Joint Power Allocation and Route Planning Scheme for Multitarget Tracking in Airborne Radar Network Under Multiuncertainty

Zhang, Yuanshi; Pan, Minghai; Han, Qing; Long, Weijun; Yang, Shiwen

doi:10.1109/jsen.2023.3249782

Cited by 5 publications

(1 citation statement)

References 32 publications

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“…For the past few years, airborne radar networks have been gaining popularity and attention from technologists in different military and research organizations. The resulting great advantages over traditional static radar systems, such as the enhanced degree of freedom in geometric diversity and high flexibility and mobility of airborne platforms, can deliver powerful capabilities and better system performances for remote sensing, area surveillance, target detection, multi-target tracking (MTT), and parameter estimation [1,2]. To best utilize the potential of airborne radar networks, resource-aware scheduling is of crucial importance in different missions, which has been a research hot issue thus far.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Introductionmentioning

confidence: 99%