采用拟蒙特卡罗法的被动多传感器目标跟踪

使用拟蒙特卡罗采样方法替代传统的蒙特卡罗采样方法，改善了高斯粒子滤波器的性能，结合多传感器集中式融合策略，提出了一种基于拟蒙特卡罗-高斯粒子滤波器的被动多传感器目标跟踪算法，较好地解决了被动跟踪中的强非线性和弱可观测性问题．该算法在降低计算复杂度的同时提高了跟踪的精度和稳定性，使算法快速收敛，并且具有并行结构，有利于用超大规模集成电路来实现．

Quasi-Monte-Carlo Gaussian particle filter based target tracking for the multiple passive sensor

This paper employs Quasi-Monte-Carlo (QMC) sampling to replace conventional Monte-Carlo (MC) sampling, thus improving the performance of the Gaussian Particle Filter (GPF). A multi-passive-sensor target tracking algorithm based on the Quasi-Monte-Carlo Gaussian Particle Filter (QMC-GPF) is proposed in connection with the multi-sensor centralized fusion strategy, which resolves the strong nonlinearity and weak observability problem in a multi-passive-sensor tracking system more efficiently. The algorithm not only reduces the computational complexity, but also improves the accuracy and stability of the tracking algorithm, thus getting fast convergence. Moreover, because of the parallel structure, which makes it easier to realize with large-scale integrated circuits.