Trilateration-Based Device-Free Sensing: Two Base Stations and One Passive IRS Are Sufficient

TL;DR

This paper extends trilateration to passive sensing in cellular networks with two base stations and one passive IRS, enabling target localization without active transmission from all anchors.

Contribution

It introduces a novel trilateration-based method for device-free sensing using passive IRS and two base stations, reducing the need for active anchors.

Findings

Outperforms traditional three-BS setups in localization accuracy

Effective in passive sensing scenarios with minimal active transmission

Demonstrates feasibility of integrated sensing and communication with passive elements

Abstract

The classic trilateration technique can localize each target based on its distances to three anchors with known coordinates. Usually, this technique requires all the anchors and targets, e.g., the satellites and the mobile phones in Global Navigation Satellite System (GNSS), to actively transmit/receive radio signals such that the delay of the one-way radio signal propagated between each anchor and each target can be measured. Excitingly, this paper will show that the trilateration technique can be generalized to the scenario where one of the three anchors and all the targets merely reflect the radio signals passively as in radar networks, even if the propagation delay between the passive IRS and the passive targets is difficult to be measured directly, and the data association issue for multi-sensor multi-target tracking arises. Specifically, we consider device-free sensing in a cellular network consisting of two base stations (BSs), one passive intelligent reflecting surface (IRS), and multiple passive targets, to realize integrated sensing and communication (ISAC). The two BSs transmit the orthogonal frequency division multiplexing (OFDM) signals in the downlink and estimate the locations of the targets based on their reflected signals via/not via the IRS. We propose an efficient trilateration-based strategy that can first estimate the distances of each target to the two BSs and the IRS and then localize the targets. Numerical results show that the considered networked sensing architecture with heterogenous anchors can outperform its counterpart with three BSs.