Passive Source Localization Research Articles

Optimal Passive Source Localization for Acoustic Emissions.

Acoustic emission is a non-destructive testing method where sensors monitor an area of a structure to detect and localize passive sources of elastic waves such as expanding cracks. Passive source localization methods based on times of arrival (TOAs) use TOAs estimated from the noisy signals received by the sensors to estimate the source position. In this work, we derive the probability distribution of TOAs assuming they were obtained by the fixed threshold technique—a popular low-complexity TOA estimation technique—and show that, if the sampling rate is high enough, TOAs can be approximated by a random variable distributed according to a mixture of Gaussian distributions, which reduces to a Gaussian in the low noise regime. The optimal source position estimator is derived assuming the parameters of the mixture are known, in which case its MSE matches the Cramér–Rao lower bound, and an algorithm to estimate the mixture parameters from noisy signals is presented. We also show that the fixed threshold technique produces biased time differences of arrival (TDOAs) and propose a modification of this method to remove the bias. The proposed source position estimator is validated in simulation using biased and unbiased TDOAs, performing better than other TOA-based passive source localization methods in most scenarios.

Fusing Measurements from Wi-Fi Emission-Based and Passive Radar Sensors for Short-Range Surveillance

In this work, we consider the joint use of different passive sensors for the localization and tracking of human targets and small drones at short ranges, based on the parasitic exploitation of Wi-Fi signals. Two different sensors are considered in this paper: (i) Passive Bistatic Radar (PBR) that exploits the Wi-Fi Access Point (AP) as an illuminator of opportunity to perform uncooperative target detection and localization and (ii) Passive Source Location (PSL) that uses radio frequency (RF) transmissions from the target to passively localize it, assuming that it is equipped with Wi-Fi devices. First, we show that these techniques have complementary characteristics with respect to the considered surveillance applications that typically include targets with highly variable motion parameters. Therefore, an appropriate sensor fusion strategy is proposed, based on a modified version of the Interacting Multiple Model (IMM) tracking algorithm, in order to benefit from the information diversity provided by the two sensors. The performance of the proposed strategy is evaluated against both simulated and experimental data and compared to the performance of the single sensors. The results confirm that the joint exploitation of the considered sensors based on the proposed strategy largely improves the positioning accuracy, target motion recognition capability and continuity in target tracking.

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.

Robust long-range source localization in the deep ocean using phase-only matched autoproduct processing.

Passive source localization in the deep ocean using array signal processing techniques is possible using an algorithm similar to matched field processing (MFP) that interrogates a measured frequency-difference autoproduct instead of a measured pressure field [Geroski and Dowling, J. Acoust. Soc. Am. 146, 4727-4739 (2019)]. These results are extended herein to a new MFP-style algorithm, phase-only matched autoproduct processing, that is more robust at source-array ranges as large as 225 km. This new algorithm is herein described and compared to three existing approaches. The performance of all four techniques is evaluated using measured ocean propagation data from the PhilSea10 experiment. These data nominally span a 12-month period; include six source-array ranges from 129 to 450 km; and involve signals with center frequencies between 172.5 and 275 Hz, and bandwidths of 60 to 100 Hz. In all cases, weight vectors are calculated assuming a range-independent environment using a single sound-speed profile measured near the receiving array. The frequency-differencing techniques considered here are capable of localizing all six sources, with varying levels of consistency, using single-digit-Hz difference frequencies. At source-array ranges up to and including 225 km, the new algorithm requires fewer signal samples for success and is more robust to the choice of difference frequencies.

Passive Source Localization Using Acoustic Intensity in Multipath-Dominant Shallow-Water Waveguide.

The array invariant technique has been recently proposed for passive source localization in the ocean. It has successfully estimated the source–receiver horizontal range in multipath-dominant shallow-water waveguides. However, it requires a relatively large-scale hydrophone array. This study proposes an array invariant method that uses acoustic intensity, which is a vector quantity that has the same direction as the sound wave propagating through a water medium. This method can be used to estimate not only the source–receiver horizontal range, but also the azimuth to an acoustic source. The feasibility of using a vector quantity for the array invariant method is examined through a simulation and an acoustic experiment in which particle velocity signals are obtained using a finite difference approximation of the pressure signals at two adjacent points. The source localization results estimated using acoustic intensity are compared with those obtained from beamforming of the acoustic signals acquired by the vertical line array.

Sparsity-promoting least-squares interferometric migration for high-resolution passive source location

Seismic source localization plays an important role in estimating the hypocenter of earthquakes and in monitoring hydraulic fracturing. Interferometric crosscorrelation migration (ICCM) is one of the most important waveform-based source location methods that produces a source image from interstation crosscorrelations without manual picking. However, the ICCM method suffers from blurring of the source image, especially for cases in which the acquisition aperture is limited, receivers are sparsely distributed, and the data signal-to-noise ratio is too low. As a result, it typically cannot resolve sources in close proximity to each other, whereas distinguishing them might be a desired feature when we want to follow the fracture evolution in space and time. One possible solution is by fitting a source distribution function to the observed interstation crosscorrelations by least-squares minimization, where the source distribution function is a source power-spectral density (PSD) defined as a function of frequency and spatial location. However, this least-squares interferometric crosscorrelation migration (LS-ICCM) approach provides limited resolution improvement compared to the ICCM method. To further improve the image resolution, we have adopted a sparsity-promoting interferometric crosscorrelation migration (SP-ICCM) method that negates the effects of unfocused sources during the inversion. This sparsity constraint is imposed on the source PSD in space to mitigate the loss of resolution that arises as a consequence of incomplete information, including limited aperture and discretization. The inverse problem involving this sparsity constraint can be solved relatively quickly with an iteratively reweighted least-squares algorithm. Using synthetic and field data tests, we demonstrate that our method is robust to noise and effective for closely spaced sources in complex geologic settings.

Acoustic source localization with the angular spectrum approach in continuously stratified media.

The angular spectrum approach (ASA)-a frequency domain method to calculate the acoustic field-enables highly efficient passive source localization and modeling forward propagation in homogeneous media. If the medium is continuously stratified, a first-order analytical solution may be obtained for the field at arbitrary depth. Simulations show that the proposed stratified ASA solution enables accurate source localization as compared to the uncorrected ASA (error from 1.2 ± 0.3 to 0.49 ± 0.3 wavelengths) at scalings relevant to biomedical, underwater, and atmospheric acoustic applications, and requiring milliseconds on nonspecialized hardware. The results suggest the proposed correction enables efficient and accurate localization in stratified environments.

Dimensionality reduction of cross-spectral density matrices using diffusion map projections

Cross-spectral density matrices (CSDMs) constructed for matched-field source localization in an ocean waveguide are both Hermitian and positive semi-definite, allowing them to be interpreted in a non-Euclidean geometric framework as “points” in a Riemannian manifold. This abstract matrix representation can be used to construct distance metrics on the manifold, and distances between pairs of such matrices provide a measure of similarity between the true source CSDM and various replica CSDMs. With this geometric interpretation, the shortest “distance” between pairs of source/replica CSDMs represents an estimate of the source location obtained from acoustic field amplitude and phase acquired on a vertical array [Finette and Mignerey, JASA 143 (2018)]. In this presentation, visualizations of CSDM manifolds obtained from simulated acoustic fields propagating in an ocean waveguide with internal wave-induced variability are illustrated to gain insight into this approach to passive source localization. Since the original manifold resides in a high-dimensional space determined by the number of sensors, manifold learning using diffusion maps is employed to reduce the dimensionality but constrained to retain the relative distance relationships among the CSDMs. The original manifold is projected down to three dimensions for visualization. [Work supported by the Office of Naval Research.]

Source localisation using TDOA and FDOA measurements under unknown noise power knowledge

In this study, the authors develop a new algorithm for passive source localisation using time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA) measurements. In this algorithm, the position and velocity of the target source are successively estimated by formulating two alone semidefinite programming problems, where the variances of the TDOA and FDOA measurement noises can be separated and neglected. Unlike the traditional methods, the authors’ approach does not require the prior knowledge of the TDOA/FDOA measurement noise power. Simulation results indicate that the proposed algorithm outperforms the existing methods under unknown noise power knowledge.

Passive Radar Source Localization Based on Cuckoo Search NVTDOA

In order to meet the requirement of passive radar source localization in electronic warfare, the concept of the virtual time differences of arrival (VTDOA) is proposed by taking advantage of the characteristics of the same UAV in different positions at different times and the periodic rotation of radar pulse signal. The VTDOAs are the passive localization information defined as the time differences of the radar pulse transmission from the radar position to different virtual receivers. Firstly, a nonlinear VTDOA (NVTDOA) localization model is constructed. Moreover, sufficient conditions for accurately calculating the periodic integers in the model are analyzed, and the observability conditions of the localization model determined are deduced. Secondly, the convergence solution of the NVTDOA localization equation is obtained by Cuckoo search algorithm; thus, passive radar source localization is realized. Finally, the performance of the proposed method is verified by comparing with the existing methods.

Peak Extraction Passive Source Localization Using a Single Hydrophone in Shallow Water

In this work, we use the acoustic ray model to study passive source localization problems using a single hydrophone for shallow water waveguides. It has been found that conventional matched field processing, which requires a multi-hydrophone array, is no longer effective. In this paper, we introduce matched feature localization to achieve passive source localization using a single hydrophone. The eigenray that contains the arrival structure is the core of the acoustic ray model; however, it is difficult to estimate the relative arrival delay of the eigenray for passive localization, especially in shallow water. Therefore, we propose a novel peak extraction passive source localization method based on the autocorrelation function, which does not require estimation of the relative arrival delay of the eigenray. According to a computer simulation, the proposed method can achieve good performance using only one hydrophone. Further, its performance is validated using wideband data collected on a vertical line array during the Shallow Water 2006 experiment.

A fusion algorithm of passive sound source localization based on the two-plane four-element cross array

In this work, a fusion algorithm is proposed for improving the accuracy and stability of passive sound source localization. Different from the traditional algorithm that contains a single-plane cross array, here, the fusion algorithm is used to overcome the position blur in the process of localization. First, the two-plane four-element cross array model is established. Based on this model, the method is defined to judge the position where the sound source is located. According to the localization principle, we derive the calculation formula of the sound source position, based on a single four-element planar array. Then, the elevation angle sine value is introduced into the coordinate formula as the weighted coefficient by analyzing the indirect measurement error, and the fusion algorithm is employed to conduct the sound source localization, based on the two-plane four-element cross array. Finally, the relationships are obtained, between the time delay estimation error, the elevation angle, the horizontal angle, and the localization performance. Besides, the validity of this algorithm is validated by measuring the ranging and direction-finding accuracy. The results show that the distance error rate is within 2%, and the angle error rate is within 3%, which means a good localization effect. The proposed algorithm is expected to be widely used in thunderstorm cloud detection for its quick measurement and high precision.

Passive Intermodulation Source Localization Based on Emission Source Microscopy

Emission source microscopy (ESM) technique can be used for localization and characterization of electromagnetic interference sources by measuring the magnitude and phase of the electromagnetic field in the far-field zone. This article presents a method which uses ESM to locate sources of passive intermodulation (PIM). Compared to the traditional methods of PIM source localization techniques including mechanical manipulation of potential sources and near-field scanning, the proposed ESM-based approach does not require access to the devices under test allowing to detect the PIM sources at relatively large distances. Moreover, the influence of background PIM generated by surrounding environment can be reduced by ESM focusing. The high-quality maps of reconstructed PIM sources can be obtained by using ESM. The feasibility of manual sparse ESM scanning, which is more practical in realistic settings is also demonstrated.

Free-Field TDOA-AOA Sound Source Localization Using Three Soundfield Microphones

Accurate source localization is an important problem in many research areas as well as practical applications in wireless communications and acoustic signal processing. This paper presents a passive three-dimensional sound source localization (SSL) method that employs a geometric configuration of three soundfield microphones. Two methods for estimating the angle of arrival (AOA) and time difference of arrival (TDOA) are proposed based on Ambisonics A and B format signals. The closed-form solution for sound source location estimation based on two TDOAs and three AOAs is derived. The proposed method is evaluated by simulations and physical experiments in our anechoic chamber. Simulations demonstrate that the estimation method can theoretically obtain Cramer-Rao lower bound for a small Gaussian noise present in AOA and TDOA observations. Investigation on the uncertainty of TDOA and AOA measurements depending on the length of measurement interval is also conducted. Experimental results in terms of RMSE indicate that the proposed solution can be used to accurately find a 3D position of the sound source in free-field environment. Performance evaluation regarding the number of estimation steps shows that higher accuracy can be achieved by longer observations of stationary sound source.

Combined carrier phase and code phase passive radiation source localisation method

In this study, the high-precision positioning method of a radiation source is improved by jointing the time difference of arrival (TDOA) and carrier phase of arrival methods. Considering that the observation accuracy of a carrier phase is higher than of the existing observations, this study proposes a carrier phase-based positioning method for radiation source of continuous signals, jointly using the information on carrier phase and code phase. In the positioning process, an extended Kalman filter is used for position tracking in a dynamic model. The numerical experiments show that the proposed method has more than ten times higher accuracy than the TDOA–FDOA (FDOA denotes the frequency-difference of arrival) method at the code period of <10−4 s. Moreover, the proposed method is 103 times more accurate than the TDOA method. Therefore, the positioning accuracy of this method is better than of the traditional positioning method.

Source localization by matching sound intensity with a vertical array in the deep ocean.

The characteristics of the reliable acoustic path (RAP) have been widely used to estimate source depth in the deep ocean. Here, a passive broadband source localization method that estimates both depth and range using the interference of the broadband signal with a non-synchronous vertical array in the RAP is demonstrated. The acoustic intensity distribution versus frequency and depth, which includes interference characteristics, is calculated from non-synchronous vertical array data and used to locate the source. Simulations and an experiment conducted in the western Pacific show that this method can successfully locate a broadband submerged source in the range of 10-30 km.

Passive Source Localization Using Compressive Sensing.

This paper presents an underwater passive source localization method by forming an underdetermined linear inversion problem. The signal strength on a specified grid is evaluated using sparse reconstruction algorithms by exploiting the spatial sparsity of the source signals. Our strategy leads to a high ratio of measurements to sparsity (RMS), an increase in the peak sharpness with a low side lobe level, and minimization of the dimensionality of the problem due to the formulation of the system equation of the multiple snapshots based on the data correlation matrix. Furthermore, to reduce the computational burden, pre-locating with Bartlett is presented. Our proposed technique can perform close to Bartlet and white noise gain constraint processes in the single-source scenario, but it can give slightly better results while localizing multiple sources. It exhibits the respective characteristics of traditionally used Bartlett and white noise gain constraint methods, such as robustness to environmental/system mismatch and high resolution. Both the simulated and experimental data are processed to demonstrate the effectiveness of the method for underwater source localization.

An algebraic geometry approach to passive source localization

An algebraic geometry approach to passive source localization

Localization of Two Sound Sources Based on Compressed Matched Field Processing with a Short Hydrophone Array in the Deep Ocean

Passive multiple sound source localization is a challenging problem in underwater acoustics, especially for a short hydrophone array in the deep ocean. Several attempts have been made to solve this problem by applying compressive sensing (CS) techniques. In this study, one greedy algorithm in CS theory combined with a spatial filter was developed and applied to a two-source localization scenario in the deep ocean. This method facilitates localization by utilizing the greedy algorithm with a spatial filter at several iterative loops. The simulated and experimental data suggest that the proposed method provides a certain localization performance improvement over the use of the Bartlett processor and the greedy algorithm without a spatial filter. Additionally, the effects on the source localization caused by factors such as the array aperture, number of hydrophones or snapshots, and signal-to-noise ratio (SNR) are demonstrated.

Source Localization in a Deep Ocean Using the Time Difference of Multipath Arrivals

Bottom bounce is a typical sound propagation mode in deep oceans. A receiver moored below the sea surface receives sound waves transmitted from a submerged source through multipath paths. Four of them which traveled by bottom bounce mode play the leading role in source localization. A passive broadband source localization method is proposed herein based on the four arrival paths and using a single receiver. Through autocorrelation of the signal that is mainly composed of four multipath arrivals, six positive relative time diff erences that are dependent on the source range and depth are acquired via combination. The time diff erences are sorted in an ascending order to form a measured vector. A ray-tracing model is used to predict the replica vector. Then, the source is localized by fitting the replica vector to the measured one. An experiment is conducted in the South China Sea. The proposed method is utilized to successfully localize a source at a depth of 47 m below the surface and at a distance of 13.6 km.