Intelligent resource scheduling of radar network for early warning and surveillance tasks

DOI:	10.11809/bqzbgcxb2023.07.032
Keywords:	radar network; resource scheduling; missile early warning; space surveillance; Analytic Hierarchy Process (AHP); Simulated Annealing (SA) algorithm; Particle Swarm Optimization (PSO)
Abstract:	The resource scheduling method of radar network in the current missile early warning and space surveillance tasks is difficult to adapt to the trend of blowout growth of space targets and the diversified development trend of space weapons. In addition, resource scheduling has the characteristics of complex scenes, large amounts of computation and high accuracy requirements. For the above matters, this paper introduces Analytic Hierarchy Process (AHP) and artificial intelligence algorithm to solve the resource scheduling problem based on the analysis of the resource scheduling principles in missile early warning and space surveillance tasks. Intelligent resource scheduling of radar network for missile early warning and space surveillance tasks is implemented at both task and radar levels. At the task level, the task priority is divided based on AHP, which provides a solution for task priority selection in the face of multi task conflicts. At the radar level, by constructing resource scheduling models in two types of task scenarios, and on the basis of Simulated Annealing (SA) algorithm and Particle Swarm Optimization (PSO), an Improved Simulated Annealing and Binary Particle Swarm Optimization (ISABPSO) algorithm for target assignment and sequencing operation is proposed, which improves the calculation time, resource saving rate and algorithm qualification rate in resource scheduling scheme optimization. The detection efficiency of the radar network in missile early warning and space surveillance tasks is improved effectively.
Published:	2023-07-28
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