DOI QR코드

DOI QR Code

Relative azimuth estimation algorithm using rotational displacement

  • Kim, Jung-Ha (Department of Electrical & Electronics Engineering, Korea Maritime and Ocean University) ;
  • Kim, Hyun-Jun (Department of Electrical & Electronics Engineering, Korea Maritime and Ocean University) ;
  • Kim, Jong-Su (Division of Marine System Engineering, Korea Maritime and Ocean University) ;
  • Lee, Sung-Geun (Division of Electrical & Electronics Engineering, Korea Maritime and Ocean University) ;
  • Seo, Dong-Hoan (Division of Electrical and Electronics Engineering, Korea Maritime and Ocean University)
  • 투고 : 2014.01.02
  • 심사 : 2014.02.10
  • 발행 : 2014.02.28

초록

Recently, indoor localization systems based on wireless sensor networks have received a great deal of attention because they help achieve high accuracy in position determination by using various algorithms. In order to minimize the error in the estimated azimuth that can occur owing to sensor drift and recursive calculation in these algorithms, we propose a novel relative azimuth estimation algorithm. The advantages of the proposed technique in an indoor environment are that an improved weight average filter is used to effectively reduce impulse noise from the raw data acquired from nodes with inherent errors and a rotational displacement algorithm is applied to obtain a precise relative azimuth without using additional sensors, which can be affected by electromagnetic noise. Results from simulations show that the proposed filter reduces the impulse noise, and the acquired estimation error does not accumulate with time by using proposed algorithm.

키워드

참고문헌

  1. I. D'Souza, W. Ma, and C. Notobartolo, "Real-time location systems for hospital emergency response," IEEE IT Professional, vol. 13, no. 2, pp. 37-43, 2011.
  2. H.-T. Cho, T.-W. Kim, Y.-J. Park, and Y.-J. Baek, "Enhanced trajectory estimation method for RTLS in port logistics environment," High Performance Computing and Communication & 2012 IEEE 9th International Conference Embedded Software Systems (HPCC-ICESS), pp. 1555-1562, 2012.
  3. H.-J. Cho, K.-I. Hwang, D.-S. Noh, and D.-H. Seo, "Real time indoor positioning system using IEEE 802.15.4a and sensors", Journal of the Korean Society of Marine Engineering, vol. 36, no. 6, pp. 850-856, 2012 (in Korean). https://doi.org/10.5916/jkosme.2012.36.6.850
  4. J.-H. Seong, T.-W. Lim, J.-S. Kim, S.-G. Park, and D.-H. Seo, "An improvement algorithm for localization using adjacent node and distance variation analysis techniques in a ship", Journal of the Korean Society of Marine Engineering, vol. 37, no. 2, pp. 213-219, 2013 (in Korean). https://doi.org/10.5916/jkosme.2013.37.2.213
  5. H.-J. Cho, J.-S. Kim, S.-G. Lee, J.-W. Kim, and D.-H. Seo, "Fixed node reduction technique using relative coordinate estimation algorithm," Journal of The Korean Society of Marine Engineering, vol. 37, no. 2, pp. 220-226, 2013 (in Korean). https://doi.org/10.5916/jkosme.2013.37.2.220
  6. J.-H. Seong, J.-S. Park, S.-H. Lee, and D.-H. Seo, "Indoor localization algorithm based on WLAN using modified database and selective," Journal of The Korean Society of Marine Engineering, vol. 37, no. 8, pp. 932-938, 2013 (in Korean). https://doi.org/10.5916/jkosme.2013.37.8.932
  7. C. Fischer and H. Gellersen, "Location and navigation support for emergency responders: A survey," IEEE Pervasive Computing, vol. 9, no. 1, pp. 38-47, 2010. https://doi.org/10.1109/MPRV.2009.91
  8. Y. Chon and H. Cha, "LifeMap: a smartphone-based context provider for location-based services," IEEE Pervasive Computing, vol. 10, no. 2, pp. 58-67, 2011.
  9. J. Fujimoto, S. Hotta, K. Sawada, Y. Hada, K. Hida, and S. Mori, "Hybrid positioning system combining spatially continuous and discrete information for indoor location-based service," Ubiquitous Positioning, Indoor Navigation, and Location Based Service (UPINLBS), pp. 1-6, 2012.
  10. B.-G. Lee and W.-Y. Chung, "Multitarget three-dimensional indoor navigation on a PDA in a wireless sensor network," IEEE Sensors Journal, vol. 11, no. 3, pp. 799-807, 2011. https://doi.org/10.1109/JSEN.2010.2076802
  11. A. R. J. Ruiz, F. S. Granja, J. C. Prieto Honorate, and J. I. G. Rosas, "Accurate pedestrian indoor navigation by tightly coupling foot-mounted IMU and RFID measurements," IEEE Transactions on Instrumentation Measurement, vol. 61, no. 1, pp. 178-189. 2012. https://doi.org/10.1109/TIM.2011.2159317
  12. R. Zhang and L. M. Reindl, "Pedestrian motion based inertial sensor fusion by a modified complementary separate bias Kalman filter," IEEE Sensors Applications Symposium (SAS), pp. 209-213, 2011.
  13. R. Zhang, F. Hoflinger, and L. Reindl, "Inertial sensor based indoor localization and monitoring system for emergency responders," IEEE Sensors Journal, vol. 13, no. 2, pp. 838-848, 2013. https://doi.org/10.1109/JSEN.2012.2227593
  14. S. E. Umbaugh, Computer Vision and Image Processing, Prentice-Hall, Englewood Cliffs, NJ, USA, 1998.
  15. H. S. Yazdi and F. Homayouni, "Impulsive noise suppression of images using adaptive median filter," International Journal of Signal processing Image Processing and Pattern Recognition, vol. 3, no. 3, pp. 1-12, 2010.
  16. T.-C. Lin, "A new adaptive center weighted median filter for suppressing impulsive noise in images," Information Sciences, vol. 177, no. 4, pp. 1073-1087, 2007. https://doi.org/10.1016/j.ins.2006.07.030

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