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.

Recently the improved deconvolution methods using sparse regularization achieve high spatial resolution in aeroacoustic imaging in the low Signal-to-Noise Ratio (SNR), but sparse prior and model parameters should be optimized to obtain super resolution and be robust to sparsity constraint. In this paper, we propose a Bayesian Sparse Inference Approach in Aeroacoustic Imaging (BSIAAI) to reconstruct both source powers and positions in poor SNR cases, and simultaneously estimate background noise and model parameters. Double Exponential prior model is selected for source spatial distribution and hyper-parameters are estimated by Joint Maximized A Posterior criterion and Bayesian Expectation and Minimization algorithm. On simulated and real data, proposed approach is well applied for near-field wideband monopole and extended source imaging. Comparing to several classical methods, proposed approach is robust to noise, super resolution, wide dynamic range, but parameters like source number or SNR are not needed.

Most exiting array signal processing techniques for bearing estimation are strongly relied on the far-field assumption. When the sources are located close to the array, these techniques may no longer perform satisfactorily. In this work, we propose a tensor-based algorithm which is dedicated to the joint estimation of the range and the bearing of multiple narrow-band and near-filed sources in a spatially white Gaussian noise. Automatic paring of the model parameters is achieved for an uniform linear array. By means of numerical simulation, we show that for low signal to noise ratio, the proposed algorithm is more accurate than the higher order statistics (HOS)- based ESPRIT algorithm for small/moderate number of snapshots.

In mobile telecommunications, the quality of demodulation is strongly impacted by channel estimation. The Joint Angle, Delay and Polarization Estimation (JADPE) problem is addressed in this paper when a linear uniform array of crossed dipoles is used to measure the signal. The purpose of this paper is to study the high resolution JADPE ESPRIT method for the estimation of the parameters that characterize each path in order to improve channel estimation and demodulation. A complete system simulation (based on TDD-UTRA standard of UMTS) is proposed in order to evaluate performance in terms of channel estimation accuracy and BER. Simulation results show the interest of considering the polarization as a channel parameter in order to increase the performance. Improvement in the path separation and received signal power is obtained and, because of this, channel estimation accuracy and data estimation accuracy are improved as well.

In mobile communications the antenna arrays make it possible to estimate the path delay, angle of arrival (or angle), and amplitude for the multipath propagation channel. This paper considers the problem of joint angle and delay estimation (JADE) in a time-division code-division multiple access system. The angle/delay estimation is made from multiple slots by exploiting the angle/delay invariance of the channel regardless of the fast faded amplitude variation (i.e., angle/delay is assumed quasistatic). JADE can be approximated by methods that estimate the frequencies of the two-dimensional complex sinusoids where the faded amplitudes act as sources. The shift invariance method (JADE-ESPRIT) is chosen for its capacity to pair delay and angle for each path and each user. The consistency of JADE-ESPRIT for a large enough number of slots guarantees the feasibility of the method for spatially correlated noise. The performance of the JADE method for channel estimation is evaluated analytically and numerically in propagation environments with increasing complexity.