Resource Allocation in Heterogeneously-Distributed Joint Radar-Communications Under Asynchronous Bayesian Tracking Framework

• Published in: IEEE journal on selected areas in communications Vol. 40; no. 7; pp. 2026 - 2042
• Main Authors: Wu, Linlong, Mishra, Kumar Vijay, Shankar, M. R. Bhavani, Ottersten, Bjorn
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bayesian analysis; Bayesian Cramér-Rao bound; Computer networks; Dwell time; heterogeneous radar networks; joint radar-communications; multi-target tracking; Multiple target tracking; Optimization; Phased arrays; Radar; Radar antennas; Radar measurements; Radar tracking; Resource allocation; Resource management; Target tracking; Tracking errors
• ISSN: 0733-8716; 1558-0008; 1558-0008
• DOI: 10.1109/JSAC.2022.3157371

Optimal allocation of shared resources is key to deliver the promise of jointly operating radar and communications systems. In this paper, unlike prior works which examine synergistic access to resources in colocated joint radar-communications or among identical systems, we investigate this problem for a distributed system comprising heterogeneous radars and multi-tier communications. In particular, we focus on resource allocation in the context of multi-target tracking (MTT) while maintaining stable communications connections. By simultaneously allocating the available power, dwell time and shared bandwidth, we improve the MTT performance under a Bayesian tracking framework and guarantee the communications throughput. Our <inline-formula> <tex-math notation="LaTeX">{a} </tex-math></inline-formula>lter<inline-formula> <tex-math notation="LaTeX">{n} </tex-math></inline-formula>ating allo<inline-formula> <tex-math notation="LaTeX">{c} </tex-math></inline-formula>ation of <inline-formula> <tex-math notation="LaTeX">{h} </tex-math></inline-formula>eterogene<inline-formula> <tex-math notation="LaTeX">{o} </tex-math></inline-formula>us <inline-formula> <tex-math notation="LaTeX">{r} </tex-math></inline-formula>esources (ANCHOR) approach solves the resulting non-convex problem based on the alternating optimization method that monotonically improves the Bayesian Cramér-Rao bound. Numerical experiments demonstrate that ANCHOR significantly improves the tracking error over two baseline allocations and stability under different target scenarios and radar-communications network distributions.