改进的“当前”统计模型变采样率目标跟踪算法

为满足实际雷达系统对高精度和高实时性的要求，提出了一种改进的“当前”统计模型变采样率机动目标跟踪算法。该算法针对“当前”统计模型必须预设加速度极值和机动频率的问题，提出一种加速度方差和机动频率在线同步自适应方法，建立改进的“当前”统计模型机动目标跟踪算法；针对在线自适应方法计算量大的问题，结合采样周期的大小与目标机动特性的关系，引入变采样率方法。仿真结果表明，与传统“当前”统计模型相比，改进的“当前”统计模型机动目标跟踪算法能显著提高对不同机动强度目标的跟踪精度；变采样率方法通过减少采样点数，节省了系统资源，提高了跟踪实时性；所提算法将两者结合，用传统的“当前”统计模型1.5～2倍的平均采样周期得到了更小的位置均方根误差，实现了用单模型方法同时改善跟踪精度和实时性的目的。

Adaptive sampling period algorithm for target tracking based on improved current statistical model

To satisfy the demand of high tracking accuracy and real-time ability in actual radar system,an adaptive sampling period algorithm for maneuvering target tracking based on improved current statistical model is designed. In this algorithm,according to the problem that the current statistical model needs to preset the maximum acceleration and the maneuvering frequency,an improved current statistical model filtering algorithm is built by introducing the methods of the adaptive acceleration variance and the adaptive maneuvering frequency. For the problem of large calculation amount in the improved current algorithm,adaptive sampling period algorithm is cited. The adaptive sampling period algorithm is introduced by combining the sampling period with maneuvering performance. The simulation indicates that the improved current statistical model filtering algorithm can enhance the adaptability of single model method for target tracking significantly,and the adaptive sampling period algorithm can improve real-time ability by saving system resource during decreasing the sampling number;the purposed algorithm which combines the two algorithms above,improves the tracking accuracy by using 1.5~2.0 times average sampling period,and realizes the goal of improving the tracking accuracy and real-time ability simultaneously.