Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

Optimization Method of Kalman Filter Parameters Based on Genetic Algorithm for Improvement of Indoor Positioning Accuracy of BLE Beacon

BLE Beacon의 실내 측위 정확도 향상을 위한 Genetic Algorithm 기반 Kalman Filter Parameters 최적화 방법

  • Kim, Seong-Chang (Department of Computer Engineering, Kyung-Nam University) ;
  • Kim, Jin-Ho (Department of Computer Engineering, Kyung-Nam University)
  • Received : 2021.08.10
  • Accepted : 2021.09.06
  • Published : 2021.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

Beacon signals used in indoor positioning system are reflected and distorted, resulting in noise signals. KF(Kalman Filter) has been widely used to remove this noise. In order to apply the KF, optimization process considering the signal type, signal strength, and environmental elements of each product is required. In this paper, we propose a solution to the optimization problem of KF Parameters using GA(Genetic Algorithm) in BLE(Bluetooth Low Energy) Beacon-based indoor positioning system. After optimizing KF Parameters by applying the proposed technique with a certain distance between Beacon and receiver, we compared the estimated distance passed through KF with the unfiltered distance. The proposed technique is expected to reduce the time required and improve accuracy of KF Parameters optimization in an indoor positioning system based on RSSI (Received Signal Strength Indication).

실내 측위 시스템에 사용되는 Beacon의 신호는 반사 및 왜곡되어 노이즈 신호가 발생한다. 이 노이즈를 제거하기 위해 KF(Kalman Filter)가 널리 사용되어 왔다. KF를 적용하기 위해서는 각 제품의 신호 종류와 강도, 환경을 고려한 Parameters 최적화 과정이 필요하다. 본 논문에서는 BLE Beacon 기반 실내 측위 시스템에서 GA(Genetic Algorithm)를 활용한 KF Parameters의 최적화 문제 해결 방안을 제안한다. Beacon과 수신기 사이에 일정 거리를 두고 제안한 기법을 적용하여 KF Parameters를 최적화한 후, KF를 통과한 추정거리와 필터링을 거치지 않은 거리를 비교하였다. 제안하는 기법은 RSSI(Received Signal Strength Indi- cation)를 기반으로 하는 실내 측위 시스템에서 KF의 Parameters 최적화 소요시간 단축과 정확도 향상이 가능할 것으로 기대된다.

Keywords

Acknowledgement

This research was supported by University Innovation Support Project.

References

  1. J. N. Lee, H. Y. Kang, Y. T. Shin, and J. B. Kim, "Indoor Positioning Algorithm Combining Bluetooth Low Energy Plate with Pedestrian Dead Reckoning," Journal of the Korea Institute of Information and Communication Engineering, vol. 22, no. 2, pp. 302-313, Feb. 2018. https://doi.org/10.6109/JKIICE.2018.22.2.302
  2. K. U. Ha, M. H. Cha, and D. W. Kim, "High Accuracy Indoor Location Sensing Solution based on EMA filter with Adaptive Signal Model in NLOS indoor environment," Journal of the Korea Institute of Information and Communication Engineering, vol. 23, no. 7, pp. 852-860, July. 2019. https://doi.org/10.6109/JKIICE.2019.23.7.852
  3. M. Kohne and J. Sieck, "Location-Based Services with iBeacon Technology," in 2014 2nd International Conference on Artificial Intelligence, Modelling and Simulation, pp. 315-321, 2014.
  4. C. P. Yoon and C. G. Hwang, "Efficient indoor positioning systems for indoor location-based service provider," Journal of the Korea Institute of Information and Communication Engineering, vol. 19, no. 6, pp. 1368-1373, Jun. 2015. https://doi.org/10.6109/JKIICE.2015.19.6.1368
  5. R. Kawecki, P. Korbel, and S. Hausman, "Influence of User Mobility on the Accuracy of Indoor Positioning with the use of RSSI and Particle Filter Algorithm," in 2019 Signal Processing Symposium (SPSympo), pp. 105-108, 2019.
  6. J. Ren, Y. Wang, W. Bai, C. Niu, and S. Meng, "An improved indoor positioning algorithm based on RSSI filtering," in 2017 IEEE 17th International Conference on Communication Technology (ICCT), pp. 1136-1139, 2017.
  7. Zhou C, Yuan J, Liu H, and Qiu J, "Bluetooth Indoor Positioning Based on RSSI and Kalman Filter," in 2017 Wireless Personal Communications, vol. 96, pp. 4115-4130, 2017. https://doi.org/10.1007/s11277-017-4371-4
  8. P. Kim, Kalman Filter for Beginners: With MATLAB Examples, 2011.
  9. Z. Chen, N. Ahmed, S. Julier, and C. Heckman. (2019, December). Kalman Filter Tuning with Bayesian Optimization [Internet]. Available: https://arxiv.org/pdf/1912.08601.pdf.
  10. T. O. Ting, K. L. Man, E. G. Lim, and M. Leach, "Tuning of Kalman Filter Parameters via Genetic Algorithm for State-of-Charge Estimation in Battery Management System," The Scientific World Journal, vol. 2014, pp. 11, Aug. 2014.
  11. J. Yan, D. Yuan, X. Xing, and Q. Jia, "Kalman filtering parameter optimization techniques based on genetic algorithm," in 2008 IEEE International Conference on Automation and Logistics, pp. 1717-1720, 2008.
  12. W. A. Kristiana, M. Mizanul Achlaq, B. Anindito, A. Nugroho, C. Darujati, and M. N. Al Azam, "UUID Beacon Advertisements For Lecture Schedule Information," in 2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), pp. 270-276, 2018.
  13. A. Mussina and S. Aubakirov, "RSSI Based Bluetooth Low Energy Indoor Positioning," in 2018 IEEE 12th International Conference on Application of Information and Communication Technologies (AICT), pp. 1-4, 2018.
  14. M. Phunthawornwong, E. Pengwang, and R. Silapunt, "Indoor Location Estimation of Wireless Devices Using the Log-Distance Path Loss Model," in TENCON 2018 - 2018 IEEE Region 10 Conference, pp. 0499-0502, 2018.
  15. J. Chen, D. Zhang, D. Liu, and Z. Pan, "A Network Selection Algorithm Based on Improved Genetic Algorithm," in 2018 IEEE 18th International Conference on Communication Technology (ICCT), pp. 209-214, 2018.