Collaborative Trajectory Optimization for Multitarget Tracking in Airborne Radar Network With Missing Data

• Published in: IEEE transactions on signal processing Vol. 73; pp. 1048 - 1064
• Main Authors: Hu, Juan, Zuo, Lei, Varshney, Pramod K., Lan, Zhengyu, Gao, Yongchan
• Format: Journal Article
• Language: English
• Published: New York IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Airborne radar; airborne radar network; Approximation; Atmospheric modeling; Collaboration; Collision avoidance; Data exchange; Lower bounds; Missing data; Multiple target tracking; Optimization; Radar; Radar cross-sections; Radar data; Radar networks; Radar tracking; Simulation; Symbols; Target tracking; Tracking; Trajectory optimization
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2025.3540798

In this paper, an effective collaborative trajectory optimization (CTO) strategy is proposed for multitarget tracking in airborne radar networks with missing data. Missing data may occur during data exchange between radar nodes and a fusion center (FC) due to unreliability of communication channels. The CTO strategy aims to enhance the overall multi-target tracking performance by collaboratively optimizing the trajectories of airborne radars and the FC. In this paper, we derive the posterior Cramér-Rao lower bound (PCRLB) with missing data to evaluate the target tracking performance. On this basis, to maximize the target tracking performance while considering dynamics, collision avoidance, and communication distance constraints, we formulate the CTO optimization problem. The formulated problem is non-convex and internally coupled, which is challenging to solve directly. We decompose the CTO problem into two subproblems and devise an alternating optimization method. Specifically, approximation, and successive convex approximation are applied to make the subproblems solvable. Then, the two subproblems are solved alternately to realize the collaborative trajectory optimization of radars and the FC. Simulation results demonstrate that the proposed CTO strategy achieves better target tracking performance as compared with other benchmark strategies.