Research Article
An Off-Grid Sparse Representation Based Localization Method for Near-Field Sources
@INPROCEEDINGS{10.1007/978-3-030-69072-4_37, author={Li Yang and Yi Jin and Changzhi Xu and Xiaoran Li and Jinzhong Zuo and Dizhu Wang}, title={An Off-Grid Sparse Representation Based Localization Method for Near-Field Sources}, proceedings={Wireless and Satellite Systems. 11th EAI International Conference, WiSATS 2020, Nanjing, China, September 17-18, 2020, Proceedings, Part II}, proceedings_a={WISATS PART 2}, year={2021}, month={2}, keywords={Near-field localization Sparse representation Off-grid signal model Alternative iteration}, doi={10.1007/978-3-030-69072-4_37} }- Li Yang
Yi Jin
Changzhi Xu
Xiaoran Li
Jinzhong Zuo
Dizhu Wang
Year: 2021
An Off-Grid Sparse Representation Based Localization Method for Near-Field Sources
WISATS PART 2
Springer
DOI: 10.1007/978-3-030-69072-4_37
Abstract
Near-field source localization is a potential research topic in next-generation wireless communications. Most existing methods focus on traditional subspace based methods or on-grid sparse methods. In this paper, we propose an off-grid sparse representation localization method. First, by obtaining a high order cumulant matrix we construct an angle based off-grid signal model and then employ the alternatively iterating optimization method to estimate the angles. For range estimation, a range based off-grid signal model is constructed by using the angle estimations and solved by alternatively iterating method. Simulation results reveal that, the proposed method not only enjoys high estimation accuracy, but also can realize auto-pairing of angles and ranges.
