Efficient Particle Swarm Optimized Particle Filter Based Improved Multiple Model Tracking Algorithm

To meet the requirements of modern radar maneuvering target tracking system and remedy the defects of interacting multiple model based on particle filter, noninteracting multiple model (NIMM) and enhanced particle swarm optimized particle filter (EPSO‐PF) are proposed. The improved maneuvering target tracking algorithm (NIMM‐EPSO‐PF) in this article combines the advantages of NIMM with those of EPSO‐PF. NIMM is used to figure out the index of particles to avoid the high computing complexity resulting from particle interaction, and EPSO‐PF can not only improve the equation of particle update through the rules individuals develop an understanding of group but also enhance particle diversity and accuracy of particle filter through the small variation probability of superior velocity. Besides, the random assignment of inferior velocity is capable of upgrading filter efficiency. As shown by the experimental result, the NIMM‐EPSO‐PF not only improves target tracking accuracy but also maintains high real‐time performance. Therefore, the improved algorithm can be applied to modern radar maneuvering target tracking field efficiently.

[1]  Min Li,et al.  Prognostics of Lithium-Ion Batteries Based on the Verhulst Model, Particle Swarm Optimization and Particle Filter , 2014, IEEE Transactions on Instrumentation and Measurement.

[2]  Yacine Morsly,et al.  Genetic Algorithm combined to IMM approach for Tracking Highly Maneuvering Targets , 2008 .

[3]  Zeng Qing-shuang.  Maneuvering Target Tracking Using IMM-PF with Doppler-Aided Measurement , 2011 .

[4]  Baiqing Hu,et al.  Huber-based novel robust unscented Kalman filter , 2012 .

[5]  Yin Ze-jie.  Enhanced multiple model particle filter for maneuvering target tracking , 2010 .

[6]  G. Hewer,et al.  Robust Preprocessing for Kalman Filtering of Glint Noise , 1987, IEEE Transactions on Aerospace and Electronic Systems.

[7]  P. H. Foo,et al.  Combining the interacting multiple model method with particle filters for manoeuvring target tracking , 2011 .

[8]  XU Xin-he.  Particle swarm optimized particle filter , 2007 .

[9]  Ming Xin,et al.  Adaptive multi-cue based particle swarm optimization guided particle filter tracking in infrared videos , 2013, Neurocomputing.

[10]  Haitao Wang,et al.  A New Particle Filter Based on Differential Evolution Method: A New Particle Filter Based on Differential Evolution Method , 2011 .

[11]  Jeroen D. Hol,et al.  Resampling in particle filters , 2004 .

[12]  H.A.P. Blom,et al.  Exact Bayesian and particle filtering of stochastic hybrid systems , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[13]  Jing Lei,et al.  Dynamic inversion in electrical capacitance tomography using the ensemble Kalman filter , 2012 .

[14]  Yongli Wang,et al.  Measuring the uncertainty of RFID data based on particle filter and particle swarm optimization , 2012, Wirel. Networks.

[15]  Ying Li,et al.  Improved particle swarm optimization algorithm for fuzzy multi-class SVM , 2010 .

[16]  Yihua Yu,et al.  Particle filter with ant colony optimization for frequency offset estimation in OFDM systems with unknown noise distribution , 2011, Signal Process..

[17]  Quan Pan,et al.  Estimation of Markov Jump systems with mode observation one-step lagged to state measurement , 2007, 2007 10th International Conference on Information Fusion.

[18]  Junping Du,et al.  PHD filter for multi-target tracking with glint noise , 2014, Signal Process..

[19]  George W. Irwin,et al.  Multiple model bootstrap filter for maneuvering target tracking , 2000, IEEE Trans. Aerosp. Electron. Syst..

[20]  Jie Yang,et al.  Adaptive multi-feature tracking in particle swarm optimization based particle filter framework , 2012 .

[21]  Hui Li,et al.  Particle filter for manoeuvring target tracking via passive radar measurements with glint noise , 2012 .