一种基于IMM的自适应目标跟踪算法研究

Study on Adaptive Target Tracking Algorithm Based on Interactive Multi-Model

对目标进行自适应跟踪是节约雷达资源的途径之一。以相控阵雷达为基础研究了一种目标自适应跟踪算法。介绍了传统连续情况下的周期采样方法,通过设定上、下界来限制算法中采样周期的变化。并在连续采样的基础上给出一种改进的离散采样算法,改进算法通过比较滤波残差和量测误差设定一组离散采样值,计算量减小,跟踪误差降低。基于IMM对两种自适应采样算法以及固定周期采样算法进行Monte Carlo仿真对比,仿真结果表明两种自适应采样算法均大大降低了采样率,改进的离散自适应采样算法跟踪性能相对较好。

Adaptive target tracking is one of the ways to save radar resources.An adaptive target tracking algorithm based on phased-array radar was researched in this paper.Traditional continuous sampling algorithm was described,which limited the sampling period by setting upper and lower bounds.An improved discrete sampling algorithm was given based on the continuous sampling algorithm,which set a set of discrete sampled values by comparing the residuals and measurement error.The improved algorithm reduced the computational and the tracking error.Monte Carlo simulation comparisons between the two kinds of adaptive sampling algorithms and fixed-period sampling algorithm were carried out based on IMM.The result shows that the two kinds of adaptive sampling algorithms have greatly reduced the sampling rate,and the discrete adaptive sampling algorithm has relatively good tracking performance.