Multi-source Direction Of Arrival Estimation Research Articles

Direction of Arrival Estimation and Phase-Correction for Noncoherent Subarrays: A Convex Optimization Approach

Estimating the direction of arrival (DOA) of sources is an important problem in aerospace and vehicular communication, localization, and radar. In this article, we consider a challenging multisource DOA estimation task, where the receiving antenna array is composed of noncoherent subarrays, i.e., subarrays that observe different unknown phase shifts at every snapshot (e.g., due to waiving the demanding synchronization of local oscillators across the entire array). We formulate this problem as the reconstruction of joint sparse and low-rank matrices and solve the problem’s convex relaxation. To scale the optimization complexity with the number of snapshots better than general-purpose solvers, we design an optimization scheme, based on integrating the alternating direction method of multipliers and the accelerated proximal gradient techniques, which exploits the structure of the problem. While the DOAs can be estimated from the solution of the aforementioned convex problem, we further show how an improvement is obtained if, instead, one estimates from this solution only the subarrays’ phase shifts. This is done using another, computationally light, convex relaxation that is practically tight. Using the estimated phase shifts, “phase-corrected” observations are created and a final plain (“coherent”) DOA estimation method can be applied. Numerical experiments show the performance advantages of the proposed strategies over existing methods.

Multisource DOA Estimation in Impulsive Noise Environments Using Convolutional Neural Networks

This work proposes an effective high-resolution multisource direction-of-arrival (DOA) estimation method in impulsive noise scenarios based on convolutional neural networks (CNNs). First of all, the array observation matrix is preprocessed and fed into a denoising network to suppress outliers and filter out impulsive noise. Secondly, the denoising network output is fed into a model order selection network to estimate the model order. Next, according to the estimation, the denoising network output is fed into a DOA subnetwork corresponding to the model order in a DOA network to estimate the DOA of each signal. Comprehensive simulations demonstrate that, in the presence of impulsive noise, the proposed method is effective and superior in accuracy and computation speed for multisource DOA estimation. Therefore, it is concluded that CNN can be well generalized for DOA estimation.

Multi-Source DoA Estimation of EM Waves Impinging Spherical Antenna Array With Unknown Mutual Coupling Using Relative Signal Pressure Based Multiple Signal Classification Approach

Spherical antenna array (SAA) is a configuration that scans almost all the radiation sphere with constant directivity. It finds applications in spacecraft and satellite communication. Multiple signal classification (MUSIC) is a widely used multiple source direction-of-arrival (DoA) estimation method because of its low complexity implementation in practical applications. Conversely, it is susceptible to noise, which consequently affects its accuracy of localization. In this paper, MUSIC-based methods that operate at low signal-to-noise ratio (SNR) are developed via relative electromagnetic (EM) wave pressure measurements of a SAA. The proposed methods are the relative pressure MUSIC (RP-MUSIC), and in spherical domain (SH-RP-MUSIC). The developed SH-RP-MUSIC algorithm is in spherical domain thereby allows frequency-smoothing approach for the de-correlation of the coherent source signals towards an enhanced accuracy of localization. Both RP-MUSIC and SH-RP-MUSIC algorithms developed have the ability to estimate the number of active sources that is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> knowledge of the conventional MUSIC algorithm. Numerical experiments were used to demonstrate the adequacy of the developed algorithms. In addition, measured data from experiment, which is the practically acceptable way to examine any procedure is employed to demonstrate the merits of the developed algorithms against the conventional MUSIC algorithm and other recent multiple source localization method in literature. Finally, in order to achieve DoA estimations with adequate localization accuracy at low SNR using SAA, SH-RP-MUSIC algorithm is a better choice.

Two-dimensional detection based LRSS point recognition for multi-source DOA estimation

This paper aims to tackle the multiple source localization problem using the signals recorded by a B-format microphone in reverberant environments. The proposed approach analyses the energy and phase information of recorded signals in the time–frequency (T-F) domain to present a two-dimensional detection of low-reverberant-single-source (LRSS) points, where the pressure–velocity energy difference and pressure–velocity phase difference are exploited. The direction of arrival (DOA) estimation of actual sources by utilizing the designed weight kernel density estimation are conducted base on the direction cues derived from the LRSS points. Evaluation on simulated data and data recorded in an actual acoustic chamber demonstrates that a better performance can be obtained through the proposed algorithm in highly reverberant environments.

Efficient FFT based multi source DOA estimation for ULA

An accurate and fast direction-of-arrival (DOA) estimation method for multiple targets in additive white Gaussian noise is presented in this paper, where the maximum number of sources’ DOA can be estimated is N/2 for N sensors. In the proposed method, the expression of each DFT bin is derived in polynomial form by using the consecutive multiplication. Then the linearity of DFT bins is used to calculate the DOA. Moreover, the complex-valued least squares framework is adopted to extend the proposed method for multiple snapshots. The effect of noise on DOA estimation is simulated. And simulation results validate the advantages of the proposed method.

Multi-Source DOA Estimation in Reverberant Environments by Jointing Detection and Modeling of Time-Frequency Points

In this article, the direction of arrival (DOA) estimation of multiple speech sources in reverberant environments is investigated based on the recording of a soundfield microphone. First, the recordings are analyzed in the time-frequency (T-F) domain to detect both “points” (single T-F points) and “regions” (multiple, adjacent T-F points) corresponding to a single source with low reverberation (known as low-reverberant-single-source (LRSS) points). Then, a LRSS point detection algorithm is proposed based on a joint dominance measure and instantaneous single-source point (SSP) identification. Following this, initial DOA estimates obtained for the detected LRSS points are analyzed using a Gaussian Mixture Model (GMM) derived by the Expectation-Maximization (EM) algorithm to cluster components into sources or outliers using a rule-based method. Finally, the DOA of each actual source is obtained from the estimated source components. Experiments on both simulated data and data recorded in an actual acoustic chamber demonstrate that the proposed algorithm exhibits improved performance for the DOA estimation in reverberant environments when compared to several existing approaches.

Multi-source DOA estimation in reverberant environments using potential single-source points enhancement

Direction-of-arrival (DOA) estimation of multiple speech sources in enclosed environments is a hot topic in the acoustic field. Reverberation and multi-source overlapping are the primary challenges in multi-source localization. These disturbances result a performance degradation. In this paper, a new method for multi-source DOA estimation in reverberant environments is presented using potential single-source points enhancement. First, we made a statistical investigation about the effect of reverberation on single-source points (SSPs) whose components dominantly derive from only one source. From the experimental results, it is found that several time-frequency points detected as SSPs in anechoic environments are converted into non-SSPs when the signals are recorded in reverberant conditions. These points are defined as potential SSPs in this paper. Then, potential SSPs enhancement algorithm is designed to suppress the effect of reverberation for SSPs detection. Finally, DOA estimation is accomplished by post-processing the histogram, which is generated by the DOA estimates calculated at the detected SSPs. We conducted objective evaluations in both simulated and real environments. Experiment results indicate that the localization scheme proposed in this paper is more efficient with some state-of-the-art localization methods in terms of both DOA estimation and source counting.

Source cancellation in cross-correlation functions for broadband multisource DOA estimation

In multi-source localization systems, a stronger source can hide weaker sources. In this paper, we present a new technique to eliminate the effect of a source in the Generalized Cross-Correlation functions (GCCs) of the signals captured with a broadband sensor array. The proposed method is based on the projection of the GCCs onto a subspace orthogonal to the source position. This technique may be seen as an extension of previous narrowband techniques with an efficient time-domain implementation. The method does not only eliminate the main peak generated by the source, but also the peaks that appear if other sources are correlated with the cancelled one. This technique can be applied to find the maximum of these new GCCs or combined with other GCC-based Direction of Arrival (DOA) estimation techniques, such as the SRP-PHAT algorithm. In addition, no assumption about time-frequency sparsity needs to be made. The technique has been evaluated with a microphone array in simulated conditions and in a real highly reverberant room to verify that the proposed technique provides better results than the conventional SRP-PHAT and other state of the art source cancellation techniques when the location of various sources with different energy levels is required.

DOA Estimation Performance Analysis and Verification of Dual Crossed-Loop/Monopole Antenna Array

The compact high-frequency surface wave radar using a crossed-loop/monopole (CLM) antenna as the receiving sensor has been widely used in ocean remote sensing and target monitoring. However, the direction of arrival (DOA) estimation accuracy of a single CLM antenna is the dominant factor that restricts the target monitoring performance of the compact HF radar. Besides, the single CLM antenna can estimate two signals simultaneously at most, but its effectiveness is challenged by the pattern distortion and the existence of coherent sources, which limits the application range of the compact HF radar. In this study, a compact array combining two CLM antennas is proposed to improve the DOA estimation accuracy and solve the multisource DOA estimation problem. The estimation error and multisource DOA estimation performance of a dual CLM antenna array are analyzed by formula derivation and simulation. Furthermore, the field experiment results are given to demonstrate the performance improvement of the dual CLM antenna array.

A Fast Multi-Source Sound DOA Estimator Considering Colored Noise in Circular Array

Sound direction of arrival (DOA) estimation keeps finding new applications. For instance, real-time sound DOA estimation enabled by acoustic sensors on low-cost and limited-resource devices can enhance or create many consumer use cases. Indeed, tracking multiple moving sound sources in noisy or reverberated condition with a cost-effective platform is highly desirable. In this paper, we develop a computation-aware DOA estimator that uses weighting in frequency domain. It relies on single source zones (SSZs) and circular integrated cross spectrum (CICS) techniques and peak search on DOA histogram. The existing histogram-based multi-source DOA estimation is fundamentally limited in angular resolution, and the CICS process is very time consuming. To tackle these problems, we propose a weighting method that exploits the unevenness of intermediate DOA estimation errors over the frequencies of interest, and a simple yet effective strategy to reduce search range. Two underlying mechanisms, the impact of the colored noise of microphones and the frequency-dependent behavior (statistic model) of DOA estimation errors, are unveiled. We have developed an experimental platform using commercially off-the-shelf devices to verify design objectives, including feasibility and real-time requirement. The proposed techniques are validated on the developed platform and the system performance is evaluated experimentally in a real-world environment. It is observed the DOA estimation resolution is improved roughly from 30° to 15° and the overall computation is reduced by 68%.

A Robust Phase-Ambiguity-Immune DOA Estimation Scheme for Antenna Array

A direction-of-arrival (DOA) estimation scheme that is immune to phase ambiguity is proposed for directional uniform circular array in the context of software defined radio based engineering implementation, where phase calibration is often required in multi-channel receiver for high-resolution, multi-source DOA estimation algorithms to work. The proposed scheme makes use of sparse signal representation technique, and is robust in the sense that no phase calibration is required when implemented by multi-channel receiver and can achieve multi-source DOA estimation when single-channel receiver is used. To facilitate the performance evaluation, a Cramer–Rao lower bound to the proposed estimation scheme is derived. Implemented by $\ell _1$ -regularized least squares program, the feasibility of the proposed scheme is validated via simulations.

Learning soft mask with DNN and DNN-SVM for multi-speaker DOA estimation using an acoustic vector sensor

Using an acoustic vector sensor (AVS), an efficient method has been presented recently for direction of arrival (DOA) estimation of multiple speech sources via the clustering of the inter-sensor data ratio (AVS-ISDR). Through extensive experiments on simulated and recorded data, we observed that the performance of the AVS-DOA method is largely dependent on the reliable extraction of the target speech dominated time–frequency points (TD-TFPs) which, however, may be degraded with the increase in the level of additive noise and room reverberation in the background. In this paper, inspired by the great success of deep learning in speech recognition, we design two new soft mask learners, namely deep neural network (DNN) and DNN cascaded with a support vector machine (DNN-SVM), for multi-source DOA estimation, where a novel feature, namely, the tandem local spectrogram block (TLSB) is used as the input to the system. Using our proposed soft mask learners, the TD-TFPs can be accurately extracted under different noisy and reverberant conditions. Additionally, the generated soft masks can be used to calculate the weighted centers of the ISDR-clusters for better DOA estimation as compared to the original center used in our previously proposed AVS-ISDR. Extensive experiments on simulated and recorded data have been presented to show the improved performance of our proposed methods over two baseline AVS-DOA methods in presence of noise and reverberation.