基于当前统计模型的水下目标自适应跟踪算法

An Adaptive Tracking Algorithm of Underwater Target Based on Current Statistical Model

为实现水下机动目标的精确跟踪,提出了一种基于当前统计模型的水下目标自适应跟踪算法。该算法引入当前统计模型作为模型基础,并结合卡尔曼滤波算法,通过自适应调整滤波增益,对目标当前状态进行实时估计,较为准确地反映水下目标实际机动特性,解决了传统卡尔曼滤波算法不能有效跟踪水下目标转弯、变速等复杂机动情况的问题。仿真结果表明,在6000 m×5000 m×3000 m的水下三维空间内,算法跟踪效果较好,有效实现了对水下机动目标的精确跟踪。

In order to achieve the accurate tracking of underwater maneuvering targets, an adaptive tracking algorithm based on current statistical model is proposed in this paper. Introduced current statistical model and combined with Kalman filtering algorithm, the filtering gain can be adjusted adaptively to real-time estimate current state. It accurately reflects the characteristics of underwater maneuvering targets and solves drawbacks of the traditional Kalman filtering in tracking the turning, speed change and other complex of underwater maneuvering targets. Sinaulation results show that, in the three-dimensional space of 6000m×5000m×3000m, the proposed algorithm has the better effect and achieves the accurate tracking of underwater maneuvering targets.