A normalizing flows-based virtual target method for many-vs-many missile guidance yields 0-4.1% gains when interceptor count equals targets and 5.8-14.4% gains when interceptors outnumber targets in Monte Carlo simulations.
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2 Pith papers cite this work. Polarity classification is still indexing.
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A Markov chain framework models satellite failures and launch delays to optimize the (r,q) spare policy and minimize costs, shown on a real mega-constellation case study.
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Many-vs-Many Missile Guidance via Virtual Targets
A normalizing flows-based virtual target method for many-vs-many missile guidance yields 0-4.1% gains when interceptor count equals targets and 5.8-14.4% gains when interceptors outnumber targets in Monte Carlo simulations.
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Analysis and Design of Spare Strategy for Large-Scale Satellite Constellation Using Direct Insertion under (r,q) Policy
A Markov chain framework models satellite failures and launch delays to optimize the (r,q) spare policy and minimize costs, shown on a real mega-constellation case study.