Two-Dimensional DOA Estimation for Polarization Sensitive Array Consisted of Spatially Spread Crossed-Dipole

This paper studies the problem of 2-D direction of arrival (DOA) estimation with polarization sensitive array (PSA), in which polarized vector sensor is consisted of spatially spread crossed-dipole (SSCD). It is well known that crossed-dipole vector sensor can obtain all the polarization information including HH, HV, VH, and VV. Consequently, it is one of the most popularly polarized vector sensor. Unfortunately, in the existing works, it is difficult to effectively solve the problem of DOA estimation with SSCD array. Motivated by this, this paper presents a sparse rectangular SSCD-PSA to solve this problem. First, the ESPRIT algorithm is adopted to calculate the high accuracy but ambiguous direction cosine estimation. Second, the polarization information in the data covariance matrix is incorporated into incident source to form a block sparse signal model, and then, the block orthogonal matching pursuit is used to get a coarse reference direction cosine estimation. At last, a finally fine 2-D DOA estimation can be obtained by disambiguation process using the combination of high accuracy but ambiguous and coarse reference direction cosine estimations. The proposed algorithm obtains the 2-D DOA estimation under the lowest components polarized vector sensor and keeps the advantage of low mutual coupling. The simulation results prove the effectiveness and correctness of the proposed algorithm.

I. Introduction

Polarization sensitive array (PSA) signal processing is a popular field of array signal processing. It has broad applications in radar, communication, sonar and biomedical systems [1], [2]. Direction of arrival (DOA) estimation is one of the main tasks of PSA signal processing. Polarized vector sensor can be formed from two orthogonal sensors to six orthogonal sensors. The six orthogonal sensors configuration includes three dipoles and three loops, which is called as electromagnetic vector sensor (EMVS) [3], proposed in 1994. Any array composed of polarized vector sensor can be called as PSA. In the early period of PSA research, the problem of DOA estimation is solved by using ESPRIT super resolution algorithm [4]–[8]. Then the necessary conditions of DOA estimation for PSA is studied, that is, the independent of array manifold [9]–[12], including the cases of one single EMVS and an EMVS array. After that, the DOA estimation with PSA are developed rapidly, which mainly includes the following several aspects: 1. For one single EMVS, DOA estimation and target tracking are studied by the methods of the vector cross product algorithm, propagator algorithm and parallel factor analysis algorithm [13]–[18]. 2. For six-component EMVS array, a outstanding kind of “dual-size” algorithms are proposed in [19]–[22], which DOA estimation accuracy is greatly increased because of the application of sparse array (we will also use the technology in this paper). 3. Under the hypercomplex framework [23]–[28], DOA estimation is calculated based on bicomplex, quaternion, biquaternion, and quad-quaternion, which provides better estimation accuracy than those of complex algorithms in the case of perturbations caused by noise and model error. 4. For coherent source, a new kind of pre-process named polarization smoothing [23], [29]–[32] is proposed, which is different from spatial smoothing, it doesn’t loss the array aperture. 5. Almost all of the aforementioned works are based on complete six-component EMVS. Thus, DOA estimation with the incomplete four-component, three-component, and two-component polarized vector sensor are studied in [33]–[36].