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GNSS World of China

GNSS World of China

TIAN Zongzheng, HUANG Xin, TAO Changpeng. Particle filter resampling-based GNSS multipath correction method[J]. GNSS World of China, 2024, 49(6): 36-46. DOI: 10.12265/j.gnss.2024090
Citation: TIAN Zongzheng, HUANG Xin, TAO Changpeng. Particle filter resampling-based GNSS multipath correction method[J]. GNSS World of China, 2024, 49(6): 36-46. DOI: 10.12265/j.gnss.2024090

Particle filter resampling-based GNSS multipath correction method

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  • Received Date: May 10, 2024
  • Available Online: December 19, 2024
  • Multipath is a critical factor hindering the provision of high-precision satellite positioning services. Notably, current multipath models have failed to effectively address the challenges posed by noise distribution and processing timeliness. In this paper, we introduce a novel correction method for GNSS pseudorange multipath, leveraging particle filter resampling. Initially, we establish the initial state space model of the particle filter, utilizing the original pseudorange multipath sequence. Subsequently, we consider the noise propagation during the particle update process and predict the distribution of particles and their weights at the subsequent time step. Crucially, we update the multipath particles, taking into account the satellite elevation angle, to obtain a weighted and sliding output of the single epoch multipath delay. Analysis of MGEX static observation data reveals that our proposed GNSS multipath correction method significantly improves the STD by 59.2%, 58%, 61.7%, and 59.8% for BDS, GPS, GLONASS, and Galileo pseudorange multipath, respectively. The PPP positioning accuracy has undergone significant enhancements, recording improvements of 10.68%, 11.95%, and 26.13% in the E, N, and U directions, respectively. The accuracy of dynamic positioning in the E, N, and U directions underwent respective enhancements of 5.9%, 1.1%, and a substantial 53.4%. Consequently, the proposed correction method for addressing GNSS multipath effects holds substantial significance in augmenting the performance of location services and plays a pivotal role in enhancing data preprocessing models.

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