Structure-Aware Bayesian Compressive Sensing for Near-Field Source Localization Based on Sensor-Angle Distributions

Abstract

A novel technique for localization of narrowband near-field sources is presented. The technique utilizes the sensor-angle distribution (SAD) that treats the source range and direction-of-arrival (DOA) information as sensor-dependent phase progression. The SAD draws parallel to quadratic time-frequency distributions and, as such, is able to reveal the changes in the spatial frequency over sensor positions. For a moderate source range, the SAD signature is of a polynomial shape, thus simplifying the parameter estimation. Both uniform and sparse linear arrays are considered in this work. To exploit the sparsity and continuity of the SAD signature in the joint space and spatial frequency domain, a modified Bayesian compressive sensing algorithm is exploited to estimate the SAD signature. In this method, a spike-and-slab prior is used to statistically encourage sparsity of the SAD across each segmented SAD region, and a patterned prior is imposed to enforce the continuous structure of the SAD. The results are then mapped back to source range and DOA estimation for source localization. The effectiveness of the proposed technique is verified using simulation results with uniform and sparse linear arrays where the array sensors are located on a grid but with consecutive and missing positions.

Sparse Reconstruction Based Robust Near-Field Source Localization Algorithm

“…Hu et al [ 22 , 23 ] achieved a sparse estimation of DOA and range by sparse representation of anti-diagonal elements of received signal covariance matrix, which is similar to the method of [ 20 ], but with lower computational complexity. In [ 24 , 25 ], by using the sensor-angle distribution to characterize the sensor-dependent phase progression as a function of the source range and its direction, the sensor-dependent spatial frequency signature was estimated by sparse reconstruction techniques, and the results were then mapped back to source range and DOA estimation for the near-filed source localization.…”

Algebraic Algorithm for Mixed Near-Field and Far-Field Sources Classification and Localization

“…Passive mixed source localisation using a sensor array has many important application areas such as radar and sonar [1,2]. For far-field (FF) sources, only DOA parameters are needed to be estimated.…”

DOA estimation of mixed coherent and uncorrelated targets exploiting coprime MIMO radar

“…The CMT-BCS achieves improved sparse signal reconstruction because it utilizes the group sparsity of the real and imaginary components of a complex variable. The superiority of the CMT-BCS has been successfully demonstrated in various applications, including DOA estimation, radar imaging, target localization, and time-frequency analysis [25][26][27][28].…”