机动目标跟踪双滤波器模型及自适应算法

Tracking a Maneuvering Target with Sliding Acceleration Meanvalue Model and Algorithm

中文关键词  双滤波器模型  机动目标跟踪  自适应滤波

英文关键词  sliding mean-value  maneuvering target tracking  adaptive filtering

中文摘要

      现代机动目标跟踪的困难来自跟踪的快速性与精度在一定计算负荷约束下的协调难以令人满意，考虑依次处理快速性与精度的方案，采用滑动均值均匀分布描述目标的随机机动特性，分别采用宽带的均值预估滤波器和窄带的跟踪滤波器串联，实现机动加速度大范围变动或突变的精确、快速跟踪。双滤波器的计算量适中、易于工程实现。对各种运动形式进行计算机模拟表明，这类算法对高度机动或弱机动或无机动均可给出较好的目标位置、速度及加速度估值。

英文摘要

      Based upon the nonzero mean-value time correlation model, a sliding acceleration meanvalue model and algorithm using two Kalman filters in series is put forward. The first filter is designed to cope with all possible target maneuvers and gives out a sliding mean-value of acceleration. Using the sliding mean-value as the input, the parameter of the second filter can be controlled adaptively to match the real states of the maneuvering target. This method widen the changeable range of target maneuvering acceleration with high-precision of state estimation comparing with the ordinary nonzero mean-value time correlation model, such as “current” model. There are also no problems of the correlated measurement noise and correlated estimates of the two filters in early research work. Results from computer simulations are included to demonstrate the performance.