As a research focus within the field of array signal processing, multi-source direction-of-arrival (DOA) estimation in enclosed environments has been paid much attention. Contaminated by reverberation, noise, and inter-source interference, DOA estimation become challenging. Hence it is essential to identify time-frequency (TF) points dominated by only one source to alleviate these issues. This paper proposes a TF point selection method for DOA estimation based on the first-order relative harmonic coefficient (RHC). This is first analyzed on the “point” level from two perspective, and we design an adaptive single-source dominant zone (SSDZ) detection method. Subsequently, the relationship between first- and zero-order RHC magnitudes of different types of TF points is explored, and we develop a simple but useful rule to further select TF points in the detected SSDZs. Finally, we adopt two-dimensional (2-D) kernel density estimation (KDE) and peak search to estimate the DOAs of sources after calculating the angles of the detected TF points. The effectiveness and robustness of the proposed method are verified and compared with the reference methods through experiments with both the simulated and real-world recordings.

Source localization in reverberant environments has been a prominent research topic in the past two decades. In this paper, instead of the commonly employed time-frequency (TF) bin based methods which rely on empirically selected threshold values, we leverage the microphone array signal model comprising an early reverberant component and a late reverberant component, to propose a novel method for the source localization problem in reverberant environments. Our proposed criterion involves the joint removal of the late reverberant component using the multi-channel linear prediction (MCLP) filter, while estimating the directions of arrival (DOAs) of the actual sources using the early component signals. By applying the azimuth sparsity constraint, the true DOA can be estimated with high resolution and free from the interference of the early reflections. To solve the proposed criterion, DOAs, source signals, and MCLP filter coefficients are estimated by alternative iterations. Additionally, we present a source localization criterion specifically designed for the single source scenario as a special case of the multiple sources scenario. Finally, a source number estimation method and a postprocessing procedure are discussed for searching the global solutions to our proposed criteria. Evaluations with both simulated and realistic data demonstrate the advantages of our proposed methods over the baseline methods.

Most of the existing Beamforming methods are based on the assumptions that the sources are all point sources and the angular separation between the direction of arrival (DOA) of the source and the interference is large enough to assure good performance. In this paper, we consider a tough scenario where the target source and the interference are simultaneously spatially distributed and overlapped. To improve the performance of Beamforming in this scenario, we propose two approaches: the first approach exploits the non-Gaussianity as well as the spectrogram sparsity of the output of the microphone array; the second approach exploits the generalized sparsity with overlapped groups of the Beampattern. The proposed criteria are solved by methods based on linearized preconditioned alternating direction method of multipliers (LPADMM) with high accuracy and high computational efficiency. Numerical simulations and real data experiments show the advantages of the proposed approaches compared to previously proposed Beamforming methods for signal enhancement.

In this paper, the localization of spatially distributed sources is considered. Based on the problem formulation of the Deconvolution Approach for the Mapping of Acoustic Sources (DAMAS), a criterion based on a convex optimization under sparsity constraint is proposed to locate the sources. Also an original method is given to recover the angular distributions and the power of the sources. Simulations executed in the scenario of a mixture of distributed and point sources illustrate the validation of the proposed approach compared to other methods.

In this paper, the direction of arrival (DOA) localization of spatially distributed sources impinging on a sensor array is considered. The performance of the well known MUSIC estimator is studied in the presence of model errors due to angular dispersion of sources. Taking into account the coherently distributed source model proposed in [1], we establish closed-form expressions of the DOA estimation error and mean square error (MSE) due to both the model errors and the effects of a finite number of snapshots. The analytical results are validated by numerical simulations and allow to analyze the performance of MUSIC for coherently distributed sources.