Multi‐static radar power allocation for multi‐stage stochastic task of missile interception. Issue 5 (2nd March 2018)

• Record Type: Journal Article
• Authors: Yang, Yichuan; Zhang, Tianxian; Yi, Wei; Kong, Lingjiang; Li, Xiaolong; Yang, Xiaobo
• Is Part Of: IET radar, sonar & navigation. Volume 12:Issue 5(2018)
• Page Start: 540
• Page End: 548
• Publication Date: 2018-03-02
• Subjects: missiles -- military radar -- military computing -- stochastic processes -- minimisation -- decision making -- learning (artificial intelligence) -- particle swarm optimisation; multistatic radar power allocation -- missile interception multi‐stage stochastic task -- MSRS -- radar resource management optimisation problem -- fixed energy budget -- loss minimisation -- optimal power allocation -- OPA -- sequential decision‐making problem -- multiple coupled stages -- computational load -- reinforcement learning -- particle swarm optimisation -- uniform power allocation scheme -- optimal resource management scheme; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2017.0534
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : Considering a multi‐stage stochastic task, in which a multi‐static radar system (MSRS) is applied to assist with missile interception, the authors study an optimisation problem of radar resource management. Specifically, under restriction of a fixed energy budget, the authors devote to minimise the loss, which is caused by the unsuccessfully intercepted missiles, through optimal power allocation (OPA) of MSRS within multiple stages. The design of OPA can be translated into a sequential decision‐making problem. The authors formulate the problem through variable definition and modelling the missile interception procedure. As the authors need to consider the randomness of multiple coupled stages and jointly allocation power between multiple radar nodes, to solve the proposed problem is of huge computational load. The authors propose a solution that combines with reinforcement learning and particle swarm optimisation. Comparing with the uniform power allocation scheme, the simulation results demonstrate that the OPA scheme designed by the proposed method is capable to achieve preferable and more stable performance for the whole missile interception. The authors' contributions include a novel optimisation resource management model for a multi‐stage stochastic task and an effective solution for the optimal resource management scheme.