An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based Positioning
Received signal strength indicator (RSSI)-based positioning is suitable for large-scale applications due to its advantages of low cost and high accuracy. However, it suffers from low stability because RSSI is easily blocked and easily interfered with by objects and environmental effects. Therefore,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/15/4244 |
| _version_ | 1797560877028737024 |
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| author | Yong Shi Wenzhong Shi Xintao Liu Xianjian Xiao |
| author_facet | Yong Shi Wenzhong Shi Xintao Liu Xianjian Xiao |
| author_sort | Yong Shi |
| collection | DOAJ |
| description | Received signal strength indicator (RSSI)-based positioning is suitable for large-scale applications due to its advantages of low cost and high accuracy. However, it suffers from low stability because RSSI is easily blocked and easily interfered with by objects and environmental effects. Therefore, this paper proposed a tri-partition RSSI classification and its tracing algorithm as an RSSI filter. The proposed filter shows an available feature, where small test RSSI samples gain a low deviation of less than 1 dBm from a large RSSI sample collected about 10 min, and the sub-classification RSSIs conform to normal distribution when the minimum sample count is greater than 20. The proposed filter also offers several advantages compared to the mean filter, including lower variance range with an overall range of around 1 dBm, 25.9% decreased sample variance, and 65% probability of mitigating RSSI left-skewness. We experimentally confirmed the proposed filter worked in the path-loss exponent fitting and location computing, and a 4.45-fold improvement in positioning stability based on the sample standard variance, and positioning accuracy improved by 20.5% with an overall error of less than 1.46 m. |
| first_indexed | 2024-03-10T18:06:31Z |
| format | Article |
| id | doaj.art-c0be000f8c4c4ade9578f8fbaf8ff0af |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T18:06:31Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c0be000f8c4c4ade9578f8fbaf8ff0af2023-11-20T08:27:53ZengMDPI AGSensors1424-82202020-07-012015424410.3390/s20154244An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based PositioningYong Shi0Wenzhong Shi1Xintao Liu2Xianjian Xiao3School of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 21300, ChinaDepartment of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong KongDepartment of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong KongSchool of Computer Information and Engineering, Changzhou Institute of Technology, Changzhou 21300, ChinaReceived signal strength indicator (RSSI)-based positioning is suitable for large-scale applications due to its advantages of low cost and high accuracy. However, it suffers from low stability because RSSI is easily blocked and easily interfered with by objects and environmental effects. Therefore, this paper proposed a tri-partition RSSI classification and its tracing algorithm as an RSSI filter. The proposed filter shows an available feature, where small test RSSI samples gain a low deviation of less than 1 dBm from a large RSSI sample collected about 10 min, and the sub-classification RSSIs conform to normal distribution when the minimum sample count is greater than 20. The proposed filter also offers several advantages compared to the mean filter, including lower variance range with an overall range of around 1 dBm, 25.9% decreased sample variance, and 65% probability of mitigating RSSI left-skewness. We experimentally confirmed the proposed filter worked in the path-loss exponent fitting and location computing, and a 4.45-fold improvement in positioning stability based on the sample standard variance, and positioning accuracy improved by 20.5% with an overall error of less than 1.46 m.https://www.mdpi.com/1424-8220/20/15/4244trilateral indoor positioningRSSI filterRSSI classificationstabilityaccuracy |
| spellingShingle | Yong Shi Wenzhong Shi Xintao Liu Xianjian Xiao An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based Positioning Sensors trilateral indoor positioning RSSI filter RSSI classification stability accuracy |
| title | An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based Positioning |
| title_full | An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based Positioning |
| title_fullStr | An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based Positioning |
| title_full_unstemmed | An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based Positioning |
| title_short | An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based Positioning |
| title_sort | rssi classification and tracing algorithm to improve trilateration based positioning |
| topic | trilateral indoor positioning RSSI filter RSSI classification stability accuracy |
| url | https://www.mdpi.com/1424-8220/20/15/4244 |
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