In view of high computational complexity of the probability data association （PDA） algorithm in cluttered environments， a data association method based on the PDA algorithm is designed. When the number of measurement points in the wavegate exceeds a certain threshold， the PDA algorithm is employed to update the target state. When the number of measurement points falls below or equals the threshold， a nearest-neighbor approach is used to filter the target measurement points.Subsequently， the Kalman filter （KF） algorithm is utilized to achieve fast filtering updates in cluttered environments.Additionally， the paper proposes an adaptive interval smoothing method that dynamically corrects the smoothing interval to achieve reverse smoothing of the overall state estimation.This approach aims to improve the algorithm’s accuracy. Experimental results of various clutter environments demonstrate that the proposed method effectively enhances the estimation accuracy of the system state while ensuring tracking efficiency. Moreover， the results validate the robustness and effectiveness of the method compared to the PDA algorithm and the KF-PDA algorithm.