Channel estimation assisted bistatic localization in terrestrial and non-terrestrial networks

Abstract

Low-Earth-orbit (LEO) satellites are regarded as a key enabler for 6G communications and localization, due to their large coverage beyond conventional terrestrial networks. In this paper, we propose a downlink LEO base station (BS) bistatic localization framework relying on hybrid beamforming that alleviates the reliance on ultra-fine angle estimation by jointly exploiting time-frequency-spatial observations. A multiple-measurement-vector (MMV) based sparse model is constructed for attaining accurate channel gains and angles from limited pilots and moderate array sizes, where a modified block orthogonal matching pursuit (BOMP) algorithm is proposed to enhance robustness under highly correlated sensing matrices for localization purposes. After geometry-based timing-advance and Doppler pre-compensation at the BS, a two-dimensional (2D) upsampling matched filter having fine delay-Doppler grids is applied to estimate the residual time of arrival (ToA) and Doppler frequency. Then, the final user equipment (UE) position is obtained by intersecting the BS-centered angle-of-arrival (AoA) ray with a bistatic-range ellipse derived from the residual delay. The numerical results under realistic LEO-BS bistatic scenarios demonstrate that the proposed scheme achieves meter-level localization accuracy and highlight the performance gains attained by increasing the number of pilot symbols, subcarriers, and angular resolutions.

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Identifiers

ORCID for Kunlun Li: orcid.org/0000-0002-5797-6560

ORCID for Chao Zhang: orcid.org/0000-0001-6742-6178

ORCID for Mohammed El-Hajjar: orcid.org/0000-0002-7987-1401

ORCID for Chao Xu: orcid.org/0000-0002-8423-0342

ORCID for Lajos Hanzo: orcid.org/0000-0002-2636-5214

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