Optimal Rescue Path for Maritime Air Crash Based on Probability Density Distribution and Bayesian Formula

Optimal rescue path for maritime air crash based on probability density distribution and Bayesian formula is proposed, where probable crash area is determined through surface search at a high altitude, and then the minimum wreckage floating zone is obtained by probability density distribution. Moreover, we divide it into regular hexagons based on Honeycomb Model to identify the point search area that is endowed with priority by Bayesian Formula. Finally, the optimal path is worked out by transferring optimization problem to be a Traveling Salesman Problem (TSP). In simulations, some spots are randomly chosen on a Google map in which we can find debris in ocean through surface search at high altitude. And a point search route is obtained by using Annealing algorithm, which strips out 4% of time compared with current strategies. An intelligent search and rescue framework based on reinforcement learning is proposed. In future work, search and rescue work will be free from manpower and bad weather constraints.