机载单站非等间隔轨迹优化滤波模型的研究

Research on non-equal interval optimal course filtering model for single airborne observer

摘要:

针对在机载单站ESM传感器对慢速运动目标跟踪中,由侦收数据率不稳定、可观测性弱及受载机运动轨迹影响导致的定位精度低等问题,提出一种基于漂移瑞利滤波器的非等间隔轨迹优化滤波模型.该模型通过实时更新采样间隔解决侦收数据不稳定的难题,同时基于位置协方差矩阵迹最小准则,预估计观测平台的最优运动方向,进而避免了由相对位置不当引发的发散问题,提高系统的定位精度.仿真实验表明在最大采样间隔不同的情况下,该滤波模型能够保持较好的跟踪稳定性,并且收敛速度较快,能够有效减小定位误差.

Abstract:

To solve the problem of low positioning accuracy issue due to trajectory effect of the observer,instable surveillance data receive rate and weak observability during slow moving target tracking by the single-airborne ESM,a course optimizing filtering model under non-equal interval data scenarios based on shifted Rayleigh filter was proposed in this paper,which updated the sampling interval changes in real-time to resolve the problem of instable bearing samplings.It also estimated the optimal movement direction of the observation platform based on the minimum trace of the location covariance matrix,which avoided divergence owing to improper relative position and enhanced positioning accuracy.The simulation results indicate that,in addition to the maintained good tracking stability,the proposed algorithm has fast convergence characteristics,which effectively reduces the positioning error.