基于卡尔曼滤波和模糊数据融合的跟踪算法

A Target Tracking Algorithm based on Fuzzy Data Fusion and Kalman Filter

在目标跟踪系统中,特别是在复杂背景情况下对地面目标的跟踪中,传统相关算法采用全局搜索的方法,使得计算量相当大,不易实时实现,而且当发生目标局部遮挡时,目标容易丢失。为此,提出一种基于模糊推理和卡尔曼预测器的目标相关跟踪的方法,它充分利用卡尔曼预测器的预测功能来预测下一帧目标可能出现的区域,然后在较小的预测区域中进行相关匹配运算,找到最佳相关匹配点,跟踪更具主动性,同时用模糊推理方法对卡尔曼预测器的参数进行自适应调整,从而可以跟踪各种机动目标。实验中用传统算法和本算法对高速行驶的坦克进行跟踪时,传统算法容易跑飞,而本算法不受遮挡干扰,始终稳定跟踪且耗时大幅减少,且能够跟踪机动速度大幅变化的目标。

The correlation is an usually method in target tracking system, especially in the complex background. But there is a problem in traditional matching method that the method searches the target in the whole area, so it can＇t be realized in real time because of large amounts of calculation. And when the target is partial shaded, the target will be lost on tracking. To solve the problem, this paper presented a new approach based on the Fuzzy Reasoning and Kalman Filter to realize target tracking. And this algorithm used greatly the forecast function of Kalman Filter to make target tracking more active. And the Fuzzy Reasoning Method can automatically adjust the parameters of Kalman Filter. In experiment, the traditional method always lose the tank when the tank is at a high speed, and the method of the paper can steadily track it.