Open AccessTask selection for radar resource management in dynamic environments
Author(s) -
Seok Jinwoo,
Kabamba Pierre,
Girard Anouck
Publication year - 2018
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2017.0236
Subject(s) - computer science , finite state machine , task (project management) , radar , distributed computing , state (computer science) , resource management (computing) , resource (disambiguation) , heuristic , function (biology) , selection (genetic algorithm) , virtual finite state machine , state space , real time computing , mathematical optimization , computer engineering , algorithm , artificial intelligence , engineering , virtual machine , systems engineering , mathematics , programming language , telecommunications , computer network , statistics , evolutionary biology , biology
A task selection method for multi‐faced static phased array radar resource management in dynamically changing environments using recomposable restricted finite state machines is presented. Restricted finite state machines allow the design of a composed finite state machine with resource limitations by restricting some of the inputs. Recomposable restricted finite state machines allow the state space of a finite state machine to change dynamically, which allows the modelling of a dynamically changing environment. Applying dynamic programming to restricted finite state machines yields optimal policies for a given cost function and applying breadth‐first search or limited breadth‐first search with fixed depth yields suboptimal solutions for the current state. The authors model a task selector for the radar in an overloaded battlefield situation using recomposable restricted finite state machines and obtain a radar resource allocation policy using dynamic programming when the environment changes dynamically and the resources are limited. The suboptimal solution for the current state is obtained using heuristic methods: breadth‐first search, or limited breadth‐first search in the task selector for large‐scale problems. Furthermore, the authors consider distributed architectures for multi‐radar systems with communication channels. The results show that their approach performs well from the standpoints of both computational time and performance.