As the detection-of-arrival (DOA) estimation algorithm used in traditional polarization sensitive arrays has such problems as high computation complexity and poor real-time performance, this study proposes a data compression-based orthogonal dipole polarization sensitive array structure.

By applying compression sensing technology to the system design (i.e., data compression technology), the proposed structure compresses the dimensions of the receiving signal vector, controls the complexity of the system by reducing the number of front-end chains, and brings high flexibility to the array structure design. At the same time, a dimensionality reduction-based multiple signal classification (MUSIC) algorithm is also proposed. First, the DOA estimation of signals is realized through spatial spectrum searching. The Lagrange multiplier method is then used to reduce the searching dimensionality, and the signal polarization parameters are obtained by solving the optimization problem.

Simulation experiments show that the proposed array structure and MUSIC algorithm can correctly estimate DOA and polarization parameters when the incident signals are completely polarized and incoherent. When the signal-noise ratio (SNR) is greater than 10 dB, the root mean square error (RMSE) of the elevation angle is less than 0.05°.

Compared with the non-compressed structure with an equal channel number under the same conditions, the proposed structure can provide higher estimation accuracy and lower computational complexity.