Adaptive spectral conditioning for improved radar detection

R. Bonneau
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Abstract

In moving target radar scenarios temporal nonstationarities can profoundly effect performance of spatial target detectors. These nonstationarities are often ignored simply because it has been computationally inefficient, or mathematically intractable to handle them. This nonstationary noise, however can often be localized in spatial frequency. A spatial multiresolution Markov structure can thus be used to isolate these temporal nonstationarities. In this paper we employ a multiresolution-Markov matched filter detection scheme that integrates Kalman conditioning criteria to remove temporal nonstationarities and thereby improve radar detection performance.
改进雷达探测的自适应光谱调节
在运动目标雷达场景下,时间非平稳性会严重影响空间目标探测器的性能。这些非平稳性常常被忽略,仅仅是因为计算效率太低,或者在数学上难以处理它们。然而,这种非平稳噪声通常可以定位在空间频率上。因此,空间多分辨率马尔可夫结构可以用来隔离这些时间非平稳性。在本文中,我们采用了一种多分辨率-马尔可夫匹配滤波检测方案,该方案集成了卡尔曼条件准则,以消除时间非平稳性,从而提高雷达检测性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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