With the massively deployed WiFi access points (APs), indoor wireless transmission has become much more convenient than several years before, which lays a basis for the upcoming integrated sensing and communication (ISAC) schemes. Since the positions of APs and user equipment (UEs) are critical to implementing the ISAC technology, the existing literature focuses on many distance-based methods to identify the places of legacy APs and UEs. However, a generally applicable positioning scheme may result in significant positioning performance degradation in the specific indoor environment. To address this issue, we propose an improved nonlinear particle swarm optimization-Gauss Newton (NPSO-GN) positioning algorithm in this paper, which incorporates the measured channel responses and some known AP locations to estimate the positions of all the APs and UEs for the ISAC applications. Based on some numerical results, we show that the proposed algorithm can achieve 36% positioning error reduction if compared with some state-of-the-art baselines, and the performance improvement is reliable under 50 randomly generated network scenarios.
An improved NPSO-GN Algorithm for TOA-based Positioning in Indoor Environments
17.12.2023
1202927 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch