Autonomous Robots

, Volume 32, Issue 1, pp 1–14 | Cite as

Target tracking without line of sight using range from radio

  • Geoffrey A. Hollinger
  • Joseph Djugash
  • Sanjiv Singh
Article

Abstract

We propose a framework for utilizing fixed ultra-wideband ranging radio nodes to track a moving target radio node in an environment without guaranteed line of sight or accurate odometry. For the case where the fixed nodes’ locations are known, we derive a Bayesian room-level tracking method that takes advantage of the structural characteristics of the environment to ensure robustness to noise. For the case of unknown fixed node locations, we present a two-step approach that first reconstructs the target node’s path using Gaussian Process Latent Variable models (GPLVMs) and then uses that path to determine the locations of the fixed nodes. We present experiments verifying our algorithm in an office environment, and we compare our results to those generated by online and batch SLAM methods, as well as odometry mapping. Our algorithm is successful at tracking a moving target node without odometry and mapping the locations of fixed nodes using radio ranging data that are both noisy and intermittent.

Keywords

Range sensing Sensor networks Target tracking 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Supplementary material

MP4 7.07 MB

References

  1. Djugash, J., Singh, S., Kantor, G., & Zhang, W. (2006). Range-only SLAM for robots operating cooperatively with sensor networks. In Proc. IEEE int. conf. robotics and automation. Google Scholar
  2. Djugash, J., Singh, S., & Grocholsky, B. (2008). Decentralized mapping of robot-aided sensor networks. In Proc. IEEE int. conf. robotics and automation. Google Scholar
  3. Ferris, B., Hahnel, D., & Fox, D. (2006). Gaussian processes for signal strength-based location estimation. In Proc. robotics: science and systems conf. Google Scholar
  4. Ferris, B., Fox, D., & Lawrence, N. D. (2007). WiFi-SLAM using Gaussian process latent variable models. In Proc. int. joint conf. artificial intelligence. Google Scholar
  5. Gezici, S., Thian, Z., Giannakis, G. B., Kobayashi, H., Molisch, A. F., Poor, H. V., & Sahinoglu, Z. (2005). Localization via ultra-wideband radios. IEEE Signal Processing Magazine, 22(4), 70–84. CrossRefGoogle Scholar
  6. Grisetti, G., Kuemmerle, R., Stachniss, C., Frese, U., & Hertzberg, C. (2010). Hierarchical optimization on manifolds for online 2D and 3D mapping. In Proc. IEEE int. conf. robotics and automation. Google Scholar
  7. Gustafsson, F., & Gunnarsson, F. (2005). Mobile positioning using wireless networks. IEEE Signal Processing Magazine, 22(4), 41–53. CrossRefGoogle Scholar
  8. Hollinger, G., Djugash, J., & Singh, S. (2008). Tracking a moving target in cluttered environments with ranging radios. In Proc. IEEE int. conf. robotics and automation. Google Scholar
  9. Hollinger, G., Singh, S., Djugash, J., & Kehagias, A. (2009). Efficient multi-robot search for a moving target. The International Journal of Robotics Research, 28(2), 201–219. CrossRefGoogle Scholar
  10. Hu, L., & Evans, D. (2004). Localization for mobile sensor networks. In Proc. int. conf. mobile computing and networking. Google Scholar
  11. Kaess, M., Ranganathan, A., & Dellaert, F. (2008). iSAM: Incremental smoothing and mapping. IEEE Transactions on Robotics, 24(6), 1365–1378. CrossRefGoogle Scholar
  12. Kehagias, A., Djugash, J., & Singh, S. (2006). Range-only SLAM with interpolated range data. Technical Report CMU-RI-TR-06-26, Robotics Institute, Carnegie Mellon Univ. Google Scholar
  13. Ko, J., & Fox, D. (2011). Learning GP-Bayes filters via Gaussian process latent variable models. Autonomous Robots, 30(1), 3–23. CrossRefGoogle Scholar
  14. Kuhn, M., Zhang, C., Mahfouz, M., & Fathy, A. E. (2009). Real-time UWB indoor positioning system with millimeter 3-D dynamic accuracy. In IEEE antennas and propagation society int. symp. Google Scholar
  15. Kumar, V., Rus, D., & Singh, S. (2004). Robot and sensor networks for first responders. IEEE Pervasive Computing, 3(4), 24–33. CrossRefGoogle Scholar
  16. Lawrence, N. D. (2005). Probabilistic non-linear principal component analysis with Gaussian process latent variable models. Journal of Machine Learning Research, 6, 1783–1816. MATHMathSciNetGoogle Scholar
  17. Liao, E., Hollinger, G., Djugash, J., & Singh, S. (2006). Preliminary results in tracking mobile targets using range sensors from multiple robots. In Proc. int. symp. distributed autonomous robotic systems. Google Scholar
  18. Mullane, J., Adams, M. D., & Wijesoma, W. S. (2009). Robotic mapping using measurement likelihood filtering. The International Journal of Robotics Research, 28(2), 172–190. CrossRefGoogle Scholar
  19. Multispectral Solutions, Inc. (2008). Company website. http://www.multispectral.com/.
  20. Nicoli, M., Morelli, C., & Rampa, V. (2008). A jump Markov particle filter for localization of moving terminals in multipath indoor scenarios. IEEE Transactions on Signal Processing, 56(8), 3801–3809. CrossRefMathSciNetGoogle Scholar
  21. Olson, E., Leonard, J. J., & Teller, S. (2006). Robust range-only beacon localization. IEEE Journal of Oceanic Engineering, 31(4), 949–958. CrossRefGoogle Scholar
  22. Priyantha, N. B., Balakrishnan, H., Demaine, E., & Teller, S. (2005). Mobile-assisted localization in wireless sensor networks. In Proc. IEEE INFOCOM. Google Scholar
  23. Schroeder, J., Galler, S., & Kyamakya, K. (2005). A low-cost experimental ultra-wideband positioning system. In IEEE int. conf. ultra-wideband. Google Scholar
  24. Schwaighofer, A., Grigoras, M., Tresp, V., & Hoffmann, C. (2003). GPPS: a Gaussian process positioning system for cellular networks. In Proc. 17th conf. on neural information processing systems. Google Scholar
  25. Snelson, E. (2007). Flexible and efficient Gaussian process models for machine learning. Ph.D. Thesis, Gatsby Computational Neuroscience Unit, University College London, University of London. Google Scholar
  26. Tenenbaum, J. B., de Silva, V., & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319–2323. CrossRefGoogle Scholar
  27. Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics. Cambridge: MIT Press. MATHGoogle Scholar
  28. Tsai, Y.-L., Tu, T.-T., Bae, H., & Chou, P. H. (2010). EcoIMU: a dual triaxial-accelerometer inertial measurement unit for wearable applications. In Int. conf. body sensor networks. Google Scholar
  29. Wang, J. M., Fleet, D. J., & Hertzmann, A. (2007). Gaussian process dynamical models for human motion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(2), 283–298. CrossRefGoogle Scholar
  30. Zhou, J., & Shi, J. (2009). RFID localization algorithms and applications—a review. Journal of Intelligent Manufacturing, 20, 695–707. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Geoffrey A. Hollinger
    • 1
  • Joseph Djugash
    • 2
  • Sanjiv Singh
    • 2
  1. 1.Computer Science Department, Viterbi School of EngineeringUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Robotics Institute, School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA

Personalised recommendations