Joint Estimation for DOA and Polarization Parameters in Sparse Bayesian Framework

Abstract

Aiming at the problems of low precision and high computational complexity in estimating coherent signals by traditional polarization sensitive array, a joint parameter estimation algorithm based on sparse Bayesian learning framework for direction of arrival and polarization information is proposed.Firstly, the observation matrix is obtained by sparse data receiving matrix, then the observation data matrix is transformed from complex domain to real domain by unitary transformation, and a three-layer sparse prior is applied to the model parameters.Then, according to the variational Bayesian theory, the power spectrum function of sparse signal is constructed by the mean and variance of the model parameters, and the DOA of the signal is obtained by peak search. Finally, the estimated signal DOA and modulus constraint are used to obtain the polarization information. The simulation results show that the proposed algorithm can correctly locate coherent incident signals, and has higher direction finding accuracy and lower computational complexity.

Keywords

• |polarization sensitive array|joint parameter estimation|sparse bayesian learning|modulus constraint|unitary transformation