摘要
以实现坦克对机动目标的有效跟踪为背景,针对传统Kalman滤波算法存在的计算量较大、需要先验信息较多的缺点,提出了一种基于神经网络的机动目标跟踪模糊Kalman滤波算法。在"当前"统计模型的基础上,将未知的目标机动加速度作为附加的过程噪声,使用模糊系统估计全部过程噪声的时变方差,利用神经网络对模糊系统中的参数进行优化。仿真结果表明了所提方法的有效性。
To achieve this goal mobile tanks right to the effective tracking the background of the traditional Kalman filter algorithm computation larger, more information prior to the shortcomings, a neural network-based mobile target tracking fuzzy Kalman filtering algorithm is presented. The ＂current＂ statistical model based on the unknown target mobile acceleration of the process as the additional noise, the use of fuzzy the systems estimate the entire processof the time-varying noise variance, use of neural network system of fuzzy the parameters are optimized. The simulation results show that the method is effective.
出处
《信息技术》
2008年第4期56-59,共4页
Information Technology
