A Quadrilinear Decomposition Method for Direction Estimation in Bistatic MIMO Radar

Abstract

We investigate into the problem of joint direction-of-departure (DOD) and direction-ofarrival (DOA) estimation in a multiple-input multiple-output radar, and a novel covariance tensor-based quadrilinear decomposition algorithm is derived in this paper. By taking into account the multidimensional structure of the matched array data, a fourth-order covariance tensor is formulated, which links the problem of joint DOD and DOA estimation to a quadrilinear decomposition model. A quadrilinear alternating least squares (QALSs) technique is applied to estimate the loading matrices, and thereafter automatically paired DODs and DOAs are obtained via the LSs fitting strategy. The proposed QALS algorithm can be regarded as an alternative to the direct parallel factor (PARAFAC) algorithm, which is more flexible than the latter since it can be easily expanded to scenario with spatially colored noise. Moreover, the proposed algorithm has much lower computational complexity than PARAFAC, especially in the presence of large snapshot. Numerical simulations verify the effectiveness of the proposed algorithm.

Keywords

• Multiple-input multiple-output radar
• direction estimation
• tensor decomposition
• colored noise