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Context Exploitation for Target Tracking

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Context-Enhanced Information Fusion

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Target tracking is the estimation of the state of one or multiple, usually moving, objects (targets) based on a time series of measurements. Widely addressed within the Bayesian statistical framework, it requires the modeling of the target state evolution and the measurement process. Information on the constraints posed by the context in which the target evolves and the measurement geometry is often available. This knowledge can be modeled, often in a statistical way, and integrated in the tracking filters to enhance their performance. This chapter presents several approaches to exploit different types of context knowledge and demonstrates context-enhanced tracking based on real and simulated data. Numerical results are given for the inclusion of sea-lanes in ship tracking and route propagation, and for road-map assisted air-to-ground radar tracking.

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Notes

  1. 1.

    It will be shown later that the Bayesian update scheme is not restricted to the propagation of conditional target state pdf’s.

References

  1. D. Hall, S. McMullen, Mathematical Techniques in Multisensor Data Fusion (Artech House, 2004)

    Google Scholar 

  2. Y. Bar-Shalom, X.-R. Li, T. Kirubarajan, Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software (John Wiley & Sons, 2001)

    Google Scholar 

  3. S. Blackman, R. Popoli, Design and Analysis of Modern Tracking Systems (Artech House, Norwood, MA, 1999)

    MATH  Google Scholar 

  4. W. Koch, Tracking and Sensor Data Fusion—Methodological Framework and Selected Applications (Springer, Berlin, 2014)

    Google Scholar 

  5. W. Koch, On Bayesian tracking and data fusion: a tutorial introduction with examples. IEEE Aerosp. Electron. Syst. Mag. 25(7), 29–52 (2010)

    Article  Google Scholar 

  6. A. Benavoli, L. Chisci, A. Farina, L. Timmoneri, G. Zappa, Knowledge-based system for multi-target tracking in a littoral environment. IEEE Trans. Aerosp. Electron. Syst. 42(3), 1100–1119 (2006)

    Article  Google Scholar 

  7. E. Blasch, J. G. Herrero, L. Snidaro, J. Llinas, G. Seetharaman, K. Palaniappan, Overview of contextual tracking approaches in information fusion, in Proceedings of SPIE Geospatial InfoFusion III, 2013

    Google Scholar 

  8. C. Yang, M. Bakich, E. Blasch. Nonlinear constrained tracking of targets on roads, in 8th International Conference on Information Fusion, 2005

    Google Scholar 

  9. W. Koch, On exploiting ‘negative’ sensor evidence for target tracking and sensor data fusion, in Proceedings of 10th International Conference on Information Fusion (Quebec, Canada, 2007)

    Google Scholar 

  10. O. Loffeld, Estimationstheorie, Bd.1, Grundlagen und stochastische Konzepte. R. Oldenbourg Verlag GmbH, Muenchen, 1990

    Google Scholar 

  11. O. Loffeld, Estimationstheorie, Bd.2, Anwendungen, Kalman-Filter. R. Oldenbourg Verlag GmbH, Muenchen, 1990

    Google Scholar 

  12. W. Koch, Advanced target tracking techniques, in Advanced Radar Signal and Data Processing, Educational Notes RTO-EN-SET-086, Paper 2, France, 2006

    Google Scholar 

  13. T. Kirubarajan, Y. Bar-Shalom, K. Pattipati, I. Kadar, Ground target tracking with variable structure IMM estimator. IEEE Trans. Aerosp. Electron. Syst. 36(1), 26–46 (2000)

    Article  Google Scholar 

  14. B. Ristic, S. Arulampalam, N. Gordon. Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House, 2004

    Google Scholar 

  15. S.J. Julier, J.K. Uhlmann, A new extension of the kalman filter to nonlinear systems, in Proceedings of 11th International Symposium Aerospace/Defense Sensing, Simulation and Controls (AeroSense), 1997

    Google Scholar 

  16. S.J. Julier, J.K. Uhlmann, Unscented filtering and nonlinear estimation. Proc. IEEE 92(3), 401–422 (2004)

    Article  Google Scholar 

  17. A. Doucet, N.D. Freitas, N. Gordon. Sequential Monte Carlo Methods in Practice (Springer, Berlin, 2001)

    Google Scholar 

  18. H. Tanizaki. Nonlinear Filters: Estimation and Applications. 2nd edn. (Springer, Berlin, 1996)

    Google Scholar 

  19. G. Battistello, M. Ulmke. Exploitation of a priori information for tracking maritime intermittent data sources, in Proceedings of 14th International Conference on Information Fusion (Chigago, 2011)

    Google Scholar 

  20. D. Simon, Kalman filtering with state constraints: a survey of linear and nonlinear algorithms. IET Control Theory Appl. 4(8), 1303–1318 (2010)

    Article  MathSciNet  Google Scholar 

  21. D. Simon, T.L. Chia, Kalman filtering with state equality constraints. IEEE Trans. Aerosp. Electron. Syst. 38(1), 128–136 (2002)

    Article  Google Scholar 

  22. N. Gupta, R. Hauser, Kalman filtering with equality and inequality state constraints. Source: http://arxiv.org/pdf/0709.2791.pdf, page URL last checked December 2014, 2007

  23. D. Simon, D.L. Simon, Constrained kalman filtering via density function truncation for turbofan engine health estimation. Int. J. Syst. Sci. 41(2), 159–171 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  24. B. Bell, J. Burke, G. Pillonetto, An inequality constrained nonlinear kalman-bucy smoother by interior point likelihood maximization. Automatica 45(1), 25–33 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  25. W. Ward, H. Durrant-Whyte, Model-based multi-sensor data fusion, in Proceedings of IEEE International Conference on Robotics Automation (Nice, France, 1992)

    Google Scholar 

  26. L. Wang, Y. Chiang, F. Chang, Filtering method for nonlinear system with constraints. IEE Proc. Control Theory Appl. 149(6), 525–531 (2002)

    Article  Google Scholar 

  27. S. Ko, R. Bitmead, State estimation for linear system with state equality constraints. Automatica 43(8), 1363–1368 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  28. C. Yang, E. Blasch, Kalman filtering with nonlinear state constraints. IEEE Trans. Aerosp. Electron. Syst. 45(1), 70–84 (2008)

    Article  Google Scholar 

  29. C. Rao, J. Rawlings, J. Lee, Constrained linear state estimation—a moving horizon approach. Automatica 37(10), 1619–1628 (2001)

    Article  MATH  Google Scholar 

  30. C. Rao, J. Rawlings, D. Mayne, Constrained state estimation for nonlinear discrete-time systems: stability and moving horizon approximations. IEEE Trans. Autom. Control 48(2), 246–258 (2003)

    Article  MathSciNet  Google Scholar 

  31. F. Papi, M. Podt, Y. Boers, G. Battistello, M. Ulmke, On constraints exploitation for particle filtering based target tracking, in Proceedings of the 15th International Conference on Information Fusion, 2012

    Google Scholar 

  32. M. Vespe, M. Sciotti, F. Burro, G. Battistello, S. Sorge. Maritime multi-sensor data association based on geographic and navigational knowledge, In Proceedings of IEEE Radar Conference (Rome, 2008)

    Google Scholar 

  33. G. Battistello, M. Ulmke, F. Papi, M. Podt, Y. Boers, Assessment of vessel route information use in Bayesian non-linear filtering, in Proceedings of 15th International Conference on Information Fusion (Singapore, 2012)

    Google Scholar 

  34. G. Pallotta, S. Horn, P. Braca, K. Bryan, Context-enhanced vessel prediction based on Ornstein-Uhlenbeck processes using historical AIS traffic patterns: real-world experimental results, in Proceedings of the 17th International Conference on Information Fusion, 2014

    Google Scholar 

  35. C. Agate, K.J. Sullivan, Road-constraint target tracking and identification using a particle filter, in Proceedings of Signal and Data Processing of Small Targets, vol. 5204, SPIE, 2003

    Google Scholar 

  36. M.S. Arulampalam, N. Gordon, M. Orton, B. Ristic, A variable structure multiple model particle filter for GMTI tracking, in Proceedings of 5th International Conference on Information Fusion (Annapolis, 2002)

    Google Scholar 

  37. W. Koch, J. Koller, M. Ulmke, Ground target tracking and road map extraction. ISPRS J. Photogrammetry Remote Sens. 61, 197–208 (2006)

    Article  Google Scholar 

  38. U. Orguner, T. Schon, F. Gustafsson, Improved target tracking with road network information, in Aerospace conference, 2009 IEEE, pp. 1–11, March 2009

    Google Scholar 

  39. D. Streller, Road map assisted ground target tracking, in Proceedings of 11th International Conference on Information Fusion (Cologne, 2008)

    Google Scholar 

  40. M. Zhang, S. Knedlik, O. Loffeld, An adaptive road-constrained IMM estimator for ground target tracking in GSM networks, in 11th International Conference on Information Fusion (Cologne, 2008)

    Google Scholar 

  41. W. Koch, Tracking and data fusion applications. Advanced Radar Signal and Data Processing, Educational Notes RTO-EN-SET-086, Paper 9 (France, 2006)

    Google Scholar 

  42. M. Ulmke, W. Koch, Road-map assisted ground moving target tracking. IEEE Trans. Aerosp. Electron. Syst. 42(4), 1264–1274 (2006)

    Article  Google Scholar 

  43. Y. Bar-Shalom, X.-R. Li, Multitarget-Multisensor Tracking: Principles and Techniques (YBS Publishing, Storrs, CT, 1995)

    Google Scholar 

  44. M. Skolnik. Radar Handbook. 3rd edn. (McGraw-Hill, 2008)

    Google Scholar 

  45. J.R. Guerci, Space-Time Adaptive Processing for Radar (Artech House, Boston, London, 2003)

    Google Scholar 

  46. R. Klemm, Principles of Space-Time Adaptive Processing, 3rd edn. IET Radar, Sonar and Navigation, Series 21, 2006

    Google Scholar 

  47. W. Koch, R. Klemm, Ground target tracking with STAP radar. IEE Proc. Radar, Sonar Navig. 148(3), 173–185 (2001)

    Article  Google Scholar 

  48. M. Mertens, T. Kirubarajan, W. Koch, Exploiting doppler blind zone information for ground moving target tracking with bistatic airborne radar. IEEE Trans. Aerosp. Electron. Syst. 50(1), 130–148 (2014)

    Article  Google Scholar 

  49. Q. Wang, S. Kulkarni, S. Verdu, Divergence estimation for multidimensional densities via k-nearest-neighbor distances. IEEE Trans. Info. Theory 55(5), 2392–2405 (2009)

    Article  MathSciNet  Google Scholar 

  50. M. Mertens, M. Feldmann, M. Ulmke, W. Koch, Tracking and data fusion for ground surveillance, chapter 6, in Integrated Tracking, Classification, and Sensor Management: Theory and Applications, ed. by M. Mallick, V. Krishnamurthy, B.-N. Vo (IEEE, Wiley, 2012)

    Google Scholar 

  51. M. Mertens, M. Ulmke, Ground moving target tracking with context information and a refined sensor model, in Proceedings of the 11th International Conference on Information Fusion (Cologne, 2008)

    Google Scholar 

  52. S. Blackman, Multiple hypothesis tracking for multiple target tracking. IEEE Aerosp. Electron. Syst. Mag. 19, 5–18 (2004)

    Article  Google Scholar 

  53. D. Cerutti-Maori, J. Klare, A.R. Brenner, J.H.G. Ender, Wide-area traffic monitoring with the SAR/GMTI system PAMIR. IEEE Trans. Geosci. Remote Sens. 46(10), 3019–3030 (2008)

    Article  Google Scholar 

  54. J.H.G. Ender, A.R. Brenner, PAMIR—a wideband phased array SAR/MTI system. IEE Proc. Radar, Sonar Navig. 150(3), 165–172 (2003)

    Article  Google Scholar 

  55. M. Mertens, R. Kohlleppel, Ground target tracking with experimental data of the pamir system, in Proceedings of the 17th International Conference on Information Fusion (Salamanca, 2014)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. Sensor Data and Information Fusion, Fraunhofer FKIE, Wachtberg, Germany

    Giulia Battistello, Michael Mertens, Martin Ulmke & Wolfgang Koch

Authors
  1. Giulia Battistello
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  2. Michael Mertens
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  3. Martin Ulmke
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  4. Wolfgang Koch
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Corresponding author

Correspondence to Giulia Battistello .

Editor information

Editors and Affiliations

  1. Department Mathematics and Computer Science, University of Udine, Udine, Italy

    Lauro Snidaro

  2. University Carlos III, Department of Computer Science and Engg. University Carlos III, Colmenarejo, Spain

    Jesús García

  3. Center Multisource Info. Fusion & Dept, Univ. at Buffalo, of Industrial and Engg Center Multisource Info. Fusion & Dept, Buffalo, New York, USA

    James Llinas

  4. Information Directorate, Air Force Research Laboratory Information Directorate, Rome, New York, USA

    Erik Blasch

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Battistello, G., Mertens, M., Ulmke, M., Koch, W. (2016). Context Exploitation for Target Tracking. In: Snidaro, L., García, J., Llinas, J., Blasch, E. (eds) Context-Enhanced Information Fusion. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-28971-7_12

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  • DOI: https://doi.org/10.1007/978-3-319-28971-7_12

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  • Online ISBN: 978-3-319-28971-7

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