A Tensor Generalized Weighted Linear Predictor for FDA-MIMO Radar Parameter Estimation
Chao Wen, Yu Xie, Zhiwei Qiao, Liyun Xu, Yuhua Qian
Abstract
Radar parameter estimation in terms of its range, angle and velocity plays a crucial role in many applications. Multiple-input multiple-output (MIMO) radar with frequency diverse array (FDA) is capable of resolving targets in one directional beam with different ranges and velocities (Doppler shifts). Prevalent methods obtain target parameters in a sequential manner to get rid of exhausted multi-dimensional search, but they suffer from accumulated estimation errors. To tackle this issue, a tensor generalized weighted linear predictor (TGWLP) is devised for FDA-MIMO radar parameter estimation, where the parameters are estimated in a parallel manner. Tensor modeling of multidimensional FDA-MIMO radar signal is developed, so that the joint parameter estimation is casted into multiple-pulse-group version of three-dimensional (3D) HR problem associated to the mixed Swerling model. Pulse-group diversity is exploited to obtain precise velocity estimation. In the presence of targets with some identical parameters, the final estimations of unambiguous slant range, conic angle, and radial velocity of a moving target can be easily obtained after the parallel frequency estimations. Besides, all the parameter pairing is automatically achieved, which is free of the extra burden from pairing process. Furthermore, the identifiability of the proposed joint estimator is analyzed. Finally, theoretical analysis and simulations are included to demonstrate that the proposed approach can achieve improved performance compared to the existing methods.