.png){kind=icon}
Return
Sparse coarray manifold separation for efficient cellular localization using coprime array
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2025.110157.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Advances in radio access network and antenna array processing have spurred the recent wave of research and trials into cost-effective schemes for cellular-based localization. To facilitate high-precision and low-latency position-based services, we propose a sparse coarray manifold separation (SCMS) method for fast joint direction-of-arrival and time-of-arrival estimation using a coprime array. By leveraging the Vandermonde structure in the manifold separation model, the two-dimensional (2D) spatial spectrum can be transformed into a discrete Fourier form and computed using the 2D robust random slice-based sparse Fourier transform. Through extensive numerical evaluations and link-level tests, we demonstrate that the SCMS method offers a precise approximation of true locations and significantly reduces computational complexity compared to baseline methods.
Keywords:
Cellular networks
Manifold separation
Array processing
Sparse Fourier transform
Indoor positioning
Journal
IF:
3.6
Papers:
10.0K
Citations:
1.7W
Organization
Cited Papers
.png){kind=icon}
ShareSave
ShareSaveShareSaveA Maximum Likelihood Method for Joint DOA and Polarization Estimation Based on Manifold Separation
PREAI
ShareSaveShareSave
ShareSave
View more