拟蒙特卡罗粒子滤波改进算法及其在雷达目标跟踪中的应用

拟蒙特卡罗粒子滤波算法（quasi-Monte-Carlo particle filter, QMC-PF）精度不高，运算复杂度大，难以满足雷达机动目标跟踪精确性和实时性需求. 为此，提出一种基于BP神经网络的新型拟蒙特卡罗粒子滤波算法. 该算法将大权重粒子通过QMC分裂采样产生低差异性的子代粒子，以此来替代低权重粒子，保证了样本的有
效性和多样性；同时利用BP神经网络计算子代粒子的权重，提高了滤波的精度和速度；最后在不同的模型中进行仿真. 实验结果表明，与QMC-PF相比，所提出的算法提高了精度和运算速度，适用于雷达机动目标的跟踪.

Improved Quasi-Monte-Carlo Particle Filtering and Its Application to Radar Target Tracking

To address the difficulties in meeting the needs of precise and real-time radar maneuvering target tracking due to low precision and high computation complexity of quasi-Monte-Carlo particle filter (QMCPF), a new quasi-Monte-Carlo particle filter algorithm base on BP neural network (NQMC-PF) is proposed.
Through QMC fission sampling, this algorithm generates low-discrepancy progeny particles to replace the low-weight particles to guarantee validity and diversity of the samples. Meanwhile, the algorithm uses BP neural network to calculate the weight of offspring of particles. With different models, the algorithm is tested. Experimental results show that, compared to QMC-PF, the proposed algorithm can enhance precision and increase calculation speed, and thus is applicable to radar for tracking maneuvering targets.