Joint Detection Threshold Optimization and Multidimensional Resource Allocation Scheme for Multitarget Tracking in Radar Networks Based on Low Probability of Intercept

• Published in: IEEE transactions on aerospace and electronic systems Vol. 61; no. 2; pp. 1433 - 1453
• Main Authors: Shi, Chenguang, Zhang, Xinrui, Shi, Zhao, Zhou, Jianjiang, Yan, Junkun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Constraints; Cramer-Rao bounds; Low probability of intercept (LPI); Lower bounds; Multiple target tracking; multitarget tracking (MTT); Optimization; Power management; Quadratic programming; Radar; Radar arrays; Radar detection; Radar imaging; Radar networks; Radar tracking; Resource allocation; Resource management; Semidefinite programming; Statistical analysis; Target detection; Target tracking; Tracking
• ISSN: 0018-9251; 1557-9603
• DOI: 10.1109/TAES.2024.3455323

In this study, a joint detection threshold optimization and multidimensional resource allocation (JDTO-MRA) scheme based on low probability of intercept is put forward for multitarget tracking in phased-array radar networks. The foundation of the proposed JDTO-MRA scheme is to adopt the optimization methodology to adaptively coordinate the detection threshold, radar node selection, transmit power, and signal bandwidth of each radar node to minimize the total power consumption of the underlying system, subject to given target detection probability and tracking accuracy constraints and several system resource budgets. The analytical expressions of the average target detection probability and Bayesian Cramér–Rao lower bound are derived and utilized as the metrics to depict the detection and tracking performance of multiple targets. By incorporating the simultaneous detection and tracking concept, resource-aware design, and improved probabilistic data association algorithm into a coherent framework, the JDTO-MRA model is established in phased-array radar networks. Due to the optimization parameters are all coupled in both the constraints and criterion function, the resulting JDTO-MRA model demonstrates a nonlinear and nonconvex problem. Combined with the semidefinite programming method and the sequential quadratic programming method, an appropriate five-step solution technique is proposed to solve the original problem. Several numerical results are developed to verify the superiority and effectiveness of the JDTO-MRA scheme.