Co-Designing Statistical MIMO Radar and In-band Full-Duplex Multi-User MIMO Communications -- Part III: Multi-Target Tracking

TL;DR

This paper presents a low-complexity approach for multi-target detection, localization, and tracking in an integrated sensing and communications system using in-band full-duplex MIMO technology, demonstrating high accuracy and feasibility.

Contribution

It introduces a novel joint design framework for multi-target sensing in IBFD MU-MIMO systems, including algorithms for target localization and tracking under practical constraints.

Findings

High accuracy in multi-target localization and tracking

Feasibility demonstrated through numerical experiments

Effective joint design of waveforms, precoders, and filters

Abstract

As a next-generation wireless technology, the in-band full-duplex (IBFD) transmission enables simultaneous transmission and reception of signals over the same frequency, thereby doubling spectral efficiency. Further, a continuous up-scaling of wireless network carrier frequencies arising from ever-increasing data traffic is driving research on integrated sensing and communications (ISAC) systems. In this context, we study the co-design of common waveforms, precoders, and filters for an IBFD multi-user (MU) multiple-input multiple-output (MIMO) communications with a distributed MIMO radar. This paper, along with companion papers (Part I and II), proposes a comprehensive MRMC framework that addresses all these challenges. In the companion papers, we developed signal processing and joint design algorithms for this distributed system. In this paper, we tackle multi-target detection, localization, and tracking. This co-design problem that includes practical MU-MIMO constraints on power and quality-of-service is highly non-convex. We propose a low-complexity procedure based on Barzilai-Borwein gradient algorithm to obtain the design parameters and mixed-integer linear program for distributed target localization. Numerical experiments demonstrate the feasibility and accuracy of multi-target sensing of the distributed FD ISAC system. Finally, we localize and track multiple targets by adapting the joint probabilistic data association and extended Kalman filter for this system.