针对非合作目标的自适应网格聚类算法
发布时间：2018-09-08 10:04
【摘要】：武器系统的探测设备通常面对的是非合作目标,观测样本在特征空间中的分布形式难以预期,噪声、不规则的类簇形状以及差异化的类簇密度给聚类分析带来极大挑战。提出了一种自适应的网格聚类算法,该算法包括基于k-近邻方法的空间分辨率自适应网格化处理方法,以及基于自适应分水岭变换的类簇结构检测与划分方法。实现了对噪声以及密度差异极大类簇的自适应处理,同时保留了网格聚类方法对类簇形状不敏感、不需要类个数作为先验参数等优点。通过雷达、电子侦察以及复杂人造数据集的仿真,证明了该算法的有效性。
[Abstract]:The detection equipment of weapon systems usually faces non-cooperative targets, and the distribution of observation samples in the feature space is difficult to predict. Noise, irregular cluster shape and differentiated cluster density pose great challenges to cluster analysis. An adaptive mesh clustering algorithm is proposed, which includes spatial resolution adaptive mesh processing method based on k- nearest neighbor method and cluster structure detection and partition method based on adaptive watershed transformation. The adaptive processing of noise and density difference cluster is realized, while the grid clustering method is not sensitive to the shape of cluster and does not need the number of classes as a priori parameter. The effectiveness of the algorithm is proved by radar, electronic reconnaissance and simulation of complex artificial data sets.
【作者单位】： 北京理工大学机电学院;北京遥感设备研究所;
【基金】：国防“973”计划项目(613196)
【分类号】：TJ03;TP311.13
本文编号：2230202
[Abstract]:The detection equipment of weapon systems usually faces non-cooperative targets, and the distribution of observation samples in the feature space is difficult to predict. Noise, irregular cluster shape and differentiated cluster density pose great challenges to cluster analysis. An adaptive mesh clustering algorithm is proposed, which includes spatial resolution adaptive mesh processing method based on k- nearest neighbor method and cluster structure detection and partition method based on adaptive watershed transformation. The adaptive processing of noise and density difference cluster is realized, while the grid clustering method is not sensitive to the shape of cluster and does not need the number of classes as a priori parameter. The effectiveness of the algorithm is proved by radar, electronic reconnaissance and simulation of complex artificial data sets.
【作者单位】： 北京理工大学机电学院;北京遥感设备研究所;
【基金】：国防“973”计划项目(613196)
【分类号】：TJ03;TP311.13
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