基于高机动目标跟踪的改进变结构IMM算法

传统交互式多模型(interacting multiple model,IMM)算法在跟踪高机动目标时存在模型集合和真实系统模式匹配欠佳所导致状态估计质量下降的问题.基于变结构的思想及图论的知识,结合协方差匹配技术提出了一种补偿式变结构交互式多模型算法(compensation variable structure interacting multiple model,CPVSIMM).针对目前对直线加速状态估计问题,传统变结构交互式多模型(variable structure interacting multiple model,VSIMM)模型集多采用角速度作为模型特征参数造成对其估计性能欠佳的情况,所提算法联合加速度和角速度作为模型参数,并建立了模型集合间的有向图连通关系及模型子集自适应调整原则.理论分析与对比仿真表明:本算法融合估计结果的精度更高,同时能在一定程度上减小由于模型集匹配欠佳及切换不及时所导致的机动累积误差.

Improved Variable-structure IMM Algorithm Research Based on High Maneuvering Targets Tracking

When applying the traditional IMM algorithms to the state estimation problems of high maneuvering targets,it may face the difficulty of estimation degradation caused by the mismatch between the prior model sets and the real models. Based on the idea of variable structure and graph theory knowledge,this paper proposes a compensation variable structure interacting multiple model( CPVSIMM)algorithm by combining with covariance-matching technique. By analyzing the condition of estimation degradation to uniformly accelerated linear motion because of traditional VSIMM only selecting angular velocity as the model parameters,this proposed algorithm selects acceleration and angular velocity as the model parameters,and establishes digraph connectivity relationships and the principle of adapting model subset. Theoretical analysis and comparison of simulation results show that it can achieve higher fusion estimation accuracy,and reduce the cumulative error due to model sets mismatch and not timely switch.