诱偏干扰环境下被动雷达导引头数据灰色处理方法

A Grey Processing Approach of Passive-Radar-Seeker in Active-Decoying Environment

  • 摘要: 有源诱偏干扰是反辐射导弹(ARM)面临的主要威胁,ARM抗诱偏干扰能力是干扰环境下ARM作战效能评估的重要指标。针对诱偏干扰下被动雷达导引头(PRS)测角精度与稳定度不高的问题,提出一种数据灰色处理方法:首先通过量测转换将由于弹道变化而时时发生改变的指向角度信息转换成相对固定的目标位置信息,然后利用数据加窗提高测量值的稳定度,获取相对稳定的测量样本集,最后采用基于灰色距离测度与灰熵定义的数据灰色处理方法,完成异常值剔除与目标位置点估计,达到剔除异常值、抑制测量随机噪声、提高PRS测量精度与稳定度的效果。仿真实验表明该方法可以有效提高PRS测量的精度与稳定度,从而大大提高ARM的抗诱偏干扰能力。

     

    Abstract: For confronting the anti-radiation missiles (ARM), Aerial defence Radar often uses Active-Decoying. It is possible that ARM confronts with active-decoying, when high-resolution measurement capability of the spatial spectrum direction-of-arrival (DOA) estimation is used by passive-radar-seeker(PRS). To improve the precision and the stabilization of the angle-measure in the active-decoying environment, a grey processing approach of the passive-radar-seeker is proposed in this paper. The approach based on grey theory and norm, from the view of the topology of the sample space and the distances between samples. Using approach of converted measurements and data windows get the sample space. Using grey data processing approach based on the definitions of grey distance measure and grey relation entropy, eliminate singularity and retain random error. Finally, the simulation test verifies that the grey processing approach can improve the precision and stabilization of anglemeasurer in active-decoying environment, and can improve the precision of ARM.

     

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