基于斜率约束和回溯搜索的水下多目标跟踪方法

针对复杂水域环境下的多目标跟踪问题,本文提出了一种基于斜率约束和回溯搜索的多目标跟踪方法。首先,基于方位测量数据和水下目标运动学分析,利用门限阈值的方法检测目标。然后,基于传统多假设跟踪算法框架设计一种新的斜率约束和共用量测的假设生成规则。在航迹中断时,通过回溯搜索的方法确定中断起始航迹点,利用容积卡尔曼滤波对中断航迹预测和补偿,同时对假设生成结果减枝,以达到降低算法空间复杂度的目的。试验结果表明:该方案能够实现多目标自动关联跟踪、中断航迹自动预测、自动航迹终止等任务,目标跟踪平均均方根误差0.594 4°,算法平均运行时间0.826 5 s。

A multi-target passive tracking method based on slope constraint and retrospective searching

To achieve multi-target tracking tasks in complex water environment, a multi-target tracking method based on slope constraint and backtracking search strategy is proposed. Firstly, the threshold detection method is used to extract targets based on the bearing-time recording (BTR) data and the underwater kinematics analysis. Then, a novel hypothesis generation rule is proposed under the frame of traditional multi-hypothesis tracking (MHT) algorithm, which could be termed as slope constraint and measurements sharing. When the trajectory is interrupted, the retrospective search method is used to determine the interruption starting track point of targets. In addition, the cubature Kalman filter (CKF) is utilized to predict and compensate the interrupted trajectory. The hypothesis generation result is reduced to optimize the space complexity of algorithm. Experimental results show that this strategy can complete tasks such as multitarget automatic association tracking, interrupted track automatic prediction, and automatic track termination. The the average root mean square error of target tracking is 0. 594 4°, and the average running time of the algorithm is 0. 826 5 s.