{'fraction_non_alphanumeric': 0.02912621359223301, 'fraction_numerical': 0.005227781926811053, 'mean_word_length': 5.261682242990654, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
MP mitigation in GNSS positioning by GRU NNs and adaptive wavelet filtering

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%.