Application of adaptive forgetting factor RLS algorithm in target tracking
Liang Shan, Hao Chen, Jia Luan, Jun Li · 2017
In order to enhance the tracking performance of maneuvering target by forgetting factor RLS algorithm, an improved forgetting factor adaptive function is proposed based on cosine function. This adaptive function can adjust the forgetting factor dynamically according to the tracking error. In addition, a comprehensive predictor is designed, which dynamically selects the linear predictor or the square predictor in accordance with the tracking error. Firstly, without the consideration of noise, the fixed forgetting factors RLS algorithm with different predictors is used to track the same target, the simulation results show that the comprehensive predictor can improve the accuracy of target tracking. Secondly, with using the comprehensive predictor and the consideration of noise, the tracking effects of the fixed forgetting factor RLS algorithm and the improved adaptive forgetting factor RLS algorithm show that the improved forgetting factor adaptive function can effectively improve the accuracy and stability of target tracking.