This paper aims to estimate the two dimensional (2D) direction-of-arrival (DOA) using a polarized uniform rectangular array (URA) under multipath propagation. To leverage the tensorial nature, a parallel factor (PARAFAC) model is established, in which it comprises two spatial response matrices, the polarization response matrix, and the source matrix. Unfortunately, the source matrix exhibits rank-deficiency, hindering effectively PARAFAC decomposition. Our analysis reveals that the rank-deficiency can be easily resolved by taking the KhatriRao product with a full column rank factor matrix. Consequently, three rearranged PARAFAC tensors are obtained that are free of the source matrix's rank-deficiency. The estimation of 2D-DOA is then performed using the vector cross product-auxiliary rotational invariance technique (VCPARIT). The proposed algorithms are insensitive to inter-sensor distance and are suitable for a one-snapshot scenario. Furthermore, they outperform existing smoothing methods from the perspective of estimation accuracy. Theoretical advantages of the proposed algorithms are corroborated by the simulations.

This paper is aiming at addressing an azimuth-elevation direction-finding algorithm using a pair of identically oriented acoustic vector sensors located near a reflecting boundary. Two fourth-order cumulant matrices related in terms of a translationally invariant structure are formed for recovering the acoustic vector sensor steering vectors in the particle-velocity coarray domain. Afterward, source azimuth-elevation directions are extracted in closed-form from the estimates of the coarray steering vectors. The identifiability study shows that the algorithm proposed herein can uniquely resolve up to 13 sources, thereby being applicable to underdetermined scenarios, where the number of sources exceeds that of the sensors. This capability constitutes the primary advantage over the many seminal works Hawkes and Nehorai (2000); Wu et al. (2016); Ahmadi-Shokouh and Keshavarz (20007); Tao et al. (2007); Xu and Liu (2007) that pioneered AVS array processing involving reflecting boundaries. Finally, numerical examples are provided to verify the theoretical analysis and demonstrate the efficacy of the proposed algorithm.

A new signal processing algorithm proposed here is aimed at estimating five-dimensional (5-D) polarization-space-time (polarization, angle, delay) channel parameters for a tri-polarized system that works in a multipath environment. Unlike most channel estimation algorithms, where spatially spread antenna arrays are deployed, our algorithm is designed for a spatial col-located tripole antenna. A third-order tensor model is firstly established for the multipath polarization-space-time channel. This tensor is subsequently solved by exploiting low-rank decomposition techniques. After that, a closed-form solution of 2-D angles, 2-D polarizations, and multipath delay estimates for each multipath ray are derived. The proposed algorithm requires no computation for iterative searching and parameter pairing, thus offering an advantage in time-sensitive applications. In addition, it is applicable to either narrowband or wideband systems of arbitrary bandwidth and center-frequency.

In sensor array processing literature, near‐field bearing and range estimation algorithms generally use spherical wavefront to model only array sensors’ phase response (sometimes with Fresnel approximation) and assume equality in amplitude response. Ignoring the range dependent amplitudes, though facilitating the algorithmic development, will cause systematic estimation errors due to model mismatch. By taking the spherical wavefront amplitude into account, a new bearing and range estimation algorithm for locating multiple near‐field sinusoid sources is presented. With the estimation of the near‐field sensor array's response vector, closed‐form formulas for bearing and range estimates are derived from its magnitude, or phase, or both. The problem of estimation ambiguity is discussed as well. Cramér‐Rao bound is also derived to serve as a benchmark for performance study.

We propose a cumulant-based algorithm for multiple near-field source localization based on exact source-sensor spatial geometry, where no simplification, such as Fresnel approximation of spatial phase-delay and inconsideration of propagation attenuation is made. We define five cumulant matrices, by which a set of coarse angle-range estimates plus a set of ambiguous angle-range estimates are derived. Fine and unambiguous estimation is finally obtained by a simple refinement step. We define the algorithm's name as Cumulant Algorithm For Exact Model (CAFEM). Compared with previous algorithms, CAFEM has the following advantages: (1) it is simple, analytical, and search-free, (2) it is not necessary to use uniformly spaced linear arrays, and (3) it is applicable to array sensors with unknown amplitude uncertainties and can accommodate any arbitrarily unknown propagation loss.

In this paper, the problem of joint space-time parameter estimation for underwater wireless communication channels in a multipath environment is addressed. We consider the receive antenna array to be configured with multiple vector sensors, each of which consists of a pair of orthogonal velocity sensors. A quadrilinear model for the channel is formulated, and a quadrilinear decomposition method developed for joint angle and delay estimation (JADE). In addition, a computationally simple subspace-based algorithm is proposed for the problem under consideration. The basic idea behind this algorithm is to use the angle information embedded in the velocity vector sensors to start two polynomial rooting procedures for the angle and delay in succession. Simulation results are finally presented to verify the efficacy of the proposed algorithms.

This letter investigates the problem of joint estimation of the direction of departure (DOD) and the direction of arrival (DOA) for multi-input multi-output (MIMO) array systems. A new bistatic MIMO array system, configured with multiple transmit sensors and multiple velocity receive sensors is introduced, and a new joint DOD and DOA estimation algorithm is proposed. The key idea behind the proposed algorithm is to use the DOA information embedded in the velocity sensors to start 1-D MUSIC searches for the DOD and DOA in succession. The proposed successive MUSIC algorithm is suitable for irregular array geometry, imposes less constraint on the receive sensor spacing, and requires no parameter pairing nor two-dimensional searching.

This paper is concerned with robust array signal processing in impulsive noise environments. A simple weighting signal is defined to weight all sensor data in a snapshot-by-snapshot way, so that the resulting array data have the desired statistical characteristics used in the subspace-based direction-of-arrival (DOA) estimation techniques. Then any traditional subspace-based technique can be used for DOA estimation. In working example, the MUSIC algorithm is employed and a weighted array data-based MUSIC (WARD-MUSIC) algorithm is detailed. Simulations show the performance advantages of the WARD processing over other related methods in Gaussian mixture noise and symmetric alpha-stable noise