Research on Encounter Probability Between a Supercavitating Vehicle and a Target Based on Path Planning

Supercavitating vehicles have received significant attention in military applications due to their high underwater speed. This paper aims to establish analytic models of the encounter probability between a supercavitating vehicle and its target in two modes: straight-running mode (SRM) and turning-straight-running mode (TSRM). Mathematical models for the supercavitating vehicle to encounter the target in SRM and TSRM are proposed. An improved particle swarm optimization (PSO) algorithm based on local best topology with a penalty function is introduced to plan the TSRM path of the supercavitating vehicle. The particle swarm position is updated by using Cauchy and Gaussian distributions. The judgement indexes of the encounter probability analytic models in SRM and TSRM are determined through statistical analysis. Based on the law of error propagation, the analytic models of judgement index variances in the two modes are derived by using the implicit function differential method, and the integral intervals of their probability density functions are obtained according to the relative motion between the supercavitating vehicle and the target. Monte Carlo simulations and hypothesis testing are utilized to verify the accuracy and feasibility of the encounter probability analytic models in SRM and TSRM.