Indoor Localization System Based on RSSI-APIT Algorithm
An indoor localization system based on the RSSI-APIT algorithm is designed in this study. Integrated RSSI (received signal strength indication) and non-ranging APIT (approximate perfect point-in-triangulation test) localization methods are fused with machine learning in order to improve the accuracy...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/24/9620 |
| _version_ | 1797379437172359168 |
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| author | Xiaoyan Shen Boyang Xu Hongming Shen |
| author_facet | Xiaoyan Shen Boyang Xu Hongming Shen |
| author_sort | Xiaoyan Shen |
| collection | DOAJ |
| description | An indoor localization system based on the RSSI-APIT algorithm is designed in this study. Integrated RSSI (received signal strength indication) and non-ranging APIT (approximate perfect point-in-triangulation test) localization methods are fused with machine learning in order to improve the accuracy of the indoor localization system. The system focuses on the improvement of preprocessing and localization algorithms. The primary objective of the system is to enhance the preprocessing of the acquired RSSI data and optimize the localization algorithm in order to enhance the precision of the coordinates in the indoor localization system. In order to mitigate the issue of significant fluctuations in RSSI, a technique including the integration of Gaussian filtering and an artificial neural network (ANN) is employed. This approach aims to preprocess the acquired RSSI data, thus reducing the impact of multipath effects. In order to address the issue of low localization accuracy encountered by the conventional APIT localization algorithm during wide-area localization, the RSSI ranging function is incorporated into the APIT localization algorithm. This addition serves to further narrow down the localization area. Consequently, the resulting localization algorithm is referred to as the RSSI-APIT positioning algorithm. Experimental results have demonstrated the successful reduction of inherent localization errors within the system by employing the RSSI-APIT positioning algorithm. The present study aims to investigate the impact of the localization scene and the number of anchors on the RSSI-APIT localization algorithm, with the objective of enhancing the performance of the indoor localization system. The conducted experiments demonstrated that the enhanced system exhibits several advantages. Firstly, it successfully decreased the frequency of anchor calls, resulting in a reduction in the overall operating cost of the system. Additionally, it effectively enhanced the accuracy and stability of the system’s localization capabilities. In a complex environment of 100 m<sup>2</sup> in size, compared with the traditional trilateral localization method and the APIT localization algorithm, the RSSI-APIT localization algorithm reduced the localization error by about 2.9 m and 1.8 m, respectively, and the overall error was controlled within 1.55 m. |
| first_indexed | 2024-03-08T20:23:13Z |
| format | Article |
| id | doaj.art-98e544220e9146618781e89b5b7d0893 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T20:23:13Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-98e544220e9146618781e89b5b7d08932023-12-22T14:39:57ZengMDPI AGSensors1424-82202023-12-012324962010.3390/s23249620Indoor Localization System Based on RSSI-APIT AlgorithmXiaoyan Shen0Boyang Xu1Hongming Shen2School of Information Science and Technology, Nantong University, Nantong 226019, ChinaSchool of Information Science and Technology, Nantong University, Nantong 226019, ChinaSchool of Information Science and Technology, Nantong University, Nantong 226019, ChinaAn indoor localization system based on the RSSI-APIT algorithm is designed in this study. Integrated RSSI (received signal strength indication) and non-ranging APIT (approximate perfect point-in-triangulation test) localization methods are fused with machine learning in order to improve the accuracy of the indoor localization system. The system focuses on the improvement of preprocessing and localization algorithms. The primary objective of the system is to enhance the preprocessing of the acquired RSSI data and optimize the localization algorithm in order to enhance the precision of the coordinates in the indoor localization system. In order to mitigate the issue of significant fluctuations in RSSI, a technique including the integration of Gaussian filtering and an artificial neural network (ANN) is employed. This approach aims to preprocess the acquired RSSI data, thus reducing the impact of multipath effects. In order to address the issue of low localization accuracy encountered by the conventional APIT localization algorithm during wide-area localization, the RSSI ranging function is incorporated into the APIT localization algorithm. This addition serves to further narrow down the localization area. Consequently, the resulting localization algorithm is referred to as the RSSI-APIT positioning algorithm. Experimental results have demonstrated the successful reduction of inherent localization errors within the system by employing the RSSI-APIT positioning algorithm. The present study aims to investigate the impact of the localization scene and the number of anchors on the RSSI-APIT localization algorithm, with the objective of enhancing the performance of the indoor localization system. The conducted experiments demonstrated that the enhanced system exhibits several advantages. Firstly, it successfully decreased the frequency of anchor calls, resulting in a reduction in the overall operating cost of the system. Additionally, it effectively enhanced the accuracy and stability of the system’s localization capabilities. In a complex environment of 100 m<sup>2</sup> in size, compared with the traditional trilateral localization method and the APIT localization algorithm, the RSSI-APIT localization algorithm reduced the localization error by about 2.9 m and 1.8 m, respectively, and the overall error was controlled within 1.55 m.https://www.mdpi.com/1424-8220/23/24/9620indoor localization systemreceived signal strength indicationRSSI-APIT algorithmANN |
| spellingShingle | Xiaoyan Shen Boyang Xu Hongming Shen Indoor Localization System Based on RSSI-APIT Algorithm Sensors indoor localization system received signal strength indication RSSI-APIT algorithm ANN |
| title | Indoor Localization System Based on RSSI-APIT Algorithm |
| title_full | Indoor Localization System Based on RSSI-APIT Algorithm |
| title_fullStr | Indoor Localization System Based on RSSI-APIT Algorithm |
| title_full_unstemmed | Indoor Localization System Based on RSSI-APIT Algorithm |
| title_short | Indoor Localization System Based on RSSI-APIT Algorithm |
| title_sort | indoor localization system based on rssi apit algorithm |
| topic | indoor localization system received signal strength indication RSSI-APIT algorithm ANN |
| url | https://www.mdpi.com/1424-8220/23/24/9620 |
| work_keys_str_mv | AT xiaoyanshen indoorlocalizationsystembasedonrssiapitalgorithm AT boyangxu indoorlocalizationsystembasedonrssiapitalgorithm AT hongmingshen indoorlocalizationsystembasedonrssiapitalgorithm |