Reducing RF Distance Error by Characterizing Multipath

Whitney, Ann; Parker, Johné M.; Kratzer, Zachary C. N.; Fessler, J.T.; Whitney, Julie G.

doi:10.1109/tim.2018.2875899

Cited by 5 publications

(1 citation statement)

References 15 publications

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“…Nonetheless we see a drift of the lower tag occurring from May to October 2021, with a temporary displacement difference of 20cm between the two tags. This drift, along with the strong increase in state covariance, is most likely a sign of radiofrequency interference[27]. This interpretation is confirmed by the received signal strength indicator which strongly decreases during the same period.…”

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confidence: 57%

Kalman Smoothing for better RFID Landslide Monitoring

Charléty,

Michel,

Breton

2023

2023 31st European Signal Processing Conference (EUSIPCO)

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The use of Radio-Frequency Identification (RFID) in Earth Sciences has been growing in the recent years, notably for landslide monitoring using phase-of-arrival localization schemes. In this article, an Extended Kalman Filtering approach is presented to exploit RFID phase data for landslide displacement monitoring. The filtering is based on a stochastic Langevin equation for the state-space model, introducing a heuristic coupling based on the mechanical continuity of the landslide material. This helps correct measurement biases and deal with missing data in the tracking of multiple tags. The Kalman state covariance matrix is a useful indicator of the tags localization quality. It can be exploited to discriminate true displacements from multipathinduced artifacts. Phase unwrapping is performed implicitly through the state model.

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confidence: 57%