Ming Bao

This article presents a deconvolution algorithm for high-resolution acoustic imaging, specifically tailored for small-element acoustic vector-sensor (AVS) arrays. While existing fourth-order cumulant (FOC)-based methods can improve spatial resolution, their localization accuracy remains fundamentally limited by the inadequate spatial sampling of small arrays. To address this limitation, the deconvolution method is developed based on a forward propagation model for sound sources received by AVS arrays. The proposed method is designed to achieve high-precision localization under challenging conditions; specifically, at low signal-to-noise ratios (SNRs) and in the presence of spatial aliasing. The FOC-based vector beamforming is initially performed to obtain the “dirty map,” with the FOC matrix iteratively updated to progressively remove sidelobe components merged in the main lobe. By incorporating coherence information, the proposed cumulant-based vector CLEAN based on spatial source coherence (CLEAN-SC) (CUMVCLEAN- SC) method effectively mitigates spatial resolution degradation caused by the sidelobes of coherent sources in small-element arrays. Meanwhile, the clean source intensity power is estimated based on the main lobe peak, and iterations continue until convergence, ultimately yielding the high-resolution acoustic imaging results. Simulation results demonstrate that the proposed method significantly enhances imaging resolution and localization accuracy compared to traditional algorithms while maintaining robustness in low SNR environments and in the presence of spatial aliasing caused by high frequencies. Furthermore, under minimal planar array configurations, the method exhibits superior robustness over traditional acoustic imaging algorithms. The experimental results in a semi-anechoic chamber further confirm the effectiveness of the proposed algorithm with small-element arrays.

Acoustic vector sensors can be employed to improve low-frequency performance of the sensor array against aperture limitation, due to their configurations that allow for the simultaneous measurements of sound pressure and particle velocities. This paper proposes a tetrahedral vector array composed of four triaxial vector sensors with particular emphasis on the manifold characteristics in comparison with the tetrahedral scalar array of the same aperture and channel number in near-field and far-field. The Cramér-Rao bounds are further derived to analyze the lowest performance boundaries for both the scalar and vector arrays. Numerical simulations are conducted to evaluate the performance of the tetrahedral arrays in terms of the root-meansquare error, mainlobe and dual-source resolution. Compared to the scalar array, the vector array provides narrower mainlobe widths and more accurate localization estimations. Moreover, the vector array is particularly useful in identifying two sources at lower frequencies and smaller azimuth intervals. To support the theoretical and simulation findings, measurements are made in a full anechoic chamber to assess the performance of the tetrahedral arrays for single-source and dual-source localization. Experimental results confirm that the low-frequency performance of the tetrahedral array is greatly enhanced by adopting the vector sensors due to the anticipated advantage of the manifold characteristics.

The acoustic vector-sensor array (AVSA) is in common use for measuring both the particle velocity and the sound pressure. This paper presents a fourth-order cumulant-based acoustic imaging method for high spatial resolution using a small-aperture AVSA. The conventional beamforming (CBF) method is first applied in the vector sound field to form the vector-CBF (VCBF) algorithm, which is subsequently combined with the fourth-order cumulant matrix (FOCM) to lead to the cumulant-based vector-CBF (CUM-VCBF) for high resolution acoustic imaging. The performance of the proposed algorithm is examined by analyzing the Cramér–Rao Bound, the root mean square error and the mainlobe width. Simulation analysis is carried out in the cases of a single point source and two point sources. It is demonstrated that the proposed CUM-VCBF algorithm produces acoustic images with superior resolution and noise suppression compared to the conventional scalar and vector beamforming algorithms. Experimental measurements in a semi-anechoic chamber are further presented to demonstrate the best performance of the proposed CUM-VCBF for acoustic imaging.

A direction-of-arrival (DOA) tracking algorithm based on AVS pseudo-smoothing, referred to as the FOC-M $\delta$ -GLMBF algorithm, is proposed to track coherent acoustic targets. This algorithm adapts the marginalized $\delta$ -generalized labeled multi-Bernoulli (M $\delta$ -GLMB) fast filtering algorithm with the fourth-order cumulants (FOC) pseudo-smoothing. It introduces higher-order cumulants capable of suppressing Gaussian noise, and constructs the cumulant matrices and the likelihood function that can be used for AVS pseudo-smoothing. The processing enhances the signal-to-noise ratio (SNR) by suppressing measurement noise, and can accomplish decoherence when there are coherent targets. Based on the labeled random finite set (RFS), it additionally introduces the index label to distinguish different motion models as hidden states, and achieves better tracking performance through the weighted mixture of multiple models. By using the AVS hybrid signal as the measurement, the algorithm avoids measurement-to-track association maps in the filtering process, to effectively support the tracking problem when targets are close to each other or have intersecting trajectories. In addition, as a joint prediction-and-update strategy, the algorithm performs the hypothesis truncation by the K-shortest path method only once, thereby further compensating for the burden of cumulant calculation. Simulations and field experiments verify the superiority of the proposed tracking algorithm for coherent targets under low SNR.

An acoustic vector sensor can directly detect acoustic particle velocity based on the measured temperature difference between closely spaced heated wires. For the detection of velocity in three dimensions, an integrated three-dimensional (3 D) sensor is desired, but it remains challenging in MEMS (Micro-Electro-Mechanical System) manufacturing. Here, a novel monolithic 3 D acoustic vector sensor is proposed, which is constructed using in-plane distributed wires assembled with acoustically transparent heat sink. The planar MEMS structure of the proposed sensor makes it easy to be fabricated and packaged. This work offers a new method for the design of acoustic vector sensors and other thermal convection-based MEMS sensors.

For direction-of-arrival (DOA) estimation of heterogeneous wideband sources, we propose a new adaptive space-frequency joint processing algorithm. The algorithm is implemented in the sparse Bayesian learning (SBL) DOA framework, which is named as ASF-SBL algorithm in this paper. The traditional SBL-based DOA methods suffer from the problem of erroneous DOA estimation due to the structural mismatch between the invariant prior and the sparse coefficients. To solve this problem, the ASF-SBL algorithm employs a new space-frequency correlation prior model that can be adaptively changed to fit heterogeneous DOA scenarios. Specifically, nine alternative space-frequency structural patterns are constructed to represent the joint space-frequency characteristics of spatial sparse signals. By evaluating the space-frequency correlation of the sparse coefficients updated in each iteration under SBL framework, a suitable pattern is selected from the nine choices to determine the adaptive prior of each coefficient. This adaptive method leads to accurate DOA estimation in different wideband sources scenarios. In addition, we introduce a distributed processing method to extend the ASF-SBL algorithm to two-dimensional DOA estimation. This extension is achieved by decoupling the DOA estimation into two one-dimensional estimations. The decoupling avoids the problems of a huge redundant dictionary and excessive computational complexity caused by the combination of azimuth and elevation angles. Numerical simulations show that the ASF-SBL algorithm is superior to existing algorithms in DOA estimation of heterogeneous sources.

Multitarget tracking (MTT) is an important component of situation-awareness based on the Internet of Things (IoT). Existing algorithms mainly focus on tracking based on conventional measurements, e.g., bearings or ranges. However, measurement parameter estimations are considered in isolation, limiting the accuracy and resolution of MTT, and the related data association is an NP-hard multidimensional assignment problem. In this article, we develop a new one-step MTT algorithm based on a novel dynamic Bayesian network (DBN), i.e., DBNMTT. The new MTT algorithm directly infers target states from the raw measurement data by fusing the array signal model, the signal propagation model, and the motion model. In this new DBNMTT framework, we treat target states and conventional measurements, such as bearings and target energies as hidden random variables. The posterior joint probability optimization problem is translated into the problem of graphical model learning. In this way, we can improve the accuracy and resolution of MTT and convert the NP-hard data association problem to a hidden variable learning problem. For nonconjugate models in the DBNMTT, we develop a novel reparameterized approximation variational inference (ReAVI) approach to solve the learning problem. The ReAVI converts nonconjugate models to conjugate models with new parameters and reuses the mean-field algorithm. The performance of our proposed new MTT method, namely, DBNMTT based on ReAVI (DBNMTT-ReAVI), is analyzed on extensive simulations in challenging scenarios. The simulation results show that the DBNMTT-ReAVI algorithm is superior to conventional measurement-based MTT algorithms in several aspects, including the success probability, convergence, resolution, and accuracy.

The multitarget tracking (MTT) based on distributed fusion methods in an acoustic sensor array network (ASAN) is limited by the performance of measurement parameter estimators, such as the received signal strength (RSS) and the direction of arrival (DOA). For measurement parameters with low accuracy and resolution, the MTT may fail in subsequent steps, e.g., data association, because the loss of upstream information cannot be made up by downstream processing. Thus, we propose a new wideband MTT algorithm based on the dynamic Bayesian network (DBN), which treats the ASAN as an overall extended array, and directly estimates the target states from the raw acoustic data. The DBN fuses the near-field model, the acoustic propagation model. and the motion model. These submodels can be optimized by each other, improving the final estimation. Also, for each subband, target signals and the precision parameters of the sensor noise are treated as hidden random variables. Based on this, the weight of each subband can be automatically adjusted according to the accurate hidden variables. Besides, the optimization problem of the posterior probability is transformed into a graphical model learning problem. Moreover, for nonconjugate models, a novel algorithm based on Laplace approximations (LAs) with Newton’s method (NM) is developed, i.e., DBN-LA-NM, bypassing data association. In addition, the corresponding Cramer–Rao lower bound and convergence conditions are derived. The numerical simulation results show that the proposed algorithm outperforms existing MTTs based on the near-field model in terms of accuracy, convergence, and computational complexity. Field experiments further verify the feasibility of the proposed algorithm.

Low-frequency multi-source direction-of-arrival (DOA) estimation has been challenging for micro-aperture arrays. Deep learning (DL)-based models have been introduced to this problem. Generally, existing DL-based methods formulate DOA estimation as a multi-label multi-classification problem. However, the accuracy of these methods is limited by the number of grids, and the performance is overly dependent on the training data set. In this paper, we propose an off-grid DL-based DOA estimation. The backbone is based on circularly fully convolutional networks (CFCN), trained by the data set labeled by space-frequency pseudo-spectra, and provides on-grid DOA proposals. Then, the regressor is developed to estimate the precise DOAs according to corresponding proposals and features. In this framework, spatial phase features are extracted by the circular convolution calculation. The improvement in spatial resolution is converted to increasing the dimensionality of features by rotating convolutional networks. This model ensures that the DOA estimations at different sub-bands have the same interpretation ability and effectively reduce network model parameters. The simulation and semi-anechoic chamber experiment results show that CFCN-based DOA is superior to existing methods in terms of generalization ability, resolution, and accuracy.

The localization of outdoor acoustic sources has attracted attention in wireless sensor networks. In this paper, the steered response power (SRP) localization of band-pass signal associated with steering time delay uncertainty and coarser spatial grids is considered. We propose a modified SRP-based source localization method for enhancing the localization robustness in outdoor scenarios. In particular, we derive a sufficient condition dependent on the generalized cross-correlation (GCC) waveform function for robust on-grid source localization and show that the SRP function with GCCs satisfying this condition can suppress the disturbances induced by the grid distance and the uncertain steering time delays. Then a GCC refinement procedure for band-pass GCCs is designed, which uses complex wavelet functions in multiple sub-bands to filter the GCCs and averages the envelopes of the filtered GCCs as the equivalent GCC to match the sufficient condition. Simulation results and field experiments demonstrate the excellent performance of the proposed method against the existing SRP-based methods.