基于马尔可夫过程的水下运动目标启发式搜索

在搜索海域存在障碍的情况下，该文将启发式搜索算法应用于对水下运动目标的搜索，研究了基于马尔可夫过程的运动目标启发式搜索算法。建立了马尔可夫水下运动目标规避模型、搜索器启发式搜索过程模型和马尔可夫水下运动目标的搜索概率模型。该算法由已知的目标先验位置分布信息不断地对目标的运动位置进行估计、更新，以获得精确的目标后验分布，再利用启发函数得到下一步的最佳搜索节点。仿真分析表明：在对水下运动目标搜索时，搜索器能有效地规避障碍，提高搜索效率，有助于研究水下目标的优化搜索。

Heuristic Search for Moving Underwater Targets Based on Markov Process

If there are obstacles in the search sea area, the heuristic search algorithm can be applied into the search process of moving underwater targets, to study the heuristic search for moving underwater targets based on Markov process in this paper. The Markov process motion model of underwater targets, the heuristic search model and search probability model of searcher are built. This algorithm continually estimates and updates the moving underwater targets location based on the target’s prior location distributed information, to gain accurate targets posterior location distribution information, by using the heuristic function to get the next best search node. The simulation shows that the heuristic search can avoid obstacles effectively, when searching the moving underwater targets. Moreover, it can improve search efficiency. It is useful to study on optimization search for moving underwater targets.