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Robust Fingerprint Localization Based on Massive MIMO Systems
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AI
DOI：10.23919/JCC.2022.00.037.png)
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摘要
En 中文
Location-based services have become an important part of the daily life. Fingerprint localization has been put forward to overcome the shortcomings of the traditional positioning algorithms in indoor scenario and rich scattering environment. In this paper, a single-site multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system is modeled, from which an angle delay channel power matrix (ADCPM) is extracted. Considering the changing environment, auto encoders are used to generate new fingerprints based on ADCPM fingerprints to improve the robustness of the fingerprints. When the scattering environment has changed beyond a certain extent, the robustness will not be able to make up for the positioning error. Under this circumstance, an updating of the fingerprint database is imperative. A new fingerprint database updating algorithm which combines a new clustering method and an updating rule based on probability is proposed. Simulation results show the desirable performance of the proposed methods.
Keyword:
massive multiple-input multiple-output
fingerprint extraction
robustness
database updating
