This article presents a new method for 3-D localization of an acoustic source signal by estimating its azimuth-angle, elevation-angle, and radial range. The proposed method exploits a spatially spread acoustic vector sensor, which is composed of a tri-axial velocity vector sensor and an isotropic pressure sensor. Unlike the spatially collocated case, the self-normalization of spatially spread acoustic vector sensor’s response is no longer independent of the source location, resulting in the inapplicability of the widely used “self-normalization” estimators (A. Nehorai et al. , 1994), (V. N. Hari et al. , 2012), (Y. Song et al. , 2013). The present article considers the near-field propagation’s path attenuation and phase difference among the sensor components and derives a source location estimation method without resorting to the “self-normalization” operation. Also, the proposed method is applicable to any nonfree-space propagation models at arbitrarily unknown path-loss exponent. In comparison with the existing methods, where the velocity vector sensor is spatially collocated, the proposed method can offer improved performance estimation due to the inherent extension of the vector sensor’s spatial aperture in the spread structure.

In this article, we address the problem of robust adaptive beamforming in the presence of array sensor miscalibration. We consider the use of partly calibrated linear arrays, where only a small portion of sensors have been gain-phase aligned. Our solution is based on the interference-plus-noise covariance matrix (INCM) reconstruction principle. In our solution, the INCM is reconstructed by performing simultaneous interference localization and array calibration (SILAC). Toward this end, a novel virtual baseline extension technique is presented for high-accuracy SILAC. After SILAC, the interference and noise powers are estimated, and the INCM is reconstructed subsequently. No computations of integration/summation and nonlinear optimization are involved in our beamformer, which is termed as “INCM-SILAC” beamformer. Numerical examples are offered to validate the performance of the INCM-SILAC beamformer. A MATLAB code for reproducing the results of radar application example is available at https://github.com/jinhesjtu/SILAC.git

This paper introduces a new algorithm called Manifold Reconstruction Unitary ESPRIT (MR-UESPRIT) for accurately estimating the direction-of-arrival (DOA). The algorithm combines manifold reconstruction and unitary ESPRIT to overcome the limitations of traditional DOA estimation methods. In this approach, two smaller overlapping subarraysare used to derive a rotational invariance equation, which helps recover the array manifold matrix. As a result, the DOA estimation problem is divided into K˗ individual single-source DOA estimation problems. Each DOA is estimated by minimizing a nonlinear least squares fitting criterion using a computationally efficient Newton's method. Computer simulations are presented to demonstrate the effectiveness of the MR-UESPRIT method compared to standard DOA estimation methods.