Abstract
Two-dimensional multiple signal classification (MUSIC) algorithm based on polarization sensitive array (PSA) has excellent performance. However, it suffers a high computational complexity due to a multitude of complex operations. In this paper, we propose a real-valued two-dimensional MUSIC algorithm based on conjugate centrosymmetric signal model, which is applicable to arbitrary centrosymmetric polarization sensitive array. The modified forward/backward averaging, which can be applied to the PSA, is presented. Hence, the eigen-decomposition analysis process and spectrum function computation are converted into real domain, prominently reducing the computational complexity. Then, the direction-of-arrival (DOA) estimation is decoupled from the polarization parameter estimation so that the four-dimensional spectral peak search process is avoided. The theoretical computational complexity is discussed and the Cramer-Rao bound (CRB) of DOA estimation is derived in this paper. The simulation results indicate that the proposed algorithm achieves superior accuracy in DOA estimation and has low computational complexity.
Highlights
Parameter estimation is an important area in array signal processing, such as adaptive beamforming [1,2] and direction-of-arrival (DOA) estimation [3,4,5,6,7]
A uniform circular array (UCA) and a uniform rectangular array (URA) as shown in Figure 2 are used in the following simulations
This paper proposes a real-valued 2D multiple signal classification (MUSIC) algorithm based on CCSM data matrix, which ensures high computational efficiency and can be applied to arbitrary centrosymmetric polarization sensitive array (PSA)
Summary
Parameter estimation is an important area in array signal processing, such as adaptive beamforming [1,2] and direction-of-arrival (DOA) estimation [3,4,5,6,7]. The two-dimensional (2D) DOA estimation based on polarization sensitive array (PSA) shows great potential [8,9,10], which has gained considerable interests. Compared with traditional scalar array (TSA), the PSA composed of electromagnetic vector sensors (EMVSs) has more inherent merits. Polarization domain information of the electromagnetic signals can be measured by the PSA [11,12], which can bring greater potential capacity for parameter estimation. Many effective algorithms based on TSA have been extended to PSA, such as multiple signal classification (MUSIC) [13,14], estimation of signal parameters via rotational invariance techniques (ESPRIT) [15,16,17], Root-MUSIC [18,19,20], etc.
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