Abstract
One of the important problems to be solved in maritime search and rescue (MSAR) is decision-making, 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.
Highlights
In helicopter maritime search and rescue (MSAR) mission, it is of great significance for reducing the loss of life and property that distressed facilities, vessels and people are found and rescued efficiently, the decisive factor of which is the accurate positioning of optimal mission area
According to the search theory [1]–[3], it can be known that the probability of success (POS), which is calculated from the probability of coverage (POC) and the probability of detection (POD), can be used to judge the pros and cons of MSAR mission area
Wang et al [17], proposed various path planning algorithms based on distributed particle swarm optimization (DPSO) for UAV swarms, which were MDC-DPSO, FCO-DPSO, ACE-particle swarm optimization algorithm (PSO), and the simulation results showed that these algorithms could effectively meet the requirements of detection time and accuracy
Summary
In helicopter maritime search and rescue (MSAR) mission, it is of great significance for reducing the loss of life and property that distressed facilities, vessels and people are found and rescued efficiently, the decisive factor of which is the accurate positioning of optimal mission area. Wang et al [17], proposed various path planning algorithms based on distributed particle swarm optimization (DPSO) for UAV swarms, which were MDC-DPSO, FCO-DPSO, ACE-PSO, and the simulation results showed that these algorithms could effectively meet the requirements of detection time and accuracy These studies provide feasible methods for exploring the global optimal solution of mission area. The insufficient analysis of SART drift prediction data and the inflexibility of grid partition when explore optimal mission area often leads to local optimal solutions Aiming at these shortcomings of it, a particle swarm optimization algorithm based on space-time weight (TS-PSO) is proposed in this paper to solve this problem in helicopter MSAR mission area planning. The last section concludes this paper and offers the future work
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