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Sparse coarray manifold separation for efficient cellular localization using coprime array
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DOI：10.1016/j.sigpro.2025.110157.png)
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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
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