Communication‐awareness adaptive resource scheduling strategy for multiple target tracking in a multiple radar system. Issue 7 (7th January 2022)

• Record Type: Journal Article
• Authors: Su, Yang; Cheng, Ziyang; He, Zishu; Deng, Minglong
• Other Names: Liao Bin guestEditor.; Liu Wei guestEditor.; Cheng Ziyang guestEditor.; Huang Tianyao guestEditor.
• Is Part Of: IET signal processing. Volume 16:Issue 7(2022)
• Page Start: 750
• Page End: 761
• Publication Date: 2022-01-07
• Subjects: communication awareness -- hybrid particle swarm optimization -- multiple target tracking -- radar node selection -- resource allocation; Signal processing -- Periodicals; 621.3822
• DOI: 10.1049/sil2.12097
• Languages: English
• ISSNs: 1751-9675

Abstract

Abstract: In this study, a communication‐awareness adaptive resource scheduling (CARS) strategy for multiple target tracking in a multiple radar system (MRS) is proposed. The CARS strategy aims to maximise the tracking performance of MRS, whilst minimising the interference from MRS to communication systems (CSs), whose mechanism is to simultaneously control the revisit frame interval of each target and determine the activation of radar nodes, the radar‐target assignment and the allocation of transmitted power. Mathematically, the CARS strategy is formulated as an optimization problem, which contains both the continuous variable and the discrete (integer) variable. To tackle the resultant mixed‐integer, non‐convex, and non‐linear problem efficiently, incorporating with the proposed hybrid particle swarm optimization algorithm based on the Kullback–Leibler divergence, a two‐stage solution technique is developed to obtain the near‐optimal solution. Numerical simulation results are provided to validate the proposed CARS strategy and demonstrate its superiority over the traditional scheduling strategies.