An expectation-maximization-based interacting multiple model approach for cooperative driving systems

IEEE Transactions on Intelligent Transportation Systems

Volumn 6, Issue 2, 2005, Pages 206-228

  Huang, Dongliang ^a   Leung, Henry ^a  

^a UNIVERSITY OF CALGARY   (Canada)

Author keywords

Collaborative driving; EKF; EM algorithm; Registration; Sensor fusion

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; ERROR ANALYSIS; KALMAN FILTERING; MATHEMATICAL MODELS; PARAMETER ESTIMATION; SENSOR DATA FUSION; SENSORS;

COLLABORATIVE DRIVING; EM ALGORITHM; EXTENDED KALMAN FILTERING (EKF); REGISTRATION;

INTELLIGENT VEHICLE HIGHWAY SYSTEMS;

EID: 21644453308     PISSN: 15249050     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITS.2005.848366     Document Type: Article

References (36)

1. L. A. Klein, Sensor Technologies and Data Requirements for ITS. Boston: Artech House, 2001.
2. S. Kato, S. Tsugawa, K. Tokuda, T. Matsui and H. Fujii, "Vehicle control algorithms for cooperative driving with automated vehicles and intervehicle communications," IEEE Trans. Intell. Transp. Syst., vol. 3, pp. 155-161, Sep. 2002.
3. J. A. Farrell and M. Barth, The Global Positioning System and Inertial Navigation. New York: McGraw-Hill, 1998.
4. M. P. Dana, "Registration: A prerequisite for multiple sensor tracking," in Multiarget-Multisensor Tracking: Advanced Applications, Y. Bar-Shalom, Ed. Norwood, MA: Artech House, 1990.
5. Y. Zhou, H. Leung and E. Bosse, "Registration of mobile sensors using the parallelized extended Kalman filter," Opt. Eng., vol. 36, no. 3, pp. 780-788, 1997.
6. Y. Zhou, H, Leung and P. C. Yip, "An exact maximum likelihood registration algorithm for data fusion," IEEE Trans. Signal Process., vol. 45, pp. 1560-1572, Jun. 1997.
7. N. Okello and B. Ristic, "Maximum likelihood registration for multiple dissimilar sensors," IEEE Trans. Aerosp. Electron. Syst., vol. 39, pp. 1074-1083, Jul. 2003.
8. W. Li and H. Leung, "Simultaneous registration and fusion of multiple dissimilar sensors for cooperative driving," IEEE Trans. Intell. Transp. Syst., vol. 5, pp. 84-98, Jun. 2004.
9. E. A. Wan and R. van der Merwe, "The unscented Kalman filter," in Kalman Filter and Neural Networks, S. Haykin, Ed. New York: Wiley, 2001, ch. 7.
10. S. Brunke and M. Campbell, "Estimation architecture for future automnomous vehicles," in Proc. American Control Conf., Anchorage, AK, 2002. vol. 2, pp. 1108-1114.
11. A. Farina, B. Ristic and D. Benvenuti, "Tracking a ballistic target: Comparison of several nonlinear filters," IEEE Trans. Aerosp. Electron. Syst., vol. 38, pp. 854-867, Jul. 2002.
12. J. Choi, A. C. de C. Lima and S. Haykin, "Unscented Kalman filter-trained recurrent neural equalizer for time-varying channels," in IEEE Conf. Commun., Anchorage, AK, May 2003, pp. 3241-3245.
13. E. Mazor, A. Averbuch, Y. Bar-Shalom and J. Dayan, "Interacting multiple model methods in target tracking: A survey," IEEE Trans. Aerosp. Electron. Syst., vol. 34, pp. 103-123, Jan. 1998.
14. S. Blackman and R. Popoli, Design and Analysis of Modern Tracking Systems. Norwood, MA: Artech House, 1999.
15. Y. Bar-Shalom, X. R. Li and T. Kirubarajan, Estimation With Applications to Tracking and Navigation. New York: Wiley, 2001.
16. N. Kaempchen, K. Weiss, M. Schaefer and K. C. J. Dietmayer, "IMM object tracking for high dynamic driving maneuvers," in IEEE Intelligent Vehicles Symp., Parma, Italy, Jun. 2004, pp. 825-830.
17. K. Weiss, D. Stueker and A. Kirchner, "Target modeling and dynamic classification for adaptive sensor data fusion," in IEEE Intelligent Vehicles Symp., Columbus, OH, Jun. 2003, pp. 132-137.
18. R. Mobus, A. Joos and U. Kolbe, "Multi-target multi-object radar tracking," in IEEE Intelligent Vehicles Symp., Jun. 2003, pp. 489-494.
19. A. Amditis, A. Polychronopoulos, I. Karaseitanidis, G. Katsoulis and E. Bekiaris, "Multiple sensor collision avoidance system for automotive applications using an IMM approach for obstacle tracking," in Proc. 5th Int. Conf. Information Fusion, Montreal, Canada, 2002, vol. 2, pp. 812-817.
20. P. Tichavsky, C. H. Muravchik and A. Nehorai, "Posterior Cramér-Rao bounds for discrete-time nonlinear filtering," IEEE Trans. Signal Process., vol. 46, pp. 1386-1396, May 1998.
21. S. M. Herman, "A particle filtering approach to joint passive radar tracking and target classification," Ph.D. dissertation, Univ. Illinois at Urbana-Champaign, Urbana, 2002.
22. G. Dissanayake, S. Sukkarieh, E. Nebot and H. Durrant-Whyte, "The aiding of a low-cost strapdown inertial measurement unit using vehicle model constraints for land vehicle applications," IEEE Trans. Robot. Autom., vol. 17, pp. 731-747, Oct. 2001.
23. Q. Ji and Y. Zhang, "Camera calibration with genetic algorithm," IEEE Trans. Syst., Man, Cybern., vol. 31, pp. 120-130, May 2001.
24. R. Y. Tsai, "A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses," IEEE Trans. Robot. Autom., vol. 3, pp. 323-344, Aug. 1987.
25. D. Simon and T. L. Chia, "Kalman filtering with state equality constraints," IEEE Trans. Aerosp. Electron. Syst., vol. 38, pp. 128-136, Jan. 2002.
26. A. P. Dempster, N. M. Laird and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. R. Stat. Soc., Ser. B, vol. 39, no. 1, pp. 1-38, 1977.
27. R. H. Shumway and D. S. Stoffer, "An approach to time series smoothing and forecasting using the EM algorithm," J. Time Ser. Anal., vol. 3, no. 4, pp. 253-264, 1982.
28. Y. Bar-Shalom and X. R. Li, Multitarget-Multisensor Tracking: Principles and Techniques. Storrs, CT: YBS, 1995.
29. B. D. O. Anderson and J. B. Moore, Optimal Filtering. Englewood Cliffs, NJ: Prentice-Hall, 1979.
30. R. H. Shumway and D. S. Stoffer, "Dynamic linear models with switching," J. Am. Stat. Assoc., vol. 86, no. 415, pp. 763-769, 1991.
31. E. Weinstein, A. V. Oppenheim, M. Feder and J. R. Buck, "Iterative and sequential algorithms for multisensor signal enhancement," IEEE Trans. Signal Process., vol. 42, pp. 846-859, Apr. 1994.
32. R. J. Elliott and V. Krishnamurthy, "New finite-dimensional filters for parameter estimation of discrete-time linear Gaussian models," IEEE Trans. Signal Process., vol. 44, pp. 938-951, May 1999.
33. R. E. Helmick, W. D. Blair and S. A. Hoffman, "Fixed-interval smoothing for Markovian switching systems," IEEE Trans. Inf. Theory, vol. 41, pp. 1845-1855, Nov. 1995.
34. D. Huang, N. Fujiyama and S. Sugimoto, "Blind image identification and restoration for noisy blurred images based on discrete sine transform, " IEICE Trans. Inf. Syst., vol. E86-D, pp. 727-735, 2003.
35. A. Bessell, B. Ristic, A. Farina, X. Wang and M. S. Arulampalam, "Error performance bounds for tracking a manoeuvering target," in Proc. 6th Conf. Information Fusion, Cairns, Qld., Australia, 2003, vol. 2, pp. 903-910.
36. d < 1 and its application to target tracking," IEEE Trans. Signal Process., vol. 50, pp. 1916-1924, Aug. 2002.