针对概率数据互联(Probability data association,PDA)算法在杂波环境下计算复杂度高的问题,设计了一种基于PDA算法的数据关联方法,当波门内量测点数量大于阈值时,采用PDA算法更新目标状态;当波门内量测点数量小于等于阈值时,采用最近邻思想筛选目标量测点,接着利用卡尔曼滤波(Kalman filter,KF)算法实现杂波环境下的快速滤波更新.在此基础上,通过自适应区间平滑方法,动态修正平滑区间,实现整体状态估计的反向平滑,从而提升算法的精度.不同杂波环境下的实验结果表明,本文方法相较于PDA算法与KF-PDA算法,在保证跟踪效率的同时,有效提升了系统状态的估计精度,验证了该方法的鲁棒性和有效性.
Single Target Tracking of Ships Based on Adaptive Smoothing KF-PDA Algorithm
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.
adaptive smoothing intervalKalman filtering algorithmprobability data association(PDA)algorithmstate estimation
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