Approximate dynamic programming for missile defense interceptor fire control
•We consider an asset-based dynamic weapon-target assignment problem.•We assign interceptors to ballistic missiles in a multiple-salvo conflict situation.•We use approximate dynamic programming to obtain high quality fire control policies.•In a representative scenario, our policy achieves a 7.74% me...
Uložené v:
| Vydané v: | European journal of operational research Ročník 259; číslo 3; s. 873 - 886 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier B.V
16.06.2017
|
| Predmet: | |
| ISSN: | 0377-2217, 1872-6860 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Shrnutí: | •We consider an asset-based dynamic weapon-target assignment problem.•We assign interceptors to ballistic missiles in a multiple-salvo conflict situation.•We use approximate dynamic programming to obtain high quality fire control policies.•In a representative scenario, our policy achieves a 7.74% mean optimality gap.•Our work informs the development and evaluation of missile defense doctrine.
Given the ubiquitous nature of both offensive and defensive missile systems, the catastrophe-causing potential they represent, and the limited resources available to countries for missile defense, optimizing the defensive response to a missile attack is a necessary national security endeavor. For a single salvo of offensive missiles launched at a set of targets, a missile defense system protecting those targets must determine how many interceptors to fire at each incoming missile. Since such missile engagements often involve the firing of more than one attack salvo, we develop a Markov decision process (MDP) model to examine the optimal fire control policy for the defender. Due to the computational intractability of using exact methods for all but the smallest problem instances, we utilize an approximate dynamic programming (ADP) approach to explore the efficacy of applying approximate methods to the problem. We obtain policy insights by analyzing subsets of the state space that reflect a range of possible defender interceptor inventories. Testing of four instances derived from a representative planning scenario demonstrates that the ADP policy provides high-quality decisions for a majority of the state space, achieving a 7.74% mean optimality gap over all states for the most realistic instance, modeling a longer-term engagement by an attacker who assesses the success of each salvo before launching a subsequent one. Moreover, the ADP algorithm requires only a few minutes of computational effort versus hours for the exact dynamic programming algorithm, providing a method to address more complex and realistically-sized instances. |
|---|---|
| ISSN: | 0377-2217 1872-6860 |
| DOI: | 10.1016/j.ejor.2016.11.023 |