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.