数据库扫描
计算机科学
聚类分析
杂乱
等距
采样(信号处理)
雷达
人工智能
算法
模式识别(心理学)
计算机视觉
数据挖掘
相关聚类
CURE数据聚类算法
滤波器(信号处理)
数学
电信
几何学
作者
Dominik Kellner,Jens Klappstein,Klaus Dietmayer
标识
DOI:10.1109/ivs.2012.6232167
摘要
The online observation using high-resolution radar of a scene containing extended objects imposes new requirements on a robust and fast clustering algorithm. This paper presents an algorithm based on the most cited and common clustering algorithm: DBSCAN [1]. The algorithm is modified to deal with the non-equidistant sampling density and clutter of radar data while maintaining all its prior advantages. Furthermore, it uses varying sampling resolution to perform an optimized separation of objects at the same time it is robust against clutter. The algorithm is independent of difficult to estimate input parameters such as the number or shape of available objects. The algorithm outperforms DBSCAN in terms of speed by using the knowledge of the sampling density of the sensor (increase of app. 40–70%). The algorithm obtains an even better result than DBSCAN by including the Doppler and amplitude information (unitless distance criteria).
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