A PSO Accelerated Immune Particle Filter for Dynamic State Estimation

Particle Filter (PF) is a flexible and powerful Sequential Monte Carlo (SMC) technique to solve the nonlinear state/parameter estimation problems. The generic PF suffers due to degeneracy or sample impoverishment, which adversely affects its performance. In order to overcome this issue of the generic PF, a Particle Swarm Optimization accelerated Immune Particle Filter (PSO-acc-IPF) is proposed in this work. It combines the robustness and the diversified search capability of the Immune Algorithm (IA) and the speed and the computational efficiency of the Particle Swarm Optimization (PSO) in pursuing the global optimal solution. Mutation plays the key role in the proposed algorithm to help avoid the local optima and search for a global best solution. A two stage mutation operation is proposed. The first stage, with a high mutation rate, helps in exploring a larger solution space and the second stage, with a smaller mutation rate, helps in local optimal search. Later on, PSO is employed to accelerate the convergence speed. To validate the effectiveness of the proposed algorithm, its performance is compared with the generic PF and PSO Particle Filter (PSO-PF). The simulation results have demonstrated better robustness in state estimation for switching dynamic systems.

[1]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[2]  Wenzhong Guo,et al.  Particle Swarm Optimization Based on Information Diffusion and Clonal Selection , 2006, SEAL.

[3]  Xiao Zhi Gao,et al.  A Hybrid Optimization Algorithm based on Clonal Selection Principle and Particle Swarm Intelligence , 2006, Sixth International Conference on Intelligent Systems Design and Applications.

[4]  El-Ghazali Talbi,et al.  Metaheuristics - From Design to Implementation , 2009 .

[5]  B. Maddock,et al.  FROM DESIGN TO IMPLEMENTATION , 1982 .

[6]  Otman Basir,et al.  An efficient, effective, and robust decoding heuristic for metaheuristics-based layout optimization , 2006 .

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

[8]  Cheng-Jian Lin,et al.  Efficient Immune-Based Particle Swarm Optimization Learning for Neuro-Fuzzy Networks Design , 2008, J. Inf. Sci. Eng..

[9]  Leandro Nunes de Castro,et al.  The Clonal Selection Algorithm with Engineering Applications 1 , 2000 .

[10]  Russell C. Eberhart,et al.  The particle swarm: social adaptation in information-processing systems , 1999 .

[11]  Abdul-Rahim Ahmad,et al.  An intelligent expert system for decision analysis and support in multi-attribute layout optimization , 2005 .

[12]  D. Simon.  Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[13]  Keum-Shik Hong,et al.  An IMM Algorithm for Tracking Maneuvering Vehicles in an Adaptive Cruise Control Environment , 2004 .

[14]  Yanchun Liang,et al.  A Hidden Markov Model and Immune Particle Swarm Optimization-Based Algorithm for Multiple Sequence Alignment , 2005, Australian Conference on Artificial Intelligence.

[15]  Zheng Fang,et al.  A Particle Swarm Optimized Particle Filter for Nonlinear System State Estimation , 2006, 2006 IEEE International Conference on Evolutionary Computation.