DOA Estimation and Localization Using Multi-Base Station Spatial Spectrum Fusion

Madhu Kumari Choudhary, Di He, Lav Dutta, Fei Wen, Peilin Liu, Wenxian Yu, Yi Zhang

Abstract:	From retail shopping, emergency services, location based services to disaster recovery accurate location technology will be prime localization experience. GPS assisted system for accurate location are available for outdoor, but indoors, where GPS fades, and presence of multipath, non-line-of-sight, and scattering environment yields unreliable position estimation. Classical subspace based indoor has been long objective of the research community, and several source estimation and positioning methods were proposed for localization of wireless device in the indoor environment. Spatial spectrum estimation plays an important role in array signal processing to achieve high resolution and high accuracy, while fusion is the technology which can process data comprehensively, particularly in the field of sensor network at cost of limited data resources. Most of the previous research has focused on sensor fusion or DOA fusion. We propose a multi-base-station spatial spectrum fusion (MBS-SSF) based localization where spatial data of each array element of each base station are processed and sent to fusion center where localization in known grid is determined by maximum array response projection on fused noise subspace with grid refinement algorithm. Simulation result shows that the proposed MBS-SSF gives reliable result over existing schemes in terms of simplicity and performance.
Published in:	Proceedings of the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2017) September 25 - 29, 2017 Oregon Convention Center Portland, Oregon
Pages:	490 - 500
Cite this article:	Choudhary, Madhu Kumari, He, Di, Dutta, Lav, Wen, Fei, Liu, Peilin, Yu, Wenxian, Zhang, Yi, "DOA Estimation and Localization Using Multi-Base Station Spatial Spectrum Fusion," Proceedings of the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2017), Portland, Oregon, September 2017, pp. 490-500. https://doi.org/10.33012/2017.15293
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