基于改进“当前”统计模型的目标跟踪算法

A Tracking Algorithm Based on Improved Current Statistical Model

提出一种基于改进"当前"统计模型的目标自适应跟踪算法。针对"当前"统计模型自适应算法对机动加速度极限值有依赖,对弱机动目标跟踪精度不高的问题,采用一种简单的加速度方差自适应调整公式加以克服,在此基础上融合隶属函数对其进行加权改进。为克服算法中自相关时间常数难以选取问题,将不同自相关时间常数的"当前"统计模型在交互式多模型框架内进行交互。仿真结果表明,无论对于强机动目标还是弱机动目标,新算法都具有较好的跟踪效果。

An adaptive tracking algorithm based on improved current statistical model is proposed.One simple adaptive formula for acceleration variance is proposed to improve the current statistical model and adaptive filtering algorithm that are dependent on the limit acceleration and have a lower precision in tracking weak maneuvering targets.Also,the membership function is used to adjust the formula.Then,multiple autocorrelation constant current statistical models are interacted under the configuration of interacting multiple model（IMM）,which can solve the choice problem about autocorrelation constant.Simulation results show that the proposed algorithm can effectively track the strong or weak maneuvering target.