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A Multi-Dimensional Hough Transform-Based Track-Before-Detect Technique for Detecting Weak Targets in Strong Clutter Backgrounds

机译:基于多维霍夫变换的检测前跟踪技术,用于在强杂波背景下检测弱目标

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摘要

The Hough transform (HT) algorithm detects straight-line features in two-dimensional data. This correspondence extends the HT to $N$-dimensional data to efficiently combine multiple first-threshold crossings from moving targets. The data dimensions can be the target position, its range and range-rate, and/or the first-threshold crossing times. This multi-dimensional HT (MHT) technique can be applied to enhance the detection of targets in random clutter backgrounds through the application of track-before-detect (TBD) processing.
机译:霍夫变换(HT)算法可检测二维数据中的直线特征。该对应关系将HT扩展到$ N $维数据,以有效地组合来自移动目标的多个第一阈值交叉点。数据维度可以是目标位置,其范围和范围速率和/或第一阈值穿越时间。通过应用检测前跟踪(TBD)处理,可以将这种多维HT(MHT)技术应用于增强随机杂波背景中目标的检测。

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