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.

This letter presents a novel scheme for moving target deception jamming (MTDJ) against multichannel synthetic aperture radar (SAR)-ground moving target indication (GMTI) based on multiple jammers. The multichannel SAR signal models for the real and false moving targets are established first. Then, the proposed MTDJ method based on multiple jammers is derived according to the principle that the interferometric phase matches the across-track velocity of the moving target. To ensure efficient deception jamming and to resolve the accurate scattering coefficient modulated in each jammer, the interferometric phase of the false moving target is decomposed into two parts, one is the term that matches the across-track velocity, while the other is the additional term generated by each jammer. Hence, the expected modulation coefficient in each jammer can be determined based on the additional interference phase cancellation. Finally, the proposed MTDJ is realized by generating the jamming signals in each jammer via the efficient deception jamming technique. The simulation results demonstrate the effectiveness of the proposed MTDJ method.

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.