Skip to Main content Skip to Navigation
Conference papers

Smooth Bias Estimation for Multipath Mitigation Using Sparse Estimation

Abstract : Multipath remains the main source of error when using lobal navigation satellite systems (GNSS) in constrained environment, leading to biased measurements and thus to inaccurate estimated positions. This paper formulates the GNSS navigation problem as the resolution of an overdetermined system, which depends nonlinearly on the receiver position and linearly on the clock bias and drift, and possible biases affecting GNSS measurements. The extended Kalman filter is used to linearize the navigation problem whereas sparse estimation is considered to estimate multipath biases. We assume that only a part of the satellites are affected by multipath, i.e., that the unknown bias vector is sparse in the sense that several of its components are equal to zero. The natural way of enforcing sparsity is to introduce an l1 regularization associated with the bias vector. This leads to a least absolute shrinkage and selection operator (LASSO) problem that is solved using a reweighted-l1 algorithm. The weighting matrix of this algorithm is designed carefully as functions of the satellite carrier to noise density ratio and the satellite elevations. The smooth variations of multipath biases versus time are enforced using a regularization based on total variation. An experiment conducted on real data allows the performance of the proposed method to be appreciated.
Document type :
Conference papers
Complete list of metadatas

Cited literature [27 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01925466
Contributor : Open Archive Toulouse Archive Ouverte (oatao) <>
Submitted on : Friday, November 16, 2018 - 5:06:06 PM
Last modification on : Friday, January 29, 2021 - 2:06:18 PM
Long-term archiving on: : Sunday, February 17, 2019 - 3:10:06 PM

File

Lesouple_20722.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01925466, version 1
  • OATAO : 20722

Citation

Julien Lesouple, Franck Barbiero, Frédéric Faurie, Mohamed Sahmoudi, Jean-Yves Tourneret. Smooth Bias Estimation for Multipath Mitigation Using Sparse Estimation. 21st International Conference on Information Fusion (FUSION 2018), Jul 2018, Cambridge, United Kingdom. pp.1696-1702. ⟨hal-01925466⟩

Share

Metrics

Record views

169

Files downloads

295