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http://hdl.handle.net/10397/106679
Title: | Random forest-based multipath parameter estimation | Authors: | Qi, X Xu, B Wang, Z Hsu, LT |
Issue Date: | Jul-2024 | Source: | GPS solutions, July 2024, v. 28, no. 3, 126 | Abstract: | Multipath is recognized as one of the major error sources for GNSS urban navigation. This study proposes a random forest (RF)-based multipath parameter estimator that uses random forest regression for parameter estimation, thereby mitigating multipath effect by removing the estimated reflected signal components. The proposed estimator is evaluated and compared with the multipath estimation delay-lock loop (MEDLL) for one-multipath and three-multipath cases, respectively. Simulation results demonstrate that the RF-based estimator is less affected by the front-end bandwidth of received signals, compared with MEDLL. The proposed RF-based estimator shows better performance than MEDLL for signals with front-end bandwidths of lower than 6 MHz. In 20 sets of tests on signals with a front-end bandwidth of 10 MHz in the three-multipath case, the RF-based estimator obtains smaller standard deviations than MEDLL. In experiments using real data with a front-end bandwidth of 2 MHz, the RF-based estimator reduces the 2D and 3D positioning errors by 8.5% and 8.7% over 180 epochs, respectively, against the conventional delayed-locked loop (DLL). | Keywords: | Global Navigation Satellite System (GNSS) Multipath estimation delay-lock loop (MEDLL) Multipath mitigation Random forest (RF) |
Publisher: | Springer | Journal: | GPS solutions | ISSN: | 1080-5370 | EISSN: | 1521-1886 | DOI: | 10.1007/s10291-024-01667-x | Rights: | © The Author(s) 2024 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The following publication Qi, X., Xu, B., Wang, Z. et al. Random forest-based multipath parameter estimation. GPS Solut 28, 126 (2024) is available at https://doi.org/10.1007/s10291-024-01667-x. |
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