基于新最速下降法的目标跟踪算法

Target tracking algorithm based on a new steepest descent method

雷达目标跟踪滤波算法是雷达信号处理的重要组成部分,在空防预警、战场监视、导弹制导等领域起着重要的作用。本文提出了基于一种新最速下降法的目标跟踪算法。首先建立一种基于改进多项式拟合模型的运动描述模型,接着用一种新最速下降法来求解运动模型的最优参数,通过实时的最优运动模型对运动目标航迹进行预测跟踪,并采用正则化思想去除噪声影响。将本文算法与目前常用的交互多模型跟踪滤波算法进行对比,仿真结果表明在目标机动和非机动的情况下,本文算法的精度更高、计算量更小、实时性更好。

The radar target tracking filtering algorithm is an important part of radar signal processing,which plays an important role in air defense early warning,battlefield surveillance,missile guidance and other fields.This paper presents a target tracking algorithm based on a new steepest descent method.Firstly,a motion model based on the improved polynomial fitting model is established,and then a new steepest descent method is used to solve the optimal parameters of the motion model.The real-time optimal motion model is used to predict and track the moving target,and the regularization idea is used to remove the influence of noise.The simulation results show that the algorithm has higher accuracy,less computation and better real-time performance in the case of maneuvering and non-maneuvering targets.