniques utilize an array of microphones along with the known character of the aeroacoustic noise source to determine the characteristics of the aircraft or its wake. This paper summarizes the current state of knowledge of acoustic localization with an emphasis on beamforming and machine learning techniques. This review brings together the fields of aeroacoustics and acoustic-based detection the advance the passive acoustic localization techniques in aerospace.

Grouper source levels and aggregation dynamics inferred from passive acoustic localization at a multispecies spawning site.

Four species of grouper (family Epinephlidae), Red Hind (Epinephelus guttatus), Nassau (Epinephelus striatus), Black (Mycteroperca bonaci), and Yellowfin Grouper (Mycteroperca venenosa) share an aggregation site in Little Cayman, Cayman Islands and produce sounds while aggregating. Continuous observation of these aggregations is challenging because traditional diver or ship-based methods are limited in time and space. Passive acoustic localization can overcome this challenge for sound-producing species, allowing observations over long durations and at fine spatial scales. A hydrophone array was deployed in February 2017 over a 9-day period that included Nassau Grouper spawning. Passive acoustic localization was used to find positions of the grouper-produced calls recorded during this time, which enabled the measurement of call source levels and evaluation of spatiotemporal aspects of calling. Yellowfin Grouper had the lowest mean peak-to-peak (PP) call source level, and Nassau Grouper had the highest mean PP call source level (143.7 and 155.2 dB re: 1 μPa at 1 m for 70-170 Hz, respectively). During the days that Nassau Grouper spawned, calling peaked after sunset. Similarly, when Red Hind calls were abundant, calls were highest in the afternoon and evening. The measured source levels can be used to estimate communication and detection ranges and implement passive acoustic density estimation for these fishes.

Probabilistic focalization for shallow water localization.

Localizing and tracking an underwater acoustic source is a key task for maritime situational awareness. This paper presents a sequential Bayesian estimation method for passive acoustic source localization in shallow water. The proposed probabilistic focalization approach associates detected directions of arrival (DOAs) to modeled DOAs and jointly estimates the time-varying source location. Embedded ray tracing makes it possible to incorporate environmental parameters that characterize the acoustic waveguide. Due to its statistical model, the proposed method can provide robustness in scenarios with severe environmental uncertainty. We demonstrate performance advantages compared to matched field processing using data collected during the SWellEx-96 experiment.

We Hear Your PACE

Indoor localization is crucial to enable context-aware applications, but existing solutions mostly require a user to carry a device, so as to actively sense location-discriminating signals. However, many applications do not prefer user involvement due to, e.g., the cumbersome of carrying a device. Therefore, solutions that track user locations passively can be desirable, yet lack of active user involvement has made passive indoor localization very challenging even for a single person. To this end, we propose Passive Acoustic loCalization of multiple walking pErsons (PACE) as a solution for small-scale indoor scenarios: it passively locates users by pinpointing the positions of their footsteps. In particular, PACE leverages both structure-borne and air-borne footstep impact sounds (FIS); it uses structure-borne FIS for range estimations exploiting their acoustic dispersion nature, and it employs air-borne FIS for Angle-of-Arrival (AoA) estimations and person identifications. To combat the low-SNR nature of FIS, PACE innovatively employs domain adversarial adaptation and spectral weighting to ranging/identification and AoA estimations, respectively. We implement a PACE prototype and extensively evaluate its performance in representative environments. The results demonstrate a promising sub-meter localization accuracy with a median error of 30 cm.

Research on Positioning Accuracy of Passive Acoustic Positioning System Based on Feature Matching in Air

Passive acoustic localization is an important interdisciplinary research branch which combines acoustics, communication and signal processing. Passive acoustic positioning system, as an important part of battlefield sensing and monitoring system, has been paid more and more attention. Due to the disturbance of battlefield and other environments and the existence of measurement errors, the positioning accuracy often cannot meet the requirements. Passive acoustic positioning system, as an important part of battlefield sensing and monitoring system, has been paid more and more attention. Due to the disturbance of battlefield and other environments and the existence of measurement errors, the positioning accuracy often cannot meet the requirements. In multi-sensor passive location, the measured data of each array element is not only related, but also compensatory and redundant, so some people use the optimal data fusion algorithm as post-processing algorithm to improve the positioning accuracy. In this paper, the positioning accuracy of ground passive acoustic positioning system is analyzed based on feature matching method, and some useful conclusions are drawn.

Grouper source levels and aggregation dynamics inferred from passive acoustic localization at a multispecies spawning site

Red hind ( Epinephelus guttatus) and Nassau (E. striatus), black (Mycteroperca bonaci), and yellowfin grouper (M. venenosa) form spawning aggregations in Little Cayman, Cayman Islands and produce sound during these aggregations. Continuous observation of these aggregations is challenging because traditional methods are limited in time and space. Passive acoustic localization can overcome some of these challenges in case of sound-producing species, allowing observations over long durations and fine spatial scales. A hydrophone array was deployed in February 2017 over a nine-day period that included Nassau grouper spawning. Hyperbolic localization was used to find positions of the grouper producing calls during this time, to measure call source levels, and evaluate spatiotemporal aspects of calling. During the days Nassau grouper spawned, calling peaked after sunset from 19:00 to 21:00. Similarly, when red hind calls were most abundant, they peaked from 16:00 to 21:00. The mean peak-to-peak source levels of calls was 143.7 dB for yellowfin grouper and 154.9 dB for Nassau grouper (re: 1 μPa at 1 m for 70 to 170 Hz). The measured source levels can be used to determine communication and detection ranges and develop passive acoustic density estimation methods for these fishes.

Time difference intersection characteristics of passiveunderwater acoustic localization in the stratified deep sea

Passive underwater acoustic localization by time difference of arrival(TDOA) has been successfully applied in determining the entering target position in shallow sea, but when it comes to deep sea, the large test sea area, curved ray paths and limited measuring conditions increase the uncertainty on localization performance. According to this problem, a TDOA localization model using multiple base stations was presented based on the assumption that the medium in deep sea was stratified, by which differences between the constant velocity medium and stratified medium in solving the target position was compared, and effects of the key variable, equivalent sound velocity (ESV), on time difference intersection were discussed by theoretical and numerical method. The variability of TDOA intersection under inhomogeneous ESV conditions in deep sea was revealed. The TDOA contour tended to be standard hyperbolic curves when the ESVs from two different stations were close, and it showed an irregular distribution when the ESVs from two base stations had an obvious bias, which tended to be close on the one side and parallel to the another one. A worse localization performance appeared as all TDOA contours were on the same side with close curves. The method was examined by the simulation case in a summer environment of Northwest Pacific, in which the test sea area was set to 8 km ×8 km in coverage and 5500 m in depth. A 4-receivers array in the squared configuration was used, and the target located at x =1 km, z =3 km. According to the simulated results, the localization performance related to receiver depth: (1) When the receiver depth was 1000 m, the favorable intersection situation was formed by the mixed acoustic ray paths composed of the direct wave and the seabed first-reflected wave, such that the relative high accuracy was achieved, the root mean square error (RMSE) was 30.4 m with typical errors randomly added; (2) when the receiver depth was 100 m, the terrible intersection situation was formed by nearly tangent and close quadratic curves which was formed by a single kind of seabed first-reflected ray paths, and the RMSE was nearly double compared with the one under 1000 m receiver depth condition; (3) when the receiver depth was 5450 m, a better intersection situation was achieved due to full coverage of direct wave, and the relative accuracy without random errors was superior to that under 1000 m receiver depth condition. This work indicates that intersecting localization by TDOA is evidently limited by sound channel in the stratified deep sea, and multipaths make the ESV different as arriving at each base station, such that intersection characteristics of the TDOA contour are changed. So the intersection condition of TDOA should be carefully considered in designing the measurement system and the array configuration and deployed receiver depth should be reasonably arranged to adapt the specific oceanographic environment.

Comparing passive localization methods for ocean vehicles from their underwater sound received on a coherent hydrophone array

Simultaneous localization and tracking of multiple ocean vehicles over instantaneous continental-shelf scale regions using the passive ocean acoustic waveguide remote sensing (POAWRS) technique employs a large-aperture densely-sampled coherent hydrophone array system to monitor the underwater sounds radiated by ocean vehicles. Here, particle filtering is implemented for bearings-only localization and tracking of surface ships where the estimated bearing-time trajectories of ship-radiated underwater sound detections are used as inputs to provide two-dimensional horizontal position estimates. Coherent beamforming of the acoustic data received on the hydrophone array not only provides estimates of ship bearing, but also significantly enhances the signal-to-noise ratio. Results of passive acoustic surface ship localization and tracking from recordings in the Gulf of Maine and the Norwegian Sea are presented. The particle filtering approach for passive source localization are compared with three other approaches: moving array triangulation (MAT), array invariant (AI), and modified-polar-coordinates extended Kalman filter (MPC-EKF). The passive source localization accuracies, determined by comparison with the GPS-measured position of the surface ships, are dependent on numerous factors such as source-receiver geometry, range, and relative speed. By combining several of these approaches, which have respective pros and cons under different circumstances, the surface ships can be localized with improved accuracy.

Localizing individual soniferous fish using passive acoustic monitoring

Identifying where fish inhabit is a fundamentally important topic in ecology and management allowing acoustically sensitive times and areas to be prioritized. Passive acoustic localization has the benefit of being a non-invasive and non-destructive observational tool, and provides unbiased data on the position and movement of aquatic animals. This study used the time difference of arrivals (TDOA) of sound recordings on a four-hydrophone array to pinpoint the location of male oyster toadfish, Opsanus tau, a cryptic fish that produces boatwhistles to attract females. Coupling the TDOA method with cross correlation of the different boatwhistles, individual toadfish were mapped during dawn (0523–0823), midday (1123–1423), dusk (1723–2023) and night (2323−0223) to examine the relationship between temporal and spatial trends. Seven individual males were identified within 0.5–24.2 m of the hydrophone array and 0.0–18.2 m of the other individuals. Uncertainty in passive acoustics localization was investigated using computer simulations as <2.0 m within a bearing of 033 to 148° of the linear hydrophone array. Passive acoustic monitoring is presented as a viable tool for monitoring the positions of soniferous species, like the oyster toadfish. The method used in this study could be applied to a variety of soniferous fishes, without disturbing them or their environment. Understanding the location of fishes can be linked to temporal and environmental parameters to investigate ecological trends, as well as to vessel activity to discuss how individuals' respond to anthropogenic noise.

Performance Analysis for Focused Beamformers in Passive Underwater Acoustic Localization

Focused beamformers are widely used in passive underwater acoustic localization. Many pseudo-peaks (spurious peaks not corresponding to a real source) are produced by focused beamformers, impacting the performance and reliability of related algorithms. After describing both the received signal model of near-field sources with a uniform line array, and the basic principle of passive localization based on focused beamformers, we define pseudo-peaks in the focused beamformer’s spectra and give the procedure to quantify these pseudo-peaks. The performance of conventional focused beamformers (C-FBs) and minimum variance distortionless response focused beamformers (MVDR-FBs) are studied, focusing on the number of pseudo-peaks in passive underwater acoustic localization. After establishing a simulation model based on an underwater acoustic environment, the algorithms are compared in terms of signal-to-noise ratio, frequency, distance of the source signal, and detection threshold. In all cases, the MVDR-FB method shows superior performance to the C-FB method in minimizing the number of pseudo-peaks. Finally, a method of selecting the detection threshold based on the convergence of pseudo-peak number is proposed.

Error Analysis of Sound Source Localization System for Small Microphone Based on Time Delay Estimation

Based on time delay,Sound source localization method has a smaller amount of calculation compared with the sound source localization method of controllable beam synthesizer and high resolution spectral estimation, and can achieve a higher positioning accuracy after improved, so it is the most suitable method for real-time positioning, widely used in the field of passive acoustic localization. This paper tries to spread the factors that affect the positioning accuracy of time delay method, and provides the direction for improving the effect of time delay sound source localization.

Passive acoustic localization and tracking using synthetic data for the Northern Gulf of Mexico

Passive acoustic localization and tracking of marine mammals shows potential for future acoustic monitoring efforts. The Littoral Acoustic Demonstration Center—Gulf Ecological Monitoring and Modeling (LADC-GEMM) project collected underwater acoustic data in the northern Gulf of Mexico during the summer of 2015 using Environmental Acoustic Recording Systems (EARS) buoys, returning to sites previously surveyed by LADC. The localization and tracking method developed for the EARS hydrophones uses Monte-Carlo based simulations based on this data. Synthetic whale clicks at random times along a sinusoidal path were propagated to the EARS receivers and the location of the synthetic source was tracked. Several whale clicks in a series determine the most probable path of the animal. The localization/tracking method and relative localization are adaptable for variables such as site location, mooring locations, number of moorings, depth, etc. Localization of two synthetic moving whales in three-dimensional space will be demonstrated. [This research was made possible by a grant from The Gulf of Mexico Research Initiative. Data are publicly available through the Gulf of Mexico Research Initiative Information &amp; Data Cooperative (GRIIDC) at https://data.gulfresearchinitiative.org.]