Location Estimation Algorithm Based on AOA Using a RSSI Difference in Indoor Environment

실내 환경에서 RSSI 차이를 이용한 AOA 기반 위치 추정 알고리즘

  • Received : 2015.11.17
  • Accepted : 2015.12.10
  • Published : 2015.12.30


There have recently been various services that use indoor location estimation technologies. Representative methods of location estimation include fingerprinting and triangulation, but they lack accuracy. Various kinds of research which apply existing location estimation methods like AOA, TOA, and TDOA are being done to solve this problem. In this paper, we study the location estimation algorithm based on AOA using a RSSI difference in indoor environments. We assume that there is a single AP with four antennas, and estimate the angle of arrival based on the RSSI value to apply the AOA algorithm. To compensate for RSSI, we use a recursive averaging filter, and use the corrected RSSI and the Pythagorean theorem to estimate the angle of arrival. The results of the experiment, show an error of 18% because of the radiation pattern of the four non-directional antennas arranged at narrow intervals.


Angle of arrival;Indoor location estimation;Recursive averaging filter;Received signal strength indicator;Location based service


  1. K. W. Cho, M. H. Jeon, and C. H. Oh, “Development of lighting control system based on location positioning for energy saving,” Journal of the Korea Institute of Information and Communication Engineering, Vol. 19, No. 12, pp. 2968-2974, Dec. 2014.
  2. H. J. Kwon, TOA estimation and AOA estimation for wireless location, M. S. theses, Sejong University, Seoul, Korea, 2007.
  3. D. Y. Lee, and Y. H. Kang, “Smart phone sensor-based indoor location tracking system for improving the location error of the radio environment,” Journal of Advanced Navigation Technology, Vol. 19, No. 1, pp. 74-79, Feb. 2015.
  4. Y. G. Kim, H. J. Shin, Y. H. Chon, and H. J. Cha, “Smartphone-based Wi-Fi tracking system exploiting the RSS peak to overcome the RSS variance problem,” Pervasive and Mobile Computing, Vol. 9, No. 3, pp. 406-420, Jun. 2013.
  5. C. Laoudias, R. Piche, and C. G. Panayiotou, “Device self-calibration in location systems using signal strength histograms,” Journal of Location Based Services, Vol. 7, No. 3, pp. 165-181, Jun. 2013.
  6. 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.
  7. J. R. Jiang, C. M. Lin, F. Y. Lin, and S. T. Huang, "ALRD: AoA localization with RSSI differences of directional antennas for wireless sensor networks," in 2012 International Conference on Information Society (i-Society), London: UK, pp. 304-309, 2012.
  8. M. Malajner, P. Planinsic, and D. Gleich, “Angle of arrival estimation using RSSI and omnidirectional rotatable antennas,” IEEE Sensors Journal, Vol. 12, No. 6, pp. 1950-1957, Jun. 2012.
  9. M. I. Jais, P. Ehkan, R. B. Ahmad, I. Ismail, T. Sabapathy, and M. Jusoh, "Review of angle of arrival (AOA) estimations through received signal strength indication (RSSI) for wireless sensors network (WSN)," in 2015 International Conference on Computer, Communications, and Control Technology (I4CT), Kuching: Malaysia, pp. 354-359, 2015.
  10. J. M. Kim, A study on the indoor location estimation algorithm using wireless networks, M. S. theses, Korea University, Seoul, Korea, 2015.


Grant : 스마트폰 LBS를 위한 고정밀 실내측위시스템의 SoC 개발

Supported by : 산업기술평가관리원