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