水面舰艇编队对潜搜索效能评估模型

  目的  为提高搜索到目标潜艇的概率，更有效地开展水面舰艇编队搜潜行动，对舰艇应召搜潜路径规划问题进行研究。  方法  首先，构建基于隐式马尔科夫模型（HMM）框架的水面舰艇应召搜潜模型，设计两阶段启发式求解的方法，使搜潜命中概率期望值最大，利用进化算法（EA），通过对种群内的个体进行交叉和变异操作，避免出现局部最优的问题，并与常规搜潜方法进行对比；然后，通过实验研究不同分割策略对路径优化的影响。  结果  单舰搜潜和多舰搜潜的仿真实验表明，采用所提方法能够获得最大化搜潜命中概率期望值以及最优搜潜路径。而分割次数的实验表明，合理的重新划分搜潜区域，有利于找到总体更优的搜潜路径。  结论  该模型能找到最优搜潜路径，有效提高水面舰艇编队搜潜效率。

Submarine searching efficiency assessment model of surface warships' fleet

  Objectives  In order to improve the successful probability of searching for target submarines and to make the operation of the surface ship formation more effectively, the problem about the path planning for the on-call submarine searching for ships is studied.  Methods  Firstly, an on-call submarine searching model of surface ships was constructed based on the Hidden Markov Model(HMM). A two-stage heuristic method was designed to maximize the probability of searching for submarine search expectation. The problem of local optimum was avoided by using evolutionary algorithm(EA)to cross and mutate the individuals in the population, and a comparison with conventional searching methods was made. Then, the effects of different segmentation methods on path optimization were studied experimentally.  Results  The simulation results of single-ship and multi-ship searching for submarines show that the method adopted in this paper can obtain the maximum submarine search expectation and the optimal searching path. The segmentation times experiment show that a reasonable re-division of the search area is beneficial to find a better searching path.  Conclusions  This model can find an optimal path for submarine search and improve the searching efficiency of the surface ship formation.