基于“当前”统计模型的机动目标自适应跟踪算法

Adaptive Tracking Algorithm for Maneuvering Targets Based on Current Statistical Model

针对"当前"统计模型对加速度极值及机动频率需人为设定的缺陷,提出了一种基于滤波残差的高斯型隶属度函数改进算法,该法在跟踪过程中对加速度极值进行自适应调整。引入一种基于残差的Sigmoid二次型隶属度函数,通过自适应改变机动频率,使模型趋于目标真实状态。理论分析和仿真结果表明,改进的模型避免了人为引入误差,提高了机动目标的跟踪精度,算法简单,易于工程实现。

The current statistical model has the weakness of excessive reliance of limit acceleration and maneuvering frequency. Aiming at this issue, an improved algorithm based on the filtering residuals＇ Gaussian membership function is utilized. It can adaptively control the limit acceleration while tracking the target. A Sigmoid quadratic membership function is introduced based on the filtering residuals, and it makes the proposed model match the target real state closer by adjusting the maneuvering frequency. The- oretical analysis and simulation results show that the improved model avoids the human error and improves the tracking accuracy. The algorithm is easy and good for project implementation.