Application of an Improved Adaptive Kalman Filter in Photoelectric Tracking

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.