Missile defense and interceptor allocation by neuro-dynamic programming

This paper proposes a solution methodology for a missile defense problem involving the sequential allocation of defensive resources over a series of engagements. The problem is cast as a dynamic programming/Markovian decision problem, which is computationally intractable by exact methods because of...

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Published inIEEE transactions on systems, man and cybernetics. Part A, Systems and humans Vol. 30; no. 1; pp. 42 - 51
Main Authors Bertsekas, D.P., Homer, M.L., Logan, D.A., Patek, S.D., Sandell, N.R.
Format Journal Article
LanguageEnglish
Published IEEE 01.01.2000
Subjects
Allocations
Asset management
Computational modeling
Computer architecture
Computer simulation
Counting circuits
Dynamic programming
Dynamical systems
Dynamics
Functional programming
Learning
Mathematical models
Missile defense
Missiles
Neural networks
Programming
Resource management
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Summary:This paper proposes a solution methodology for a missile defense problem involving the sequential allocation of defensive resources over a series of engagements. The problem is cast as a dynamic programming/Markovian decision problem, which is computationally intractable by exact methods because of its large number of states and its complex modeling issues. We employed a neuro-dynamic programming framework, whereby the cost-to-go function is approximated using neural network architectures that are trained on simulated data. We report on the performance obtained using several different training methods, and we compare this performance with the optimal approach.
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ISSN:1083-4427
1558-2426
DOI:10.1109/3468.823480