A Real-Time BLE/PDR Integrated System by Using an Improved Robust Filter for Indoor Position
Source abstract
Abstract
Indoor position technologies have attracted the attention of many researchers. To provide a real-time indoor position system with high precision and stability is necessary under many circumstances. In a real-time position scenario, gross errors of the Bluetooth low energy (BLE) fingerprint method are more easily occurring and the heading angle of the pedestrian will drift without acceleration and magnetic field compensation. A real-time BLE/pedestrian dead-reckoning (PDR) integrated system by using an improved robust filter has been proposed. In the PDR method, the improved Mahony complementary filter based on the pedestrian motion states is adopted to estimate the heading angle reducing the drift error. Then, an improved robust filter is utilized to detect and restrain the gross error of the BLE fingerprint method. The robust filter detected the gross error at different granularity by constructing a robust vector changing the observation covariance matrix of the extended Kalman filter (EKF) adaptively when the application is running. Several experiments are conducted in the true position scenario. The mean position accuracy obtained by the proposed method in the experiment is 0.844 m and RMSE is 0.74 m. Compared with the classic EKF, these two values are increased by 38% and 18%, respectively. The results show that the improved filter can avoid the gross error in the BLE method and provide high precision and scalability in indoor position service.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Indoor and Outdoor Localization Technologies · Target Tracking and Data Fusion in Sensor Networks · Inertial Sensor and Navigation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Key Laboratory of Land Environment and Disaster Monitoring, MNR, China University of Mining and Technology, Xuzhou 221116, China
School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Funding
National Key Research and Development Program of China
References (56)
Show source-provided references
1. A novel WiFi indoor positioning strategy based on weighted squared Euclidean distance and local principal gradient direction10.1108/SR-06-2017-0109 ↗
2. Augmentation of Fingerprints for Indoor WiFi Localization Based on Gaussian Process Regression10.1109/TVT.2018.2870160 ↗
3. A Novel Convolutional Neural Network Based Indoor Localization Framework With WiFi Fingerprinting10.1109/ACCESS.2019.2933921 ↗
4. Technological Viability Assessment of Bluetooth Low Energy Technology for Indoor Localization10.1061/(ASCE)CP.1943-5487.0000778 ↗
5. Bayesian Fusion for Indoor Positioning Using Bluetooth Fingerprints10.1007/s11277-012-0777-1 ↗
6. An RFID Indoor Positioning Algorithm Based on Bayesian Probability and K-Nearest Neighbor10.3390/s17081806 ↗
7. Real-Time Locating Systems Using Active RFID for Internet of Things10.1109/JSYST.2014.2346625 ↗
8. Design of Chirp Waveforms for Multiple-Access Ultrasonic Indoor Positioning10.1109/JSEN.2018.2846481 ↗
9. Lee, C., Chang, Y., Park, G., Ryu, J., Jeong, S.-G., Park, S., Park, J.W., Lee, H.C., Hong, K.-S., and Lee, M.H. (2004, January 2–6). Indoor positioning system based on incident angles of infrared emitters. Proceedings of the 30th Annual Conference of IEEE Industrial Electronics Society, IECON 2004, Busan, Korea.
10. A decision tree-based NLOS detection method for the UWB indoor location tracking accuracy improvement10.1002/dac.3997 ↗
11. A Novel NLOS Mitigation Algorithm for UWB Localization in Harsh Indoor Environments10.1109/TVT.2018.2883810 ↗
12. Precise calibration method of pseudolite positions in indoor navigation systems10.1016/S0898-1221(03)90205-7 ↗
13. Performance analysis of indoor pseudolite positioning based on the unscented Kalman filter10.1007/s10291-019-0870-y ↗
14. An Indoor Positioning System Based on Static Objects in Large Indoor Scenes by Using Smartphone Cameras10.3390/s18072229 ↗
15. Indoor Positioning and Navigation with Camera Phones10.1109/MPRV.2009.30 ↗
16. (2021, June 18). Google. Wi-Fi Scanning Overview. Available online: https://developer.android.google.cn/guide/topics/connectivity/wifi-scan.
17. Bluetooth Indoor Positioning Based on RSSI and Kalman Filter10.1007/s11277-017-4371-4 ↗
18. Cao, H., Wang, Y., Bi, J., and Qi, H. (2019). An Adaptive Bluetooth/Wi-Fi Fingerprint Positioning Method based on Gaussian Process Regression and Relative Distance. Sensors, 19.10.3390/s19122784 ↗
19. Gain without Pain: Accurate WiFi-based Localization using Fingerprint Spatial Gradient
20. Indoor Localization Improved by Spatial Context—A Survey10.1145/3322241 ↗
21. Kenn, H., Behrens, N., and Kleiner, A. (2007, January 12–13). Optimizing indoor PDR performance with self-deployed position markers. Proceedings of the 4th International Forum on Applied Wearable Computing 2007, Tel Aviv, Israel.
22. Zuo, Z., Liu, L., Zhang, L., and Fang, Y. (2018). Indoor Positioning Based on Bluetooth Low-Energy Beacons Adopting Graph Optimization. Sensors, 18.10.3390/s18113736 ↗
23. Optimizing AP and Beacon Placement in WiFi and BLE hybrid localization10.1016/j.jnca.2020.102673 ↗
24. High Resolution Beacon-Based Proximity Detection for Dense Deployment10.1109/TMC.2017.2759734 ↗
25. Improving BLE Beacon Proximity Estimation Accuracy Through Bayesian Filtering10.1109/JIOT.2020.2965583 ↗
26. Subhan, F., Khan, A., Saleem, S., Ahmed, S., Imran, M., Asghar, Z., and Bangash, J.I. (2019). Experimental analysis of received signals strength in Bluetooth Low Energy (BLE) and its effect on distance and position estimation. Trans. Emerg. Telecommun. Technol.10.1002/ett.3793 ↗
27. Zhao, X., Xiao, Z., Markham, A., Trigoni, N., and Ren, Y. (2014, January 14–16). Does BTLE measure up against wifi? A comparison of indoor location performance. Proceedings of the 20th European Wireless Conference, EW 2014, Barcelona, Spain.
28. Pakanon, N., Chamchoy, M., and Supanakoon, P. (2020, January 1–4). Study on Accuracy of Trilateration Method for Indoor Positioning with BLE Beacons. Proceedings of the 2020 6th International Conference on Engineering, Applied Sciences and Technology (ICEAST), Chiang Mai, Thailand.10.1109/ICEAST50382.2020.9165464 ↗
29. Bahl, P., and Padmanabhan, V.N. (2000, January 26–30). RADAR: An in-Building RF-Based User Location and Tracking System. Proceedings of the IEEE INFOCOM 2000 Conference on Computer Communications, Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064), Tel Aviv, Israel.
30. Wang, X., Gao, L., Mao, S., and Pandey, S. (2015, January 9–12). DeepFi: Deep learning for indoor fingerprinting using channel state information. Proceedings of the 2015 IEEE Wireless Communications and Networking Conference (WCNC), New Orleans, LA, USA.
31. Yan, H., Shan, Q., and Furukawa, Y. (2018, January 8–14). RIDI: Robust IMU Double Integration. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01261-8_38 ↗
32. Chen, C., Lu, X., Markham, A., and Trigoni, N. (2018, January 2–7). IONet: Learning to Cure the Curse of Drift in Inertial Odometry. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.10.1609/aaai.v32i1.12102 ↗
33. Herath, S., Yan, H., and Furukawa, Y. (August, January 31). RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, & New Methods. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.10.1109/ICRA40945.2020.9196860 ↗
34. TLIO: Tight Learned Inertial Odometry10.1109/LRA.2020.3007421 ↗
35. Sun, S., Melamed, D., and Kitani, K. (2021). IDOL: Inertial Deep Orientation-Estimation and Localization. arXiv.10.1609/aaai.v35i7.16763 ↗
36. Pedestrian dead reckoning—A solution to navigation in GPS signal degraded areas?
37. Jahn, J., Batzer, U., Seitz, J., Patino-Studencka, L., and Boronat, J.G. (2010, January 15–17). Comparison and evaluation of acceleration based step length estimators for handheld devices. Proceedings of the 2010 International Conference on Indoor Positioning and Indoor Navigation, Zurich, Switzerland.10.1109/IPIN.2010.5646888 ↗
38. Hybrid Indoor Positioning System for Pedestrians with Swinging Arms Based on Smartphone IMU and RSSI of BLE
39. Li, X., Wang, J., Liu, C., Zhang, L., and Li, Z. (2016). Integrated WiFi/PDR/Smartphone Using an Adaptive System Noise Extended Kalman Filter Algorithm for Indoor Localization. ISPRS Int. J. Geo-Inf., 5.10.3390/ijgi5020008 ↗
40. Extended Kalman Filter for Real Time Indoor Localization by Fusing WiFi and Smartphone Inertial Sensors10.3390/mi6040523 ↗
41. Atia, M., Iqbal, U., Givigi, S., Noureldin, A., and Korenberg, M. (2015, January 26–28). Adaptive Integrated Indoor Pedestrian Tracking System Using MEMS sensors and Hybrid WiFi/Bluetooth-Beacons with Optimized Grid-based Bayesian Filtering Algorithm. Proceedings of the 2015 International Technical Meeting of the Institute of Navigation, Dana Point, CA, USA.
42. Adaptively robust filtering for kinematic geodetic positioning10.1007/s001900000157 ↗
43. Robust estimator for correlated observations based on bifactor equivalent weights10.1007/s00190-002-0256-7 ↗
44. Robust Kalman filtering based on Mahalanobis distance as outlier judging criterion10.1007/s00190-013-0690-8 ↗
45. Li, Z., Liu, C., Gao, J., and Li, X. (2016). An Improved WiFi/PDR Integrated System Using an Adaptive and Robust Filter for Indoor Localization. Int. J. Geo-Inf., 5.10.3390/ijgi5120224 ↗
46. The effects of AP number on WiFi fingerprint positioning
47. Robust and Accurate Smartphone-Based Step Counting for Indoor Localization10.1109/JSEN.2017.2685999 ↗
48. PDR/INS/WiFi Integration Based on Handheld Devices for Indoor Pedestrian Navigation10.3390/mi6060793 ↗
49. Rai, A., Chintalapudi, K., Padmanabhan, V., and Sen, R. (2012, January 22–26). Zee: Zero-effort crowdsourcing for indoor localization. Proceedings of the 18th Annual International Conference on Mobile Computing and Networking, Istanbul, Turkey.10.1145/2348543.2348580 ↗
50. Sun, M., Wang, Y., Xu, S., Cao, H., and Si, M. (2020). Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter. Appl. Sci., 10.10.3390/app10020668 ↗
51. Ladetto, Q. (2000, January 19–22). On foot navigation: Continuous step calibration using both complementary recursive prediction and adaptive Kalman filtering. Proceedings of the 13th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS 2000), Salt Lake City, UT, USA.
52. Location Estimation Indoors by Means of Small Computing Power Devices, Accelerometers, Magnetic Sensors, and Map Knowledge
53. Using the ADXL202 in pedometer and personal navigation applications
54. A Hybrid Smartphone Indoor Positioning Solution for Mobile LBS10.3390/s121217208 ↗
55. Wang, H., Sen, S., Elgohary, A., Farid, M., Youssef, M., and Choudhury, R.R. (2012, January 26–28). No need to war-drive: Unsupervised indoor localization. Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, Low Wood Bay, UK.10.1145/2307636.2307655 ↗
56. Nonlinear Complementary Filters on the Special Orthogonal Group10.1109/TAC.2008.923738 ↗