We gratefully acknowledge support from
the Simons Foundation and member institutions.
Full-text links:

Download:

Current browse context:

eess.SP

Change to browse by:

References & Citations

Bookmark

(what is this?)
CiteULike logo BibSonomy logo Mendeley logo del.icio.us logo Digg logo Reddit logo

Electrical Engineering and Systems Science > Signal Processing

Title: Device-Free 3D Drone Localization in RIS-Assisted mmWave MIMO Networks

Abstract: In this paper, we investigate the potential of reconfigurable intelligent surfaces (RISs) in facilitating passive/device-free three-dimensional (3D) drone localization within existing cellular infrastructure operating at millimeter-wave (mmWave) frequencies and employing multiple antennas at the transceivers. The developed localization system operates in the bi-static mode without requiring direct communication between the drone and the base station. We analyze the theoretical performance limits via Fisher information analysis and Cram\'er Rao lower bounds (CRLBs). Furthermore, we develop a low-complexity yet effective drone localization algorithm based on coordinate gradient descent and examine the impact of factors such as radar cross section (RCS) of the drone and training overhead on system performance. It is demonstrated that integrating RIS yields significant benefits over its RIS-free counterpart, as evidenced by both theoretical analyses and numerical simulations.
Comments: 6 pages, 5 figures, submitted to IEEE GLOBECOM 2024
Subjects: Signal Processing (eess.SP)
Cite as: arXiv:2404.14879 [eess.SP]
  (or arXiv:2404.14879v1 [eess.SP] for this version)

Submission history

From: Jiguang He [view email]
[v1] Tue, 23 Apr 2024 10:06:32 GMT (215kb,D)

Link back to: arXiv, form interface, contact.