Multi-Layer Perceptron Model Integrating Multi-Head Attention and Gating Mechanism for Global Navigation Satellite System Positioning Error Estimation
To better understand and evaluate the GNSS positioning performance, it is convenient to adopt corresponding measures to reduce the impact of errors on positioning. A GNSS positioning error estimation scheme based on an improved multi-layer perceptron model is proposed. The multi-head attention mecha...
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| author | Xiuxun Liu Zuping Tang Jiaolong Wei |
| author_facet | Xiuxun Liu Zuping Tang Jiaolong Wei |
| author_sort | Xiuxun Liu |
| collection | DOAJ |
| description | To better understand and evaluate the GNSS positioning performance, it is convenient to adopt corresponding measures to reduce the impact of errors on positioning. A GNSS positioning error estimation scheme based on an improved multi-layer perceptron model is proposed. The multi-head attention mechanism and gating operation are integrated into the multi-layer perceptron model to adaptively select and filter features, enhancing the model’s ability to understand input features. First, the original positioning error of the satellite is obtained through the Kalman filter positioning method. The data are then preprocessed to extract available features. Finally, the features are input into the constructed model for training and testing to obtain the estimated positioning error value. Two types of comparative experiments were completed. The performance of the presented model is evaluated by the root mean square error. Experimental results show that the proposed method performs well in terms of performance indicators, and has obvious advantages over other state-of-the-art methods. In particular, the root mean square error of the presented method in the first dataset is 0.239 m, which is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>39.2</mn><mo>%</mo></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>17</mn><mo>%</mo></mrow></semantics></math></inline-formula> lower than the current state-of-the-art long short-term memory network and convolutional neural network, respectively. The presented method can provide higher-precision estimated values for studying the GNSS positioning error estimation problem. |
| format | Article |
| id | doaj-art-15ba6006adc642559b074f4ab2f6b277 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-15ba6006adc642559b074f4ab2f6b2772025-01-24T13:48:03ZengMDPI AGRemote Sensing2072-42922025-01-0117230110.3390/rs17020301Multi-Layer Perceptron Model Integrating Multi-Head Attention and Gating Mechanism for Global Navigation Satellite System Positioning Error EstimationXiuxun Liu0Zuping Tang1Jiaolong Wei2School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, ChinaTo better understand and evaluate the GNSS positioning performance, it is convenient to adopt corresponding measures to reduce the impact of errors on positioning. A GNSS positioning error estimation scheme based on an improved multi-layer perceptron model is proposed. The multi-head attention mechanism and gating operation are integrated into the multi-layer perceptron model to adaptively select and filter features, enhancing the model’s ability to understand input features. First, the original positioning error of the satellite is obtained through the Kalman filter positioning method. The data are then preprocessed to extract available features. Finally, the features are input into the constructed model for training and testing to obtain the estimated positioning error value. Two types of comparative experiments were completed. The performance of the presented model is evaluated by the root mean square error. Experimental results show that the proposed method performs well in terms of performance indicators, and has obvious advantages over other state-of-the-art methods. In particular, the root mean square error of the presented method in the first dataset is 0.239 m, which is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>39.2</mn><mo>%</mo></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>17</mn><mo>%</mo></mrow></semantics></math></inline-formula> lower than the current state-of-the-art long short-term memory network and convolutional neural network, respectively. The presented method can provide higher-precision estimated values for studying the GNSS positioning error estimation problem.https://www.mdpi.com/2072-4292/17/2/301global navigation satellite systempositioning error estimationmachine learningmulti-layer perceptron model |
| spellingShingle | Xiuxun Liu Zuping Tang Jiaolong Wei Multi-Layer Perceptron Model Integrating Multi-Head Attention and Gating Mechanism for Global Navigation Satellite System Positioning Error Estimation Remote Sensing global navigation satellite system positioning error estimation machine learning multi-layer perceptron model |
| title | Multi-Layer Perceptron Model Integrating Multi-Head Attention and Gating Mechanism for Global Navigation Satellite System Positioning Error Estimation |
| title_full | Multi-Layer Perceptron Model Integrating Multi-Head Attention and Gating Mechanism for Global Navigation Satellite System Positioning Error Estimation |
| title_fullStr | Multi-Layer Perceptron Model Integrating Multi-Head Attention and Gating Mechanism for Global Navigation Satellite System Positioning Error Estimation |
| title_full_unstemmed | Multi-Layer Perceptron Model Integrating Multi-Head Attention and Gating Mechanism for Global Navigation Satellite System Positioning Error Estimation |
| title_short | Multi-Layer Perceptron Model Integrating Multi-Head Attention and Gating Mechanism for Global Navigation Satellite System Positioning Error Estimation |
| title_sort | multi layer perceptron model integrating multi head attention and gating mechanism for global navigation satellite system positioning error estimation |
| topic | global navigation satellite system positioning error estimation machine learning multi-layer perceptron model |
| url | https://www.mdpi.com/2072-4292/17/2/301 |
| work_keys_str_mv | AT xiuxunliu multilayerperceptronmodelintegratingmultiheadattentionandgatingmechanismforglobalnavigationsatellitesystempositioningerrorestimation AT zupingtang multilayerperceptronmodelintegratingmultiheadattentionandgatingmechanismforglobalnavigationsatellitesystempositioningerrorestimation AT jiaolongwei multilayerperceptronmodelintegratingmultiheadattentionandgatingmechanismforglobalnavigationsatellitesystempositioningerrorestimation |