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 polarizationsensitive arra...
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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 polarizationsensitivearray (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.
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