雷达探测器的峰值拾取损失

A. Yildirim, M. Efe, A.K. Ozdemir
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引用次数: 5

摘要

本文分析了传统雷达信号处理器在采用点目标模型进行检测和跟踪的情况下产生的拣峰损失。通过仿真表明,在点目标假设下,高分辨率雷达的性能下降可能是显著的,其中目标扩展到多个探测单元。在检测到的峰值附近插值数据只提供了轻微的改进。本文提出了一种极大似然估计器(MLE)来减少峰值拾取损失。通过将估计量的方差与本文导出的Cramer Rao下界的方差进行比较,证明了极大似然估计量显著地减少了拣峰损失
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Peak picking losses in radar detectors
In this paper we analyze the peak picking losses induced by conventional radar signal processors, which assume a point target model for detection and tracking. As demonstrated through simulations, the performance degradation under the point target assumption can be significant for high-resolution radars, where targets extend across several detection cells. Interpolation of nearby data around the detected peak provides only a slight improvement. This paper presents a maximum likelihood estimator (MLE) to reduce the peak picking losses. By comparing the variance of the estimator with the Cramer Rao lower bound derived in this paper, it has been shown that the maximum likelihood estimator significantly reduces peak picking losses
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