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首页> 外文会议>Conference on Signal and Data Processing of Small Targets 2002, Apr 2-4, 2002, Orlando, USA >EM-Based Measurement Fusion for HRR Radar Centroid Processing
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EM-Based Measurement Fusion for HRR Radar Centroid Processing

机译：基于EM的HRR雷达质心处理测量融合

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摘要

This paper develops a new algorithm for high range resolution (HRR) radar centroid processing for scenarios where there are closely spaced objects. For range distributed targets with multiple discrete scatterers, HRR radars will receive detections across multiple range bins. When the resolution is very high, and the target has significant extent, then it is likely that the detections will not occur in adjacent bins. For target tracking purposes, the multiple detections must be grouped and fused to create a single object report and a range centroid estimate is computed since the detections are range distributed. With discrete scatterer separated by multiple range bins, then when closely spaced objects are present there is uncertainty about which detections should be grouped together for fusion. This paper applies the EM algorithm to form a recursive measurement fusion algorithm that segments the data into object clusters while simultaneously forming a range centroid estimate with refined bearing and elevation estimates.
机译：本文针对对象间距很小的场景，开发了一种用于高分辨力（HRR）雷达质心处理的新算法。对于具有多个离散散射体的距离分布目标，HRR雷达将接收多个距离范围内的检测结果。当分辨率非常高且目标范围很大时，检测可能不会在相邻箱中发生。为了进行目标跟踪，必须对多个检测进行分组和融合以创建单个对象报告，并且由于检测是范围分布的，因此要计算距离质心估计。由于离散的散射体被多个测距箱隔开，因此当存在间隔很近的物体时，对于哪些检测应该分组在一起进行融合存在不确定性。本文应用EM算法来形成递归测量融合算法，该算法将数据分割为对象簇，同时形成带有精确方位角和仰角估计值的范围质心估计值。

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