Research on target localization in airborne electro-optical stabilized platforms based on adaptive extended Kalman filtering

To address the problem of inaccurate process noise affecting the accuracy of target positioning in airborne electro-optical stabilized platforms, a novel adaptive extended Kalman filter is proposed. Firstly, this paper employs a classification method to estimate of process noise covariance based on the range values between the target and the unmanned aerial vehicle. Secondly, this noise estimation strategy is combined with the extended Kalman filter algorithm, resulting in an adaptive extended Kalman filter based on uncertain process noise estimation. Finally, the proposed algorithm is validated through simulations and flight tests. The simulation results demonstrate that the accuracy of the algorithm proposed in this paper is respectively improved by 50 % compared to the least squares algorithm and by 20 % compared to the Kalman filter algorithm. The results of actual flight tests reveal that this algorithm significantly improves the positioning accuracy of static ground targets. The error rate is 25 % lower than that of the other current algorithms. It has enormous guiding significance for engineering applications.