Joint User and Target Tracking in Multi-Static Sensing Systems

Perceiving the mobility of the passive targets is critical in the context of Integrated Sensing and Communication (ISAC). While target localization has been extensively studied in radio sensing, the motion estimation and tracking of the targets have not been revealed. This paper proposes a new framework for joint UE and target tracking in the multi-static sensing system under the promising scenario that the user equipment (UE) serves as the receiver. We first estimate the UE's location using the direct paths between the UE and the base stations (BSs), and estimate the target's location of each scattering path to identify the targets by the mean-shift clustering algorithm. Then, we jointly estimate the motions of the UE and targets using the Taylor-least squares (LS) algorithm, and propose a joint UE and target tracking method based on the extended Kalman filter (EKF). Simulations demonstrate that the proposed joint motion estimation and tracking scheme improves the accuracy of location estimates by 42.34% w.r.t. the UE, and 71.29% w.r.t. the targets.

I. Introduction

Integrated Sensing and Communication (ISAC) is expected to be a key technique in the upcoming fifth-generation (B5G) and sixth-generation (6G) wireless networks due to its potential for offering the communication systems the ability to perceive the surrounding environment [1]. The development of ISAC has spawned new high-quality connectivity- and high-accuracy sensing-based applications, such as vehicle to everything (V2X) and industrial internet of things (IoT) [2]. Instead of a dedicated and static base station (BS), using mobile user equipment (UEs) as the sensing receiver can save on infrastructure costs and, more importantly, enable range-unlimited and highly flexible sensing of targets.