Tucker tensor based regularized polarimetric ESPRIT

The problem of direction of arrival (DOA) estimation using a polarization sensitive array is addressed. A Tucker tensor based regularized polarimetric estimation of signal parameters via rotational invariance technique (trpESPRIT) is proposed by using sequentially truncated higher-order singular value decomposition (STHOSVD). In the method, the Tucker tensor model for the polarimetric measurements is firstly established, then the signal subspace is obtained by using STHOSVD, and the DOAs of signals are finally obtained by using multiple rotationally invariant subspace amplitude and phase information. Compared with the traditional matrix methods, the Tucker tensor modeling scheme is more convenient for the characterization of the multidimensional data structure and the multidimensional data matching operation. The STHOSVD can be used to obtain more accurate signal subspace and the subsequent DOA estimation. The simulation results show that, compared with the matrix and vector methods, the trpESPRIT has a higher noise suppression capability and a higher DOA estimation precision. Under the condition of low signal-to-noise ratio and few snapshots, the trpESPRIT is still observed to have a good resolution.