Abstract: The detection equipment of weapon systems is usually used to detect the noncooperative targets, causing the distribution patterns of observed samples to be unpredictable in feature spaces. The irregular cluster shapes, diversified cluster densities and noise bring great challenges to clustering algorithms. A novel adaptive grid-based clustering algorithm, which consists of a k-nearest neighbor method-based gridding method with spatial resolution adaptability, and an adaptive watershed transform-based method for cluster detection and segmentation in the gridded space are presented. The proposed algorithm could process the clusters with noises and significantly diverse densities, meanwhile keeps the advantages of gird-based clustering, including robustness for cluster shape and no need for cluster number as priori parameter. The effectiveness of the algorithm is tested with simulation and artificial datasets. Key

Key words: artificialintelligence, grid-basedclustering, modifiablearealunitproblem, watershedtransform