Abstract
Multipath is the main systematic error of the Global Navigation Satellite System (GNSS) short baseline positioning. Multipath cannot be eliminated by the double-differenced technique and is difficult to parameterize, which severely restrict the high-precision GNSS positioning application. Based on the spatiotemporal repeatability of multipath, the sidereal filtering in coordinate-domain (SF-CD), the sidereal filtering in observation-domain (SF-OD), and the multipath hemispherical map (MHM) can be used to mitigate the multipath in real-time. However, the multipath model with large matrix for multi-GNSS multipath mitigation is difficult to achieve lightweight calculation and the SF-CD cannot be applied to mitigate the multi-GNSS multipath. In this paper, we propose a new multipath mitigation strategy in the coordinate-domain that shakes off the formation mechanism of multipath, a CNN (convolutional neural network)-LSTM (long short-term memory) method is used to mine the deep multipath features in GNSS coordinate series. Furthermore, multipath will be mitigated in real-time by constantly predicting the value of the next epoch. The experimental results show that the CNN-LSTM effectively mitigates the multi-GNSS multipath. The method can reduce the average RMS (root-mean square) of multi-GNSS positioning errors in the east, north, and vertical directions by 62.3%, 70.8%, and 66.0%. Moreover, comparing with the SF-CD, SF-OD, and MHM, CNN-LSTM can more effectively mitigate the effects of the GPS multipath, and the ability of multipath mitigation is almost not affected over time.
Highlights
Basic Principle and AlgorithmWe firstly describe the mechanism of multipath generation, and we introduce the basic principles of CNN and long short-term memory (LSTM)
Introduction e Global Navigation SatelliteSystem (GNSS), as a realtime and high-precision positioning technology, is widely used in many fields such as navigation, geodesy, deformation monitoring, and photogrammetry [1,2,3,4]
It is challenging to build a mathematical model of Global Navigation Satellite System (GNSS) multipath due to this complex nonlinearity [43]. erefore, we propose the use of deep learning methods for feature mining of multipath in GNSS coordinate series, real-time prediction, and mitigation of multipath. e specific deep learning method used in this paper is the combination of the convolutional neural network and long short-term memory (CNN-LSTM), which combined the advantages of local feature extraction of CNN and the prediction ability advantage of LSTM [44, 45]
Summary
We firstly describe the mechanism of multipath generation, and we introduce the basic principles of CNN and LSTM. Based on the principle of the algorithms, CNN has a strong ability to mine local features of data, and LSTM can better obtain time-series features and has an excellent predictive ability. Is work can be divided into two parts: one is to build a CNN-LSTM network model based on a certain amount of training data; the other is based on the established network model to perform real-time prediction and multipath mitigation. E CNN-LSTM network model is trained on the training data, which is described as follows: firstly, we apply the convolution layers to perform convolution calculations on the data in each time series to extract local features and apply the pooling layers to transform the output dimensions. Its parameters setting is as follows: the number of convolution filters is 3, the convolution kernel size is 12, the dropout is 0.1, and the number of hidden layers in the LSTM is 12
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