针对机动目标在多帧积累检测算法中存在的问题展开讨论与研究,提出了基于自适应状态转移集合的多帧积累检测与跟踪算法,并设计了相应的仿真实验验证算法的有效性。针对目标机动性较强时算法性能严重下降的问题,详细介绍相应的改进策略,使用当前统计模型提高对机动目标的适应能力。利用值函数积累过程中的目标预测路径实时估计目 标的转移加速度并进行加速度约束,有效地减小目标状态的搜索空间。通过转移加速度在时间维上的相关性结合当前统计模型,自适应地调整目标状态转移集合的范围,进而减少目标搜索空间内的干扰信息,提高算法的检测精度与计算效率。利用不同机动目标场景,仿真结果证明所提改进算法具有更好的检测与跟踪性能。

In view of the problems existing in the multi-frame accumulation detection algorithm of maneuvering target, a multi frame accumulation detection and tracking algorithm based on adaptive state transition set is proposed, and the corresponding simulation experiments are designed to verify the effectiveness of the algorithm. Aiming at the existing problems, the corresponding improvement strategies are introduced in detail, and the current statistical model is used to improve the adaptability to the maneuvering target. In order to effectively limit the search space of the target state, the target prediction path in the process of value function accumulation is used to estimate the transfer acceleration of the target in real time and constrain the acceleration. By combining the correlation of transfer acceleration in time dimension with the current statistical model, the range of target state transfer set is adaptively adjusted,so as to reduce the interference information in target search space and improve the detection accuracy and calculation efficiency of the algorithm. Finally, the improved algorithm and the existing algorithm are simulated in different maneuvering target scenarios. The simulation results show that the improved algorithm proposed in this chapter has better detection and tracking performance.

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