One of the important problems to be solved in maritime search and rescue (MSAR) is decisionmaking, and the premise of it is determining the mission area for search and rescue unit. To solve the problem that classical cellular iterative search (CIS) algorithm is easy to fall into local optimal solution when determining the mission area, the particle swarm optimization algorithm based on time-space weight (TS-PSO) is proposed in this paper. This algorithm summarizes the optimization objectives and constraint conditions of the MSAR mission area planning according to search theory, carries out the parametric modeling of mission area legitimately and obtains the global optimal solution by continuous exploration in the parameter definition domain. On this basis, by analyzing the time-space weight of drift prediction data, the optimization results are further improved. Finally, through the case simulation analysis, it can be seen that the TS-PSO algorithm can effectively make up for the deficiency of the CIS algorithm and further improve the success probability of optimal MSAR mission area.INDEX TERMS Global optimal solution, maritime search and rescue, mission area planning, particle swarm optimization algorithm, time-space weight.

No abstract

Helicopters are widely used in maritime search and rescue (SAR) missions. To improve the probability of success (POS) of SAR missions, search areas should be carefully planned. However, the search area is usually determined based on the survivors' probable locations at a given moment by existing planning methods, while the effects of the relative motion between the helicopter and the search objects are ignored, possibly leading to a significant decrease in the POS. To minimize the impact of search object motion, a time domain-based iterative planning (TIP) method is proposed in this paper to obtain the optimal search areas. The survivors' probable locations and mean drift direction are updated iteratively, while the probability map is developed by taking survivors' mean drift direction as a reference. Then, the optimal search area is determined by an iterative search method starting from the cell with the highest probability of containment. To evaluate the effectiveness of a search plan, an agent-based simulation environment of a maritime search mission is constructed based on the AnyLogic simulation platform. Taking a capsizing case as an example, the simulation results show that the novel TIP method minimizes the impact of search object motion on the search effectiveness and obtains higher POS values than those obtained by other methods.

No abstract