神经网络的单滤波器目标跟踪算法

摘要

当前统计模型及其自适应滤波(CSMAF)算法是机动目标跟踪中的一种有效方法.但该方法对目标机动加速度极限值有依赖,并且对弱机动目标跟踪的精度不高.为解决这一问题,利用一种改进的加速度方差自适应调整公式克服了对加速度极限值的依赖,同时利用神经网络对滤波参数信息进行融合,自适应调整过程噪声.仿真结果表明,该方法有很好的机动适应性,对目标的跟踪精度较高.%The Current Statistical Model and Adaptive Filtering (CSMAF) algorithm is one of the most effective methods for tracking the maneuvering targets. However, its performance suffers from the shortcoming of its heavy dependence on the choice of limit acceleration. Also, it has a lower precision in tracking the weak maneuvering target. For solving this problem, one improved self-adaptive formula for acceleration variance is proposed to overcome the dependence on the limit acceleration. At the same time, the neural network is used to fuse the filtering parameters information and self-adjust the process noise. Simulation results show that the proposed method has better adaptability for maneuvering and has higher precision for tracking.