Cong Xiang

Robust adaptive beamforming for MIMO radar

In this paper, a new robust adaptive beamforming method is developed for MIMO radar in the presence of unknown mismatches of the desired signal steering vectors. Explicit models of uncertainties in both transmitted and received signal steering vectors are considered. It is shown that the robust adaptive beamforming problem for MIMO radar can be solved by minimizing a convex quadratic cost function based on the optimization of worst-case performance when full DOFs of MIMO radar are used. Whereas, we reformulate the quadratic cost function into a bi-quadratic cost function by adopting a separable form for the weight vector and the minimum point of the new cost function can be efficiently found by combining bi-iterative algorithm (BIA) with second-order cone programming (SOCP). The proposed beamformer has lower computational complexity and faster convergence rate comparable with that of the traditional robust adaptive beamforming algorithms with full DoFs, while, at the same time, it provides better robustness to the non-ideal cases and reduces the training samples required. Numerical experiments with the frequently encountered types of signal steering vector mismatches are provided to demonstrate the effectiveness of the proposed robust adaptive beamforming algorithm as compared with the other robust adaptive beamforming algorithms.

Fast communication: Tri-iterative least-square method for bearing estimation in MIMO radar

In this paper, we introduce a novel tri-iterative least-square (TI-LS) method for bearing estimation in multiple-input multiple-output (MIMO) radar system. The TI-LS iteratively finds two steering matrices associated with the transmit and receive arrays, and the targets bearing can be easily obtained based on prior knowledge of the transmit and receive array manifolds. Furthermore, by jointly using the information from the transmit and receive array responses, angular estimation accuracy can be significantly improved. Numerical examples are presented to illustrate the excellent angular estimation accuracy and computational efficiency of this new method.

Fast communication: Three-dimensional reduced-dimension transformation for MIMO radar space-time adaptive processing

In this paper, a spatial-temporal filter with three-dimensional (3D) cascaded configuration is proposed for multi-input multi-output (MIMO) radar space-time adaptive processing (STAP) based on reduced-dimension transformation. The 3D weight vector which is decomposed into the Kronecker product of transmitting, receiving and temporal weight vectors can be efficiently obtained by the proposed tri-iterative algorithm (TRIA). It is shown that the proposed approach can significantly reduce the computational burden and training-data requirement. The effectiveness of the proposed algorithm is demonstrated via the numerical experiments as compared with the other methods.

Adaptive beamforming for MIMO radar with planar array antennas using separable weight vectors

This paper focuses on adaptive beamforming for multiple-input multiple-output (MIMO) radar with planar array antennas which improves the angular resolution in azimuth and elevation. The length of the steering vector in MIMO radar system with a two-dimensional (2D) planar array can be very large, which complicates the design for real-time applications. We optimize two low dimensional vectors which are separated from the optimum weight vector to decrease the computational complexity and the requirement of training samples. Simulations results demonstrate that the proposed method can converge very well and achieve better performance of interference and jammer suppression in the presence of short data records.