MP mitigation in GNSS positioning by GRU NNs and adaptive wavelet filtering. Issue 17 (1st October 2019)

• Record Type: Journal Article
• Authors: Li, Dengao; Zhang, Pei; Zhao, Jumin; Cheng, Junbing; Zhao, Hongyan
• Is Part Of: IET communications. Volume 13:Issue 17(2019)
• Page Start: 2756
• Page End: 2766
• Publication Date: 2019-10-01
• Subjects: satellite navigation -- learning (artificial intelligence) -- Global Positioning System -- recurrent neural nets -- wavelet transforms -- adaptive filters; recurrent NNs -- dynamic environment -- MP signals parameters -- adaptive wavelet filter -- compensated pseudodistance -- machine learning method -- signal data -- MP signal -- positioning accuracy -- MP mitigation -- GNSS positioning -- adaptive wavelet filtering -- urban canyons -- multipath signals -- short time delay -- MP effect -- recurrent unit neural networks -- compressed sensing algorithm -- GRU‐RNN model -- GRU NN -- velocity 20.0 km/h; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2018.5792
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : In urban canyons, multipath (MP) signals have some problems such as short time delay, high dynamic, and uncontrollable. To solve this problem and mitigate the interference of MP effect on positioning, the authors proposed a new method which uses gated recurrent unit (GRU) neural networks (NNs) aided by compressed sensing algorithm to estimate parameters of MP signal accurately. They showed how to build the GRU – deformation of recurrent NNs (RNNs) – in a dynamic environment, and estimated the MP signals parameters accurately. Also, considering the special cases in a dynamic environment, they used the adaptive wavelet filter to optimise the NNs, compensated pseudo‐distance, and improved the accuracy of positioning. Like other NNs, the learning process of this machine learning method in the dynamic environment can be summarised as obtaining data from different environmental conditions. For this, they collected signal data from different speeds to train GRU–RNN model, which correctly estimated the MP signal's parameter value in a different environment and different speeds. By experimental verification, when the speed is slower than 20 km/h, GRU–RNN can reduce code tracking error to 0.08 chips, and increased the positioning accuracy by about 13%.