改进的当前统计模型及自适应跟踪算法

对于机动目标跟踪问题，在当前统计（CS）模型的基础上，提出了一种新的机动目标自适应跟踪算法。通过引入强跟踪滤波器（STF）的渐消因子，增强了模型对目标突发机动的自适应跟踪能力，同时针对模型对目标加速度极限值的依赖性这一缺点，引入一种利用位置估计值与加速度的函数关系自适应调整加速度方差的方法，提高了对弱机动和非机动目标的跟踪能力。仿真结果表明，该算法与标准的当前统计模型滤波算法相比具有较高的跟踪精度。

A Modified Adaptive Tracking Algorithm Based on Current Statistic Model

Based on the current statistical（CS） model, a new adaptive maneuvering target tracking algorithm is presented. By introducing a fading factor of Strong Tracking Fiher（STF）, this algorithm improves the adaptive tracking performance greatly when there is a sudden maneuver. Being dead against the disadvantage of the model depending on the target acceleration limit value, a method using the relation of location estimation and acceleration is presented, which can adjust acceleration variance adaptively. The method improves the tracking performance when there is a thin maneuver or no maneuver. Simulation results indicate that this improved algorithm has better performance in target tracking than normal current statistical model.