A novel direction-finding method is proposed for bistatic multiple-input-multiple-output (MIMO) radar in the impulse noise in this paper. The method has the capacity to suppress the impulse noise by means of infinite norm normalization and can obtain better performance for direction finding via the weighted signal subspace fitting algorithm. To solve the objective function of this method, we devise a quantum-inspired grey wolf optimization algorithm to acquire the global optimal solution. The proposed method based on QGWO can resolve the direction-finding difficulties of bistatic MIMO radar. Monte-Carlo experiments have confirmed the robustness and superiority of the proposed method for locating independent and coherent sources with a small number of snapshots in the impulse noise compared with some existing direction-finding methods in a series of scenarios. In addition, we present the Cramér-Rao bound (CRB) of angle estimation for bistatic MIMO radar in the impulse noise, which generalizes the Gaussian CRB for performance analysis.

针对脉冲噪声中的双基地多输入多输出雷达，提出了一种新颖的测向方法。该方法具有通过无限范数归一化抑制脉冲噪声的能力，并且可以通过加权信号子空间拟合算法获得更好的测向性能。为了解决该方法的目标函数，我们设计了一种量子启发式灰狼优化算法来获取全局最优解。所提出的基于QGWO的方法可以解决双基地MIMO雷达的测向困难。蒙特卡洛实验已经证实，与一系列场景中的某些现有测向方法相比，该方法在脉冲噪声中具有少量快照的独立和相干源定位方法的鲁棒性和优越性。此外，我们提出了脉冲噪声中双基地MIMO雷达角度估计的Cramér-Rao界（CRB），将高斯CRB推广到性能分析中。