中文摘要：

针对系统状态估计、目标跟踪等是包含多源不确定性信息的非线性非高斯随机过程,提出了一种基于信赖域的序贯拟蒙特卡洛（Sequential Quasi-Monte Carlo,SQMC）滤波算法.该算法利用拟蒙特卡洛积分技术优化采样粒子在状态空间的分布特性,降低了滤波过程中的积分误差,提高了状态估计精度;同时,利用信赖域（Trust Region,TR）方法将采样粒子向高似然区域移动,减少了所需采样粒子的数目,降低了算法复杂度.实验结果表明：该算法有效克服了粒子贫乏和拟蒙特卡洛滤波计算复杂度高的问题,且在非线性系统状态估计精度以及目标跟踪的准确性上要优于粒子滤波和拟蒙特卡洛滤波等现有算法.

英文摘要：

A trust region based sequential quasi-Monte Carlo filter is proposed for system state estimation and object tracking which are the non-linear and non-Gaussian random procedures with multi-source uncertain information.In the proposed algorithm,the quasi-Monte Carlo（QMC） technique is used to optimize the distribution of the sampling particles in the state space,which can obtain a small error of the integration in the filtering process and a better accuracy of the state estimation.Furthermore,a trust region（TR） procedure is used to move particles to regions of high likelihood,which results in a fewer particle selection and lower computational cost.Experimental results show that the proposed algorithm overcomes the particle impoverishment,reduces the computational complexity of the QMC filter,and gets a more accuracy estimation than existing algorithms such as particle filter and QMC filter in system state estimation and object tracking.