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Exact interception probability-based assignment for multiple pursuers against multiple evaders
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AI
DOI：10.1016/j.ast.2025.109944.png)
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Abstract
En 中文
This paper aims to address the assignment issue associated with multiple aerial pursuers against multiple evaders by proposing a guidance performance-based algorithm. An evaluation function for assignment is first established based on the interception probability of evaders, which is exactly derived by reconstructing augmented ideal proportional navigation (AIPN) guidance law as a time-varying close system and leveraging the covariance analysis technique. The total interception control consumption, identical to the AIPN performance index, is also analytically obtained as an assignment evaluator. By simultaneously optimizing interception probability and control consumption with constraints of individual field-of-view and pursuer quantity, the Pareto-optimal assignment solution is calculated by designing a customized PEA-Non-dominant Sorting Genetic Algorithm II. The superiority of the proposed approach lies in the accurate assignment evaluators constructed based on terminal guidance performance, which is the key improvement to existing methods. Numerous simulations are conducted to validate the effectiveness of the proposed algorithm.
Keywords:
Assignment
Interception probability
Control consumption
Multi-objective optimization
Journal
IF:
5.8
Papers:
1.0W
Citations:
3.0W