Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
권호 표지

A Study on Enhancing Outdoor Pedestrian Positioning Accuracy Using Smartphone and Double-Stacked Particle Filter

스마트폰과 Double-Stacked 파티클 필터를 이용한 실외 보행자 위치 추정 정확도 개선에 관한 연구

  • 성광제 (상명대학교 소프트웨어학과)
  • Received : 2023.06.06
  • Accepted : 2023.06.21
  • Published : 2023.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

In urban environments, signals of Global Positioning System (GPS) can be blocked and reflected by tall buildings, large vehicles, and complex components of road network. Therefore, the performance of the positioning system using the GPS module in urban areas can be degraded due to the loss of GPS signals necessary for the position estimation. To deal with this issue, various localization schemes using inertial measurement unit (IMU) sensors, such as gyroscope and accelerometer, and Bayesian filters, such as Kalman filter (KF) and particle filter (PF), have been designed to enhance the performance of the GPS-based positioning system. Among Bayesian filters, the PF has been widely used for the target tracking and vehicle navigation, since it can provide superior performance in estimating the state of a dynamic system under nonlinear/non-Gaussian circumstance. This paper presents a positioning system that uses the double-stacked particle filter (DSPF) as well as the accelerometer, gyroscope, and GPS receiver on the smartphone to provide higher pedestrian positioning accuracy in urban environments. The DSPF employs a nonparametric technique (Parzen-window) to create the multimodal target distribution that approximates the posterior distribution. Experimental results show that the DSPF-based positioning system can provide the significant improvement of the pedestrian position estimation in urban environments.

Keywords

References

  1. B. Ben-Moshe, E. Elkin, and H. Levi, "Weissman, A. Improving Accuracy of GNSS Devices in Urban Canyons," Canadian Conference on Computational Geometry, pp. 511-515, 2011. 
  2. A. Fernandez, M. Wis, P. F. Silva, I. Colomina, E. Pares, F. Dovis, and J. Lindenberger, "GNSS/INS/LiDAR Integration in Urban Environment: Algorithm Description and Results from ATENEA Test Campaign," 6th ESA Workshop on Satellite Navigation Technologies, Navitec, and European Workshop on GNSS Signals and Signal Processing, IEEE, pp. 1-8, 2012. 
  3. Ye, F., Pan, S., Gao, W., Wang, H., Liu, G., Ma, C., and Wang, Y., "An improved single-epoch GNSS/INS positioning method for urban canyon environment based on real-time DISB estimation," IEEE Access, Vol. 8, pp. 227566-227578, 2020.  https://doi.org/10.1109/ACCESS.2020.3044197
  4. Chu H.J., Tsai G.J., Chiang K.W., and Duong T.T., "GPS/MEMS INS data fusion and map matching in urban areas," Sensors, Vol. 13, No. 9, pp. 11280-11288, 2013.  https://doi.org/10.3390/s130911280
  5. Cossaboom M., Georgy J., Karamat T., and Noureldin A., "Augmented Kalman filter and map matching for 3D RISS/GPS integration for land vehicles," International Journal of Navigation and Observation, 2012. 
  6. Georgy, J., Iqbal, U., and Noureldin, A., "Quantitative comparison between Kalman filter and Particle filter for low cost INS/GPS integration," 2009 6th International Symposium on Mechatronics and its Applications, Sharjah, United Arab Emirates, pp. 1-7, 2009. 
  7. Wang, X. and Ni, W., "An improved particle filter and its application to an INS/GPS integrated navigation system in a serious noisy scenario," Measurement Science and Technology, Vol. 27, No. 9, 095005, 2016. 
  8. Hosseinyalamdary S., "Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study," Sensors. Vol. 18, No. 5, pp. 1316-1330, 2018.  https://doi.org/10.3390/s18051316
  9. Shen, Z., Georgy, J., Korenberg, M. J., and Noureldin, A., "Low cost two dimension navigation using an augmented Kalman filter/Fast Orthogonal Search module for the integration of reduced inertial sensor system and Global Positioning System," Transportation Research Part C: Emerging Technologies, Vol. 19, No. 6, pp. 1111-1132, 2011  https://doi.org/10.1016/j.trc.2011.01.001
  10. Sung, K., Lee, H.K., and Kim, H., "Pedestrian Positioning Using a Double-Stacked Particle Filter in Indoor Wireless Networks," Sensors, Vol. 19, No. 18, pp. 3907-3927, 2019.  https://doi.org/10.3390/s19183907
  11. Duda, R.O., Hart, P.E., and Stork, D.G., "Pattern Classification," John Wiley & Sons, New York, NY, USA, 2012. 
  12. Bowman, A. and Azzalini, A., "Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations: The Kernel Approach with S-Plus Illustrations," OUP Oxford, UK, 1997. 
  13. Kitagawa, G., "Monte Carlo filter and smoother for non-Gaussian nonlinear state space models," Journal of computational and graphical statistics, Vol. 5, No. 1, pp. 1-25, 1996.  https://doi.org/10.1080/10618600.1996.10474692
  14. Lange, K. L., Little, R. J., and Taylor, J. M., "Robust statistical modeling using the t distribution," Journal of the American Statistical Association, Vol. 84, No. 408, pp. 881-896, 1989.  https://doi.org/10.1080/01621459.1989.10478852
  15. Arulampalam, M. S., Maskell, S., Gordon, N., and Clapp, T., "A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking," IEEE Trans. Signal Processing, Vol. 50, No. 2, pp. 174-188, 2002.  https://doi.org/10.1109/78.978374
  16. Kim, M. K., Kim, J. S., Yang, O., "Design of the Position Control System for Parabolic Antenna using Gyro Sensor," Journal of the Semiconductor & Display Technology, Vol. 12, No. 2, pp.85-91, 2013. 
  17. Park, C.-S., Choeh, J.-Y., "Visual Location Recognition Using Time-Series Streetview Database," Journal of the Semiconductor & Display Technology, Vol. 18, No. 4, pp. 57-61, 2019. 
  18. Ha, E.-H. and Choi, G.-H., "Position Control of an Object Using Vision Sensor," Journal of the Semiconductor & Display Technology, Vol. 10, No. 2, pp. 49-56, 2011.