Optimized Benchmark Highlight Clustering Algorithm Based on Planar Element Method

Aiming at the problem that the available highlight model of submarine target in torpedo homing simulation is not exquisite enough, the fundamental principle of planar element model and the main idea of k-means clustering algorithm are employed to propose optimized Benchmark highlight clustering algorithm. First, a more exquisite highlight model of submarine target was built for torpedo homing simulation. A three-dimensional Benchmark submarine model was divided into planar elements, and the acoustic potential functions of each element were computed. Then, the elements were disposed by using the primary clustering algorithm, and a Benchmark highlight model was built. At last, the influence of division quality of the elements on the result of simulation was analyzed and the secondary division method was discussed to optimize the algorithm. Simulation indicated that the highlight model of Benchmark based on the proposed clustering algorithm is more exquisite with longitudinal stability compared with the available method. This research may provide the reference for target recognition of a torpedo.