Improvement of Starting Point Selection of Data Field Clustering Algorithm

Abstract Since it does not depend on the starting point selection, data field clustering can perform unsupervised according to distribution characteristics. However, due its drawback of high computational complexity caused by iterative updates, is suitable for scenarios with real-time requirements such as radar signal sorting. In this paper, an improved method selection proposed address problems low timeliness and poor interference immunity in The performance algorithm simulated verified a complex electromagnetic environment, results show that improvement paper improves algorithm.