Collaborative Trajectory Planning and Resource Allocation for Multi-Target Tracking in Airborne Radar Networks under Spectral Coexistence

Shi, Chenguang; Dong, Jing; Salous, Sana; Wang, Ziwei; Zhou, Jianjiang

Abstract

This paper develops a collaborative trajectory planning and resource allocation (CTPRA) strategy for multi-target tracking (MTT) in a spectral coexistence environment utilizing airborne radar networks. The key mechanism of the proposed strategy is to jointly design the flight trajectory and optimize the radar assignment, transmit power, dwell time, and signal effective bandwidth allocation of multiple airborne radars, aiming to enhance the MTT performance under the constraints of the tolerable threshold of interference energy, platform kinematic limitations, and given illumination resource budgets. The closed-form expression for the Bayesian Cramér–Rao lower bound (BCRLB) under the consideration of spectral coexistence is calculated and adopted as the optimization criterion of the CTPRA strategy. It is shown that the formulated CTPRA problem is a mixed-integer programming, non-linear, non-convex optimization model owing to its highly coupled Boolean and continuous parameters. By incorporating semi-definite programming (SDP), particle swarm optimization (PSO), and the cyclic minimization technique, an iterative four-stage solution methodology is proposed to tackle the formulated optimization problem efficiently. The numerical results validate the effectiveness and the MTT performance improvement of the proposed CTPRA strategy in comparison with other benchmarks.

Citation

Shi, C., Dong, J., Salous, S., Wang, Z., & Zhou, J. (2023). Collaborative Trajectory Planning and Resource Allocation for Multi-Target Tracking in Airborne Radar Networks under Spectral Coexistence. Remote Sensing, 15(13), Article 3386. https://doi.org/10.3390/rs15133386

• Journal Article Type: Article
• Acceptance Date: Jun 8, 2023
• Online Publication Date: Jul 3, 2023
• Publication Date: Jul 1, 2023
• Deposit Date: Nov 7, 2023
• Publicly Available Date: Nov 7, 2023
• Journal: Remote Sensing
• Electronic ISSN: 2072-4292
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 15
• Issue: 13
• Article Number: 3386
• DOI: https://doi.org/10.3390/rs15133386
• Keywords: General Earth and Planetary Sciences
• Public URL: https://durham-repository.worktribe.com/output/1900157