MIMO雷达迭代最差性能最优鲁棒波束形成算法

Robust Beamforming Using Iterative Worst-Case Performance Optimization for MIMO Radar

  • 摘要: 针对现有的MIMO雷达波束形成算法在联合导向矢量失配较大情形时,输出信干噪比性能严重下降的问题,本文提出了一种MIMO雷达迭代最差性能最优算法。结合MIMO雷达收发阵元结构,对联合导向矢量失配误差,尤其是方向估计误差进行了理论推导,给出了集中式MIMO雷达方向估计误差的经验取值,并基于此对大不确定集算法的局限性进行了分析。所提算法采用较小的不确定集对误差进行约束,利用权矢量与联合导向矢量对应关系,不断迭代更新期望导向矢量估计值,直至满足设计的终止条件。仿真实验表明,所提方向估计误差经验取值与理论值相吻合,所提算法对联合导向矢量大失配情形具有很强的鲁棒性,且输出信干噪比性能达到最优。

     

    Abstract: The output signal to noise plus interference (SINR) of the existing MIMO radar beamforming algorithms suffer from the large joint steering vector mismatch. Aiming at this problem, a method using iterative worst-case performance optimization is proposed in this paper. Combining the MIMO radar transceiver array structure, the joint steering vector mismatch error, especially the direction estimation error, is analyzed in theory and the empirical value of direction estimation mismatch for co-located MIMO radar is given. Based on this, the limitation of the large-uncertainty-set-based algorithms is pointed out. The proposed algorithm uses a smaller set to constraint all errors and iterate over the estimated joint steering vector using the corresponding relationship between the joint steering vector and the weighted vector until meet the designed termination conditions. Simulation experiments show that the proposed empirical value is consistent with the theoretical value, and that the proposed algorithm presents outstanding performance in improving the output SINR and stong robustness for large mismatch case.

     

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