Joint DOD and DOA Estimation for Bistatic MIMO Radar in Unknown Correlated Noise

Abstract

In this paper, joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation for bistatic multiple-input-multi-output (MIMO) radar in an unknown spatially correlated noise environment is investigated. The signal model is based on the assumption that the waveforms are transmitted by two separated subarrays having M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> sensors and received by two separated subarrays having N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and N2 sensors, respectively. The received data are pulse-compressed using a matching matrix consisting of M=M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> +M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> orthogonally transmitted waveforms. The joint covariance matrix of unknown correlated noise is analyzed. A novel algorithm is proposed by jointly estimating the DOD and DOA with transmitter and receiver subarrays in unknown noise. The joint estimation algorithm is based on the canonical correlation decomposition (CCD) and exploits the shift-invariance properties in the Kronecker product structure of each column of the various steering matrices. The estimated DOA and DOD can be automatically paired correspondingly. In addition, the formulas of stochastic Cramér-Rao bounds (CRB) for DOD and DOA estimation are derived. Simulations show that our method can effectively improve the performance of estimation in unknown correlated noise environments and is insensitive to having different noise environments in the two subarrays.