Non-line-of-sight sound source localization using matched-field processing

Towards passive non-line-of-sight acoustic localization around corners using uncontrolled random noise sources

Non-line-of-sight (NLoS) imaging is an important challenge in many fields ranging from autonomous vehicles and smart cities to defense applications. Several recent works in optics and acoustics tackle the challenge of imaging targets hidden from view (e.g. placed around a corner) by measuring time-of-flight information using active SONAR/LiDAR techniques, effectively mapping the Green functions (impulse responses) from several controlled sources to an array of detectors. Here, leveraging passive correlations-based imaging techniques (also termed 'acoustic daylight imaging'), we study the possibility of acoustic NLoS target localization around a corner without the use of controlled active sources. We demonstrate localization and tracking of a human subject hidden around a corner in a reverberating room using Green functions retrieved from correlations of broadband uncontrolled noise sources recorded by multiple detectors. Our results demonstrate that for NLoS localization controlled active sources can be replaced by passive detectors as long as a sufficiently broadband noise is present in the scene.

Sound source localization using multiple ad hoc distributed microphone arrays

Sound source localization is crucial for communication and sound scene analysis. This study uses direction-of-arrival estimates of multiple ad hoc distributed microphone arrays to localize sound sources in a room. An affine mapping between the independent array estimates and the source coordinates is derived from a set of calibration points. Experiments show that the affine model is sufficient to locate a source and can be calibrated to physical dimensions. A projection of the local array estimates increases localization accuracy, particularly further away from the calibrated region. Localization tests in three dimensions compare the affine approach to a nonlinear neural network.

Matched Field Processing Based on Bayesian Estimation

In order to improve the robustness and positioning accuracy of the matched field processing (MFP) in underwater acoustic systems, we propose a conditional probability constraint matched field processing (MFP-CPC) algorithm in this paper, which protects the main-lobe and suppresses the side-lobe to the AMFP by the constraint parameters, such as the posterior probability density of source locations obtained by Bayesian criterion under the assumption of white Gaussian noise. Under such constraint, the proposed MFP-CPC algorithm not only has the same merit of a high resolution as AMFP but also improves the robustness. To evaluate the algorithm, the simulated and experimental data in an uncertain shallow ocean environment is used. From the results, MFP-CPC is robust to the moored source, as well as the moving source. In addition, the localization and tracking performances of using the proposed algorithm are consistent with the trajectory of the moving source.

Autoproducts in and near acoustic shadow zones created by barriers

The autoproducts are nonlinear mathematical constructs developed from acoustic fields with non-zero bandwidth. When averaged through the field's bandwidth, the autoproducts may mimic a genuine acoustic field at frequencies that are lower or higher than the original field's bandwidth. The resulting opportunity to extend signal processing to user-selectable below- or above-band frequencies is intriguing for many signal processing algorithms. Based on prior work, the limitations of the autoproducts' mimicry of out-of-band fields are understood when the in-band acoustic field is well-represented by ray acoustics. Thus, the focus in this study is on autoproducts in acoustic shadow zones behind barriers containing only diffracted acoustic fields where a sum of ray-path contributions is not an adequate field description. Diffraction is expected to be a detriment to autoproduct techniques due to its sensitivity to frequency. Two ideal shadow-zone environments with exact analytic Helmholtz-equation solutions are considered: Sommerfeld's half-plane problem, also known as knife-edge diffraction, and Mie scattering from a sphere with ka = 40, where k is the wavenumber and a is the sphere's radius. With the exception of the shadow regions, autoproducts experience only mild degradation in field-mimicry performance when compared to what the ray-based theory would predict.

Stochastic matched-field localization of an acoustic source based on principles of Riemannian geometry

Passive localization of acoustic sources is treated within a geometric framework where non-Euclidean distance measures are computed between a cross-spectral density estimate of received data on a vertical array and a set of stochastic replica steering matrices, rather than traditional replica steering vectors. A processing scheme involving matrix-matrix comparisons where steering matrices, as functions of the replica source coordinates, naturally incorporate environmental variability or uncertainty provides a general framework for considering the acoustic inverse source problem in an ocean waveguide. Within this context a subset of matched-field processors is examined, based on recent advances in the application of non-Euclidean geometry to statistical classification of data feature clusters. The matrices are interpreted abstractly as points in a Riemannian manifold, and an appropriately defined distance measure between pairs of matrices on this manifold defines a matched-field processor for estimating source location. Acoustic simulations are performed for a waveguide comprising both a depth-dependent sound-speed profile perturbed by linear internal gravity waves and a depth-correlated surface noise field, providing an example of the viability of this approach to passive source localization in the presence of sound-speed variability.

Impulse source localization in an urban environment: Time reversal versus time matching

This study investigates two approaches for localizing an impulse sound source with distributed sensors in an urban environment under controlled processing time. In both approaches, the numerical model used for calculating the sound propagation is a finite-difference time-domain (FDTD) model. The simulations are drastically accelerated by restricting to the lower frequencies of the impulse signals and are evaluated against in situ measurements. The first tested localization technique relies on the time reversal of the measurements with the model. In the second technique, the source is localized by matching the observed differences in the first times of arrival of the signals to those obtained from a pre-defined database of simulations with known source positions. The localization performance is physically investigated on the basis of the measurements, considering two source positions and all possible combinations from 5 to 15 microphones. The time matching localization attains an accuracy of 10 m, which is targeted in this study, in the vast majority of the configurations. In comparison, the time reversal localization is affected by the weakness of contributions from sensors masked and distant from the source. Practical requirements are also discussed, such as real-time constraints, hardware and description of the urban environment.