一种带机动检测的权值约束多新息修正算法

A Weight-Based Multi-innovation Amendment Algorithm with Maneuvering Detection

目标运动状态的突变会导致跟踪算法精度大幅降低。为了提高对目标机动阶段的跟踪性能,提出了一种带机动检测的权值约束多新息修正算法。首先,为了准确判断机动时机,提出了一种双误差椭圆的机动检测算法,通过设置双边门限,加强算法对机动的敏感度;然后,为了降低因延迟修正造成的机动误差,以预测量测与真实量测间的欧氏距离为基础,建立距离与权值间的映射关系,从而获得之前修正信息的权值以加大对之前隐含信息的利用率;最后,通过3种场景下的仿真分析说明所提算法的有效性,并经过与标准卡尔曼滤波及自适应渐消卡尔曼滤波的对比,证明所提算法在跟踪强机动目标及弱机动目标情况下均具有较高的费效比。

The performance of tracking algorithm may be seriously degraded due to the sudden state change of target. To improve the performance for maneuvering target tracking, a weight-based multi-innovation amendment algorithm with maneuvering detection is put forward. Firstly, in order to judge the moment of maneuvering precisely, we proposed a maneuvering detection algorithm based on the double error ellipses, which can increase the sensitivity to maneuvering by setting bilateral threshold. Then to decrease the maneuvering deviation caused by delayed amendment, we built up a mapping relationship between the distance and the weight based on Euclidean distance between the predicted measurement and the actual measurement. From the mapping relationship, the weight of the pre-amendment information was obtained and the utilization ratio of implicit information was improved. Finally, the simulation results of three scenarios indicated that the proposed algorithm is effective. Compared with the standard Kalman filter and adaptive fading Kalman filter, the proposed method has a higher cost-benefit ratio for both highly and weakly maneuvering target tracking.