Genetic Interacting Multiple Model Algorithm Based on $H_{\infty}$ Filter for Maneuvering Target Tracking

• Published in: International journal of control, automation, and systems Vol. 9; no. 1; pp. 125 - 131
• Main Authors: Ma, Haiping, Ruan, Xieyong, Pan, Zhangxin
• Format: Journal Article
• Language: Korean
• Published: 2011
• Subjects: interacting multiple model; maneuvering target tracking; Genetic algorithm; H_{\infty}$ filter
• ISSN: 1598-6446; 2005-4092

In order to have a good performance for maneuvering target tracking, a genetic interacting multiple model (GIMM) algorithm based on the $H_{\infty}$ filter is proposed in this paper. It introduces the $H_{\infty}$ filter as model-conditional filter, which keeps its robustness by constantly adjusting parameters, to improve the performance and the precision. Meanwhile, it optimizes model probabilities using the genetic algorithm (GA), chooses sub-models which are close to true models from a set of models, adjusts the number of models and parameters in real-time, reduces excessive competition, and improves the performance of the algorithm. The simulation results indicate that, the algorithm has higher tracking accuracy and stronger robustness than the standard IMM algorithm.