Skip to main content
SpringerLink
Log in

A Noise-Robust Convex-Optimized Positioning System Based on Code-Aided RSS Estimation and Virtual Base Station Transform

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

This paper presents a noise-robust mobile positioning system based on received signal strength (RSS) estimators, particle filters and a convex optimization processor. The positioning system uses a code-aided noise cancellation technique to refine the (RSS) signals and improve the positioning accuracy. This study also presents a virtual base-station transform (VBST) and convex optimization algorithm to deal with the none-light-of-sight (NLOS) travelling problem of the microwave transmission. The proposed positioning processor consists of four code-aided SNR/RSS estimators and four latency-reduced particle filters to refine the RSS signals from four base stations and estimate their distances to the mobile station. Then, the estimated distances are delivered to a convex optimization processor with the VBST algorithm to locate the mobile station. This work implemented the positioning algorithm on Xilinx Virtex-4 FPGA and RF modules to verify the positioning performance. The measurement and analysis results show that the proposed convex-optimized positioning system reduces 20 % RMSE in the mixed NLOS/LOS environment compared to the sole particle filtering approach. The RSS estimator can further improve the positioning performance in the noisy environments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21

Similar content being viewed by others

References

  1. Paton, I., Crompton, E., Gardiner, J., & Noras, J. (1991). Terminal self-location in mobile radio systems, in Mobile Radio and Personal Communications. In Sixth International Conference on, dec 1991, pp. 203–207.

  2. Hashemi, H. (1991). Pulse ranging radiolocation technique and its application to channel assignment in digital cellular radio. In Vehicular Technology Conference, Gateway to the Future Technology in Motion., 41st IEEE, may (pp. 675–680).

  3. Giordano, A., Chan, M., & Habal, H. (1995). A novel location-based service and architecture, in Personal, Indoor and Mobile Radio Communications. In PIMRC’95. ’Wireless: Merging onto the Information Superhighway’. Sixth IEEE International Symposium on, vol. 2, pp. 853–857.

  4. Koshima, H., & Hoshen, J. (2000). Personal locator services emerge. Spectrum, IEEE, 37(2), 41–48.

    Article  Google Scholar 

  5. Kerr, T. (1997). A critical perspective on some aspects of gps development and use. In Digital Avionics Systems Conference, 1997. 16th DASC., AIAA/IEEE, (Vol. 2 pp. 9.4–9–9.4–20).

  6. Arulampalam, M., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. In IEEE Transactions on Signal Processing, (Vol. 50 pp. 174 –188).

  7. Ristic, B., Arulampalm, S., & Gordon, N. (2004). Beyond the Kalman filter : particle filters for tracking applications,Artech House.

  8. Al-Jazzar, S., Caffery, J., & You, H.-R. (2007). Scattering-model-based methods for toa location in nlos environments. IEEE Transactions on Vehicular Technology, 56(2), 583–593.

    Article  Google Scholar 

  9. Chiu, W.-Y., & Chen, B.-S. (2009). Mobile location estimation in urban areas using mixed manhattan/euclidean norm and convex optimization. IEEE Transactions on Wireless Communications, 8(1), 414–423.

    Article  Google Scholar 

  10. Huang, L.-H., Shr, K.-T., & Huang, Y.-H. (2012). Mobile positioning system based on virtual base station transform and convex optimization. In IEEE Workshop on Signal Processing Systems (SiPS 2012), vol. 4.

  11. Pauluzzi, D.R., & Beaulieu, N.C. (2000). A comparison of SNR estimation techniques for the AWGN channel. IEEE Transactions on Communications, 48(10), 1681–1691.

    Article  Google Scholar 

  12. Wiesel, A., Goldberg, J., & Messer, H. (2002). Non-data-aided signal-to-noise-ratio estimation,. In Proceedings IEEE ICC, vol. 1 New York (pp. 197–201).

  13. Boujelben, M. A., Bellili, F., Affes, S., & Stephenne, A. (2009). EM algorithm for non-data-aided SNR estimation of linearly-modulated signals over SIMO channels. In Proceeding IEEE GLOBECOM (pp. 1–6).

  14. Sankaranarayanan, A., Chellappa, R., & Srivastava, A. (2005). Algorithmic and architectural design methodology for particle filters in hardware, in Computer Design: VLSI in Computers and Processors, 2005. ICCD 2005. In Proceedings IEEE International Conference (pp. 275–280).

  15. Marsaglia, G., & Tsang, W.W. (2000). The ziggurat method for generating random variables. Journal of Statistical Software, 5(8), 1–7, 10. http://www.jstatsoft.org/v05/i08.

    Article  Google Scholar 

  16. Ye, B., & Zhang, Y. (2009). Improved fpga implementation of particle filter for radar tracking applications. In Synthetic Aperture Radar, APSAR. 2nd Asian-Pacific Conference, pp. 943–946.

  17. Cho, J.U., Jin, S.H., Pham, X.D., & Jeon, J.W. (2006). Object tracking circuit using particle filter with multiple features. In SICE-ICASE. International Joint Conference (pp. 1431–1436).

Download references

Acknowledgements

This work was supported by the National Science Council (NSC), Taiwan, under Grant Number NSC 101-2221-E-007-127-MY2.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, National Tsing Hua University, Taiwan, 30013, Republic of China

    Li-Hong Huang, Kai-Ting Shr, Ming-Hung Lin & Yuan-Hao Huang

Authors
  1. Li-Hong Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kai-Ting Shr
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ming-Hung Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuan-Hao Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuan-Hao Huang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, LH., Shr, KT., Lin, MH. et al. A Noise-Robust Convex-Optimized Positioning System Based on Code-Aided RSS Estimation and Virtual Base Station Transform. J Sign Process Syst 84, 309–323 (2016). https://doi.org/10.1007/s11265-015-1082-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-015-1082-5

Keywords

Search

Navigation