Application of inertial navigation high precision positioning system based on SVM optimization
With the advancement of semiconductor technology, pedestrian navigation and positioning technology based on smartphones is becoming increasingly important in people's travel. However, precise positioning is challenging due to the use of inertial measurement units in low-cost smartphones and the...
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Elsevier
2024-12-01
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| Series: | Systems and Soft Computing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772941924000346 |
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| author | Ruiqun Han |
| author_facet | Ruiqun Han |
| author_sort | Ruiqun Han |
| collection | DOAJ |
| description | With the advancement of semiconductor technology, pedestrian navigation and positioning technology based on smartphones is becoming increasingly important in people's travel. However, precise positioning is challenging due to the use of inertial measurement units in low-cost smartphones and the complex motion states of pedestrians. To navigate and locate pedestrians in complex motion states, a method for converting between smartphone coordinate systems and navigation coordinate systems was studied and designed, and the errors of the built-in sensors of smartphones were analyzed and calibrated. In addition, support vector machines were used to optimize pedestrian trajectory prediction algorithms, and a pedestrian motion state recognition algorithm was designed based on this. To solve the classification problem of multiple human motion states, a multi classification model was constructed and adjacent gait correlation constraints were introduced to correct the classification results. Experiments indicated that the sum of squared errors for traditional algorithms estimating pedestrian trajectories was 0.92, whereas the optimized algorithms produced an improved sum of squared errors of 0.26. Consequently, the average sum of squared errors was reduced by 71.74 %, and the convergence speed increased by 55.56 %. The pedestrian trajectory prediction algorithm optimized by support vector machine could significantly lift the positioning and navigation efficiency, with a correct recognition rate of over 93 % and a position recognition accuracy of 78.8 % - 88.4 %. By optimizing recognition of the motion state of pedestrians, more accurate determination of their position and motion state can be achieved. |
| format | Article |
| id | doaj-art-ddd3f435750e43eea0f11ad2f8b9b708 |
| institution | OA Journals |
| issn | 2772-9419 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Systems and Soft Computing |
| spelling | doaj-art-ddd3f435750e43eea0f11ad2f8b9b7082025-08-20T01:58:30ZengElsevierSystems and Soft Computing2772-94192024-12-01620010510.1016/j.sasc.2024.200105Application of inertial navigation high precision positioning system based on SVM optimizationRuiqun Han0Corresponding author:; School of Mechanical and Electrical Engineering, Tianjin Bohai Vocational Technology College, Tianjin, 300402, ChinaWith the advancement of semiconductor technology, pedestrian navigation and positioning technology based on smartphones is becoming increasingly important in people's travel. However, precise positioning is challenging due to the use of inertial measurement units in low-cost smartphones and the complex motion states of pedestrians. To navigate and locate pedestrians in complex motion states, a method for converting between smartphone coordinate systems and navigation coordinate systems was studied and designed, and the errors of the built-in sensors of smartphones were analyzed and calibrated. In addition, support vector machines were used to optimize pedestrian trajectory prediction algorithms, and a pedestrian motion state recognition algorithm was designed based on this. To solve the classification problem of multiple human motion states, a multi classification model was constructed and adjacent gait correlation constraints were introduced to correct the classification results. Experiments indicated that the sum of squared errors for traditional algorithms estimating pedestrian trajectories was 0.92, whereas the optimized algorithms produced an improved sum of squared errors of 0.26. Consequently, the average sum of squared errors was reduced by 71.74 %, and the convergence speed increased by 55.56 %. The pedestrian trajectory prediction algorithm optimized by support vector machine could significantly lift the positioning and navigation efficiency, with a correct recognition rate of over 93 % and a position recognition accuracy of 78.8 % - 88.4 %. By optimizing recognition of the motion state of pedestrians, more accurate determination of their position and motion state can be achieved.http://www.sciencedirect.com/science/article/pii/S2772941924000346Indoor positioningSmartphonesInertial sensorSupport vector machinePedestrian trajectory estimation |
| spellingShingle | Ruiqun Han Application of inertial navigation high precision positioning system based on SVM optimization Systems and Soft Computing Indoor positioning Smartphones Inertial sensor Support vector machine Pedestrian trajectory estimation |
| title | Application of inertial navigation high precision positioning system based on SVM optimization |
| title_full | Application of inertial navigation high precision positioning system based on SVM optimization |
| title_fullStr | Application of inertial navigation high precision positioning system based on SVM optimization |
| title_full_unstemmed | Application of inertial navigation high precision positioning system based on SVM optimization |
| title_short | Application of inertial navigation high precision positioning system based on SVM optimization |
| title_sort | application of inertial navigation high precision positioning system based on svm optimization |
| topic | Indoor positioning Smartphones Inertial sensor Support vector machine Pedestrian trajectory estimation |
| url | http://www.sciencedirect.com/science/article/pii/S2772941924000346 |
| work_keys_str_mv | AT ruiqunhan applicationofinertialnavigationhighprecisionpositioningsystembasedonsvmoptimization |