A coarray processing technique for nested vector-sensor arrays with improved resolution capabilities

In this paper, we propose a new coarray processing technique for nested electromagnetic vector-sensor (EMVS) arrays. Vector-sensors by providing multi-component measurements of the incident waves offer additional information over the common single-output scalar sensors. The current paper takes the advan-tage of the multi-component measurements of vector-sensors combined with the elegant nested array strategy to design a new coarray processing technique. In this way, we utilize the auto/cross-correlation matrices of the underlying subarrays of the nested EMVS array to serve as snapshots in the coarray do-main. Then, we apply two levels of spatial and temporal smoothing procedure to recover the rank of the model and also improve the covariance matrix estimation in the difference coarray domain. The proposed method can provide O(N2) degrees of freedom (DOFs) with only N physical EMVSs. The effectiveness of the proposed coarray processing technique is demonstrated through several simulation examples. Simula-tion results show that the proposed technique provides superior resolution capabilities and more accurate DOA estimates in the asymptotic region compared to the other counterparts. Moreover, the proposed method requires relatively less computational efforts among the other methods.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

A new coarray processing technique for nested electromagnetic vector-sensor arrays is proposed in this paper. By utilizing the multi-component measurements of vector-sensors and the nested array strategy, the proposed method provides superior resolution capabilities and more accurate DOA estimates with relatively less computational efforts.