改进的自适应Kalman滤波在光电跟踪中的应用

Application of an Improved Adaptive Kalman Filter in Photoelectric Tracking

为解决机载激光通信中光电跟踪平台获得脱靶量存在滞后的问题,将协方差匹配技术与Sage-Husa自适应Kalman滤波相结合应用到脱靶量滞后补偿中。首先,通过Sage-Husa自适应Kalman算法补偿滞后的脱靶量,并引入遗忘滤波的思想降低过去的量测数据对现在的影响;然后,加入基于协方差匹配技术的判据,判据生效时更新噪声协方差阵,同时增大遗忘因子使量测先验协方差阵误差估计值与理论值更快地相容达到平衡,保证系统的实时性。在仿真中目标为等效正弦运动时改进的Kalman滤波比普通Kalman滤波降低了31.1%的预测误差。在实验验证中改进的Kalman滤波跟踪精度提高18.5%,实时性提高了18%,满足了控制系统对脱靶量滞后补偿的要求,使系统运行更稳定。

In this study,covariance matching technology is combined with a Sage-Husa adaptive Kalman filter to compensate for the lag in the miss distance obtained from a photoelectric tracking platform during airborne laser communication.First,a Sage-Husa adaptive Kalman algorithm is used to compensate for the off-target lagging,and the idea of forgetting filtering is introduced to reduce the influence of past measurement data on the present study.Subsequently,a criterion based on covariance matching technology is applied and if valid,the noise covariance matrix is updated and the forgetting factor is increased to accelerate the balancing of the estimated and theoretical values of the covariance matrix,thereby guaranteeing the real-time performance of the system.Based on the experimental results,the equivalent target sinusoidal motion in the simulation reduces the prediction error by 31.1%compared with the ordinary Kalman motion.Moreover,the tracking accuracy and real-time performance are increased by 18.5%and 18%,respectively,which meets the requirements for system control during off-target delay compensation and increases the stability of the system.