一种强机动目标自适应跟踪算法

An Adaptive Track Method for High Maneuvering Target

针对强机动目标跟踪精度不高的问题,提出了一种强机动目标自适应跟踪算法(HMIMM-CV/CAT)。首先通过机动检测区别目标的机动性能,分别应用Kalman滤波和交互多模算法对目标进行跟踪。其次对机动段交互多模算法,给出一组转弯模型离散模型集,在目标机动时通过角速度估计在离散模型集中遴选出一个最匹配的模型参加交互计算,使模型更加逼近目标真实运动模式,且不增加参与交互运算模型数量。蒙特卡罗仿真结果表明,该算法与几种类似算法相比,更加适用于强机动目标。

A new adaptive track method （HMIMM-CV/CAT） is introduced to improve the tracking precision for high maneuvering target. First, by maneuvering test to distinguish the target in the non- motorized segment or motorized segment, Kalman filter algorithm and interactive multi-mode are applied, respectively, to track the target. Second, an optimal model is selected from the discrete model set by estimating angular velocity so as to match the real system mode in the maneuvering segment, meantime, keep the number of model on the low side. The Monte Carlo simulation results show that the proposed algorithm is much suitable for the maneuvering tracking, compared with some similar algorithms.