基于S修正卡尔曼滤波的自适应网格模糊交互多模型算法研究

Research on algorithm of adaptive grid and fuzzy interacting multiple model based on S-amended kalman filter

针对机动目标跟踪问题中,固定结构多模型(FSMM)算法费效比不高以及交互式多模型(IMM)算法马尔可夫转移概率难以准确确定的问题,研究一种基于S修正卡尔曼滤波的自适应网格模糊交互式多模型(AGFIMM-SKF)算法。该算法通过自适应网格调整实现了模型集自适应,通过模糊逻辑推理得到模型集中各个模型的匹配度,并且对标准卡尔曼滤波器进行S修正。仿真结果表明,AG-FIMM-SKF算法与标准的IMM算法相比,可以有效提高多模型算法的精度和费效比,且适合工程应用。

This paper studies on an algorithm of adaptive grid and fuzzy interacting multiple model based on S-amended Kalman filter （AG-FIMM-SKF） for maneuvering target tracking,aiming at the problems of FSMM algorithm＇s cost-efficiency ratio being not high and the Markov transition probability of the interacting multiple model algorithm （IMM） being difficult to determine exactly.This algorithm realizes adaptive model set by adaptive grid adjustment,obtains each model matching degree in model set by fuzzy logic inference and uses S-amended method to improve standard Kalman fliter.The simulation results show that AG-FIMM-SKF algorithm can effectively improve the accuracy and cost-efficiency ratio of the multiple model algorithm,and is suitable for engineering applications.