Automatic Identification of Maritime Targets based on K-means Optimization Algorithm

As the key to fire attack and defense in offshore war, rapid and accurate positioning through automatic identification of maritime targets has always been the greatest concern of global military research. This paper focuses on an offshore target identification method based on the improved K-means clustering algorithm, which combines the advantages of k-means clustering algorithm of favorable clustering effect, convergence speed and recognition effect. Large-scale offshore target signal source data is converted into digital signals, and the shortest distance between each particle and its corresponding class is obtained by improving the K-means clustering algorithm. The signals are then divided into several different clusters to achieve target identification. According to the results of a practical example, the method demonstrates notable performance and high practical value in the fast and automatic identification of maritime targets.