Sparsity-Based Direction-of-Departure and Direction-of-Arrival Estimation for Bistatic Multiple-Input Multiple-Output Radar

This paper provides an efficient method to determine the direction of departure (DOD) and direction of arrival (DOA) in bistatic multiple-input multiple-output (MIMO) radars. The proposed method firstly decouples the DOD and DOA parameters by converting the original received signal model into two separate new signal models. The new signal model corresponding to DOA can be directly obtained by matched filtering operation. In order to obtain the model for DOD, vectorization operation and kronecker transformation are utilized after the matched filtering operation. Both the new signal models for DOA and DOD behave like an augmented signal model of uniform linear array (ULA). Then, a covariance- vector sparsity-aware estimator is developed to find the accurate angular parameter. Meanwhile, in order to improve the estimation accuracy, the additive noise is eliminated by exploiting the toeplitze structure inherent in the array received covariance matrix and the asymptotic distribution of the sampling errors is also derived. Furthermore, the regularization parameter setting used by the proposed estimator is derived with the aid of the Lagrangian duality theory to guarantee the sparsity of solution. Simulation results are conducted to verify the effectiveness and the superiority of the sparsity-based estimator over other methods in terms of the angular estimation accuracy.