Abstract
Target detection is critical in many mission critical sensors and sensor network (MC-SSN) applications. For target detection in complicated electromagnetic environment, DOA estimation using polarization sensitive array (PSA) has been receiving increased attentions. In this paper, we propose the parallel co-prime polarization sensitive array (PCP-PSA) which consists of the cocentered orthogonal dipole triads (CODTs) to estimate two-dimensional direction-of-arrival (2D DOA) and polarization parameters. The degrees of freedom (DOFs) have been extended due to the co-prime structure, so that the more signals can be detected and the estimation accuracy is improved. In order to reduce the computation complexity, we construct a new cross-covariance matrix based on the CODTs, which converts the two-dimensional DOA estimation into two independent one-dimensional DOA estimations. Then, the spatial smoothing-based multiple signal classification algorithm(MUSIC) and the sparse representation-based method are applied to estimate 2D DOA with only one-dimensional (1D) peak searching and 1D dictionary, respectively. Finally, the polarization parameters are estimated by using the cross-covariance matrix between components of electric field vector. Compared with previous PSA-based algorithms, the proposed algorithm based on PCP-PSA can solve the underdetermined 2D DOA and polarization parameters estimation problem and has better estimation accuracy. Theoretical analyses and simulation results verify the effectiveness of the proposed methods in terms of computational complexity and estimation accuracy.
Highlights
In recent years, mission critical sensors and sensor network (MC-SSN) applications, such as target detection and reliable communication, have received increasing attentions from both research community and industry [1]–[3]
We propose a parallel co-prime polarization sensitive array (PCP-PSA), and construct a novel cross-covariance matrix
The results are compared with the parallel co-prime array (PCPA) algorithm [20], three-parallel co-prime array (TPCPA) algorithm [23], and LV-MUSIC algorithm [7]
Summary
Mission critical sensors and sensor network (MC-SSN) applications, such as target detection and reliable communication, have received increasing attentions from both research community and industry [1]–[3]. Compared with the existing estimation algorithm based on PSA, the proposed model dramatically increases the DOFs since the co-prime structure is constructed, which is applicable to underdetermined situations and improves the estimation performance. The spatial smoothing method can effectively recover the rank of the array covariance matrix, so that the correlated signals can be estimated by subspace-based algorithm. The forward/backward spatial smoothing (FBSS)MUSIC algorithm [32] and 1−norm penalty-based sparse recover algorithm are used to estimate the DOA angle β. Appling the FBSS covariance matrix to the conventional MUSIC algorithm, the DOA angle β can be estimated. Once the estimation of DOA angle β is obtained, the steering matrix B (β) of the virtual ULA in (14) can be calculated as. {θk , φk paired automatically in the process of the least square operation
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