Improved SSA-Based GRU Neural Network for BDS-3 Satellite Clock Bias Forecasting
Satellite clock error is a key factor affecting the positioning accuracy of a global navigation satellite system (GNSS). In this paper, we use a gated recurrent unit (GRU) neural network to construct a satellite clock bias forecasting model for the BDS-3 navigation system. In order to further improv...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/4/1178 |
| _version_ | 1797297031218200576 |
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| author | Hongjie Liu Feng Liu Yao Kong Chaozhong Yang |
| author_facet | Hongjie Liu Feng Liu Yao Kong Chaozhong Yang |
| author_sort | Hongjie Liu |
| collection | DOAJ |
| description | Satellite clock error is a key factor affecting the positioning accuracy of a global navigation satellite system (GNSS). In this paper, we use a gated recurrent unit (GRU) neural network to construct a satellite clock bias forecasting model for the BDS-3 navigation system. In order to further improve the prediction accuracy and stability of the GRU, this paper proposes a satellite clock bias forecasting model, termed ITSSA-GRU, which combines the improved sparrow search algorithm (SSA) and the GRU, avoiding the problems of GRU’s sensitivity to hyperparameters and its tendency to fall into local optimal solutions. The model improves the initialization population phase of the SSA by introducing iterative chaotic mapping and adopts an iterative update strategy based on t-step optimization to enhance the optimization ability of the SSA. Five models, namely, ITSSA-GRU, SSA-GRU, GRU, LSTM, and GM(1,1), are used to forecast the satellite clock bias data in three different types of orbits of the BDS-3 system: MEO, IGSO, and GEO. The experimental results show that, as compared with the other four models, the ITSSA-GRU model has a stronger generalization ability and forecasting effect in the clock bias forecasting of all three types of satellites. Therefore, the ITSSA-GRU model can provide a new means of improving the accuracy of navigation satellite clock bias forecasting to meet the needs of high-precision positioning. |
| first_indexed | 2024-03-07T22:15:21Z |
| format | Article |
| id | doaj.art-d02eb2dd524c431092af92e86807c2fd |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-07T22:15:21Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d02eb2dd524c431092af92e86807c2fd2024-02-23T15:33:46ZengMDPI AGSensors1424-82202024-02-01244117810.3390/s24041178Improved SSA-Based GRU Neural Network for BDS-3 Satellite Clock Bias ForecastingHongjie Liu0Feng Liu1Yao Kong2Chaozhong Yang3College of Computer Science, Xi’an Polytechnic University, Xi’an 710600, ChinaCollege of Computer Science, Xi’an Polytechnic University, Xi’an 710600, ChinaCollege of Electronics and Information, Xi’an Polytechnic University, Xi’an 710600, ChinaNational Time Service Center, Chinese Academy of Sciences, Xi’an 710600, ChinaSatellite clock error is a key factor affecting the positioning accuracy of a global navigation satellite system (GNSS). In this paper, we use a gated recurrent unit (GRU) neural network to construct a satellite clock bias forecasting model for the BDS-3 navigation system. In order to further improve the prediction accuracy and stability of the GRU, this paper proposes a satellite clock bias forecasting model, termed ITSSA-GRU, which combines the improved sparrow search algorithm (SSA) and the GRU, avoiding the problems of GRU’s sensitivity to hyperparameters and its tendency to fall into local optimal solutions. The model improves the initialization population phase of the SSA by introducing iterative chaotic mapping and adopts an iterative update strategy based on t-step optimization to enhance the optimization ability of the SSA. Five models, namely, ITSSA-GRU, SSA-GRU, GRU, LSTM, and GM(1,1), are used to forecast the satellite clock bias data in three different types of orbits of the BDS-3 system: MEO, IGSO, and GEO. The experimental results show that, as compared with the other four models, the ITSSA-GRU model has a stronger generalization ability and forecasting effect in the clock bias forecasting of all three types of satellites. Therefore, the ITSSA-GRU model can provide a new means of improving the accuracy of navigation satellite clock bias forecasting to meet the needs of high-precision positioning.https://www.mdpi.com/1424-8220/24/4/1178satellite clock biasGRU neural networkSSA |
| spellingShingle | Hongjie Liu Feng Liu Yao Kong Chaozhong Yang Improved SSA-Based GRU Neural Network for BDS-3 Satellite Clock Bias Forecasting Sensors satellite clock bias GRU neural network SSA |
| title | Improved SSA-Based GRU Neural Network for BDS-3 Satellite Clock Bias Forecasting |
| title_full | Improved SSA-Based GRU Neural Network for BDS-3 Satellite Clock Bias Forecasting |
| title_fullStr | Improved SSA-Based GRU Neural Network for BDS-3 Satellite Clock Bias Forecasting |
| title_full_unstemmed | Improved SSA-Based GRU Neural Network for BDS-3 Satellite Clock Bias Forecasting |
| title_short | Improved SSA-Based GRU Neural Network for BDS-3 Satellite Clock Bias Forecasting |
| title_sort | improved ssa based gru neural network for bds 3 satellite clock bias forecasting |
| topic | satellite clock bias GRU neural network SSA |
| url | https://www.mdpi.com/1424-8220/24/4/1178 |
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