Nonlinear filtering with multiple packet dropouts

This paper considers the nonlinear system filtering with packet dropouts. We assume that the packet arrived rate is known in advance but the sequence of packet dropouts is unknown. At first, we use the probability-weighted method to achieve a pseudo measurement sequence, and every pseudo measurement is the weighted value of the measurements acquired at the current time step and the prior time step. Some classical nonlinear filtering methods can be used via the pseudo measurement sequence and the dynamic equation of the system, and then the pseudo measurement unscented Kalman filter (PM_UKF) and the pseudo measurement particle filter (PM_PF) are given. This pseudo measurement sequence can be also used in the linear system, and its time complexity is lower than that of Sun's optimal filter at this time. Simulation results show the effectiveness of the proposed algorithms.

[1]  Nan Xiao,et al.  Optimal Filtering for Systems With Multiple Packet Dropouts , 2008, IEEE Transactions on Circuits and Systems II: Express Briefs.

[2]  Shu-Li Sun,et al.  Optimal Estimators for Systems With Finite Consecutive Packet Dropouts , 2009, IEEE Signal Processing Letters.

[3]  Sirish L. Shah,et al.  Optimal H2 filtering with random sensor delay, multiple packet dropout and uncertain observations , 2007, Int. J. Control.

[4]  Lihua Xie,et al.  Optimal linear estimation for systems with multiple packet dropouts , 2008, Autom..

[5]  Ling Shi,et al.  Kalman filtering over a packet-delaying network: A probabilistic approach , 2009, Autom..

[6]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[7]  Lihua Xie,et al.  Optimal Full-Order and Reduced-Order Estimators for Discrete-Time Systems With Multiple Packet Dropouts , 2008, IEEE Transactions on Signal Processing.

[8]  Ling Shi,et al.  Kalman filtering over a packet dropping network: A probabilistic approach , 2008, 2008 10th International Conference on Control, Automation, Robotics and Vision.

[9]  Yeng Chai Soh,et al.  Optimal Kalman filtering with random sensor delays, packet dropouts and missing measurements , 2009, 2009 American Control Conference.

[10]  Simon J. Godsill,et al.  An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo , 2007, Proceedings of the IEEE.

[11]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[12]  Hugh F. Durrant-Whyte,et al.  A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..

[13]  Y. Bar-Shalom,et al.  Tracking in a cluttered environment with probabilistic data association , 1975, Autom..

[14]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..