Particle filter for nonlinear systems with multi-sensor asynchronous random delays  

作　　者：Junyi Zuo Xiaoping Zhong 

机构地区：[1]School of Aeronautics,Northwestern Polytechnical University,Xi’an 710072,China

出　　处：《Journal of Systems Engineering and Electronics》2017年第6期1064-1071,共8页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(61473227;11472222);the Fundamental Research Funds for the Central Universities(3102015ZY001);the Aerospace Technology Support Fund of China(2014-HT-XGD);the Natural Science Foundation of Shaanxi Province(2015JM6304);the Aeronautical Science Foundation of China(20151353018)

摘　　要：This paper is concerned with the recursive filtering problem for a class of discrete-time nonlinear stochastic systems in the presence of multi-sensor measurement delay. The delay occurs in a multi-step and asynchronous manner, and the delay probability of each sensor is assumed to be known or unknown. Firstly, a new model is constructed to describe the measurement process, based on which a new particle filter is developed with the ability to fuse multi-sensor information in the case of known delay probability.In addition, an online delay probability estimation module is introduced in the particle filtering framework, which leads to another new filter that can be implemented without the prior knowledge of delay probability. More importantly, since there is no complex iterative operation, the resulting filter can be implemented recursively and is suitable for many real-time applications. Simulation results show the effectiveness of the proposed filters.This paper is concerned with the recursive filtering problem for a class of discrete-time nonlinear stochastic systems in the presence of multi-sensor measurement delay. The delay occurs in a multi-step and asynchronous manner, and the delay probability of each sensor is assumed to be known or unknown. Firstly, a new model is constructed to describe the measurement process, based on which a new particle filter is developed with the ability to fuse multi-sensor information in the case of known delay probability.In addition, an online delay probability estimation module is introduced in the particle filtering framework, which leads to another new filter that can be implemented without the prior knowledge of delay probability. More importantly, since there is no complex iterative operation, the resulting filter can be implemented recursively and is suitable for many real-time applications. Simulation results show the effectiveness of the proposed filters.

关 键 词：particle filter nonlinear dynamic system state estima tion measurement delay multiple sensors 

分 类 号：TN713[电子电信—电路与系统]