An Enhanced ZigBee-Based Indoor Localization Method Using Multi-Stage RSSI Filtering and LQI-Aware MLE
Accurate indoor localization in wireless sensor networks remains a non-trivial challenge, particularly in complex environments characterized by signal variability and multipath propagation. This study presents a ZigBee-based localization approach that integrates multi-stage preprocessing of received...
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| Format: | Article |
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MDPI AG
2025-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/9/2947 |
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| author | Jianming Li Shuyan Yu Zhe Wei Zhanpeng Zhou |
| author_facet | Jianming Li Shuyan Yu Zhe Wei Zhanpeng Zhou |
| author_sort | Jianming Li |
| collection | DOAJ |
| description | Accurate indoor localization in wireless sensor networks remains a non-trivial challenge, particularly in complex environments characterized by signal variability and multipath propagation. This study presents a ZigBee-based localization approach that integrates multi-stage preprocessing of received signal strength indicator (RSSI) data with a reliability-aware extension of the maximum likelihood estimation (MLE) algorithm. To improve measurement stability, a hybrid filtering framework combining Kalman filtering, Dixon’s Q test, Gaussian smoothing, and mean averaging is applied to reduce the influence of noise and outliers. Building on the filtered data, the proposed method introduces a noise and link quality indicator (LQI)-based dynamic weighting mechanism that adjusts the contribution of each distance estimate during localization. The approach was evaluated under simulated and semi-physical non-line-of-sight (NLOS) indoor conditions designed to reflect practical deployment scenarios. While based on a limited set of representative test points, the method yielded improved positioning consistency and achieved an average accuracy gain of 11.7% over conventional MLE in the tested environments. These results suggest that the proposed method may offer a feasible solution for resource-constrained localization applications requiring robustness to signal degradation. |
| format | Article |
| id | doaj-art-bbb74de4cd8248019aa69d7f2d91ea63 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-bbb74de4cd8248019aa69d7f2d91ea632025-08-20T03:53:01ZengMDPI AGSensors1424-82202025-05-01259294710.3390/s25092947An Enhanced ZigBee-Based Indoor Localization Method Using Multi-Stage RSSI Filtering and LQI-Aware MLEJianming Li0Shuyan Yu1Zhe Wei2Zhanpeng Zhou3School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, ChinaYuanpei College, Shaoxing University, Shaoxing 312000, ChinaSchool of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, ChinaSchool of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, ChinaAccurate indoor localization in wireless sensor networks remains a non-trivial challenge, particularly in complex environments characterized by signal variability and multipath propagation. This study presents a ZigBee-based localization approach that integrates multi-stage preprocessing of received signal strength indicator (RSSI) data with a reliability-aware extension of the maximum likelihood estimation (MLE) algorithm. To improve measurement stability, a hybrid filtering framework combining Kalman filtering, Dixon’s Q test, Gaussian smoothing, and mean averaging is applied to reduce the influence of noise and outliers. Building on the filtered data, the proposed method introduces a noise and link quality indicator (LQI)-based dynamic weighting mechanism that adjusts the contribution of each distance estimate during localization. The approach was evaluated under simulated and semi-physical non-line-of-sight (NLOS) indoor conditions designed to reflect practical deployment scenarios. While based on a limited set of representative test points, the method yielded improved positioning consistency and achieved an average accuracy gain of 11.7% over conventional MLE in the tested environments. These results suggest that the proposed method may offer a feasible solution for resource-constrained localization applications requiring robustness to signal degradation.https://www.mdpi.com/1424-8220/25/9/2947indoor positioningZigBeeRSSI filteringmaximum likelihood estimationlink quality indicator |
| spellingShingle | Jianming Li Shuyan Yu Zhe Wei Zhanpeng Zhou An Enhanced ZigBee-Based Indoor Localization Method Using Multi-Stage RSSI Filtering and LQI-Aware MLE Sensors indoor positioning ZigBee RSSI filtering maximum likelihood estimation link quality indicator |
| title | An Enhanced ZigBee-Based Indoor Localization Method Using Multi-Stage RSSI Filtering and LQI-Aware MLE |
| title_full | An Enhanced ZigBee-Based Indoor Localization Method Using Multi-Stage RSSI Filtering and LQI-Aware MLE |
| title_fullStr | An Enhanced ZigBee-Based Indoor Localization Method Using Multi-Stage RSSI Filtering and LQI-Aware MLE |
| title_full_unstemmed | An Enhanced ZigBee-Based Indoor Localization Method Using Multi-Stage RSSI Filtering and LQI-Aware MLE |
| title_short | An Enhanced ZigBee-Based Indoor Localization Method Using Multi-Stage RSSI Filtering and LQI-Aware MLE |
| title_sort | enhanced zigbee based indoor localization method using multi stage rssi filtering and lqi aware mle |
| topic | indoor positioning ZigBee RSSI filtering maximum likelihood estimation link quality indicator |
| url | https://www.mdpi.com/1424-8220/25/9/2947 |
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