改进的基于“当前”统计模型自适应滤波算法及其在航迹预测中的应用

基于"当前"统计模型的模糊自适应(FACS)滤波算法利用机动目标"当前"加速度调整加速度极限值,实现了对一般机动目标的有效预测;但是在预测强机动目标时却存在较大的预测误差。为了解决这一问题,引入强跟踪滤波器(STF),提出了一种新的自适应滤波算法STF-FACS。该算法根据滤波残差实时调整卡尔曼滤波增益,提高了对强机动目标的预测能力;同时保留了FACS算法对于一般机动目标的预测性能。最后,对强机动目标分直线机动和转弯机动,分别进行航迹预测仿真。仿真结果表明,对弱机动目标进行航迹预测时,两种算法的预测效果相当;对强机动目标进行航迹预测时,STF-FACS算法无论是在动态时延和预测精度方面都比FACS算法要好。

Improved Adaptive Filtering Algorithm Based on Current Statistical Model and its Application in Track Prediction

The fuzzy adaptive filtering algorithm based on current statistical model(FACS) can not predict strong maneuvering targets precisely,though it has a good prediction accuracy for general maneuvering targets by using current acceleration to adjust the upper and lower limits of acceleration.In order to solve this problem,a new adaptive filtering algorithm STF-FACS was presented by introducing a strong track filter(STF).This algorithm,using filtering residual to adjust the kalman filter gain,can predict strong maneuvering targets well,and has a good performance for predicting general maneuvering targets as the algorithm FACS.Finally, strong maneuvering targets are divided into linear maneuvering targets and turning maneuvering targets,and track prediction simulations about them are did.Simulation results show that,when there is a feeblish maneuver,the performance of the two algorithms is the same.when there is a strong maneuver,the performance of the STF-FACS is much better than the FACS in both convergence rate and prediction accuracy.