基于变步长增益调整的机动目标跟踪新算法

针对Singer模型的缺陷和“当前”统计模型存在的对弱机动目标跟踪能力较差的缺陷进行了改进,设计了一种新的加速度自适应模型;利用该模型设计出新的机动目标跟踪滤波算法,该算法对机动目标跟踪的综合性能有了较大的提高。在此基础上,为了减少滤波增益矩阵的计算量,使算法易于微机工程化实现,提出对滤波增益矩阵进行变步长调整的新方法,即通过在线检测算法确定何时有必要进行滤波增益的调整,而不需要每一步都计算增益矩阵,从而较多地降低了滤波算法的计算量。通过以上两个方面的改进,不仅提高了机动目标跟踪的精度,而且提高了目标跟踪的快速性和实时性。仿真验证表明该算法有良好的跟踪性能,而且计算量小,易于微机工程化实现。

A new algorithm based on variable-step gain adjustment for maneuvering target tracking

Considering the limitation of Singer Model and the limitation of the current statistical model in less-maneuvering target tracking, an accelerated adaptive tracking model is designed for improvement. The synthetical tracking capability is enhanced using the filter algorithm based on this model. Besides, filter gain matrix is adjusted with variable-step algorithm, i.e. the filter gain adjustment is decided through an on-line monitoring algorithm, and the computation overheads of the filter algorithm are reduced greatly. After those improvements, the precision and the real-time performance of maneuvering target tracking are improved. The simulation results show that this algorithm is effective.