Uncertain IMM Estimator for Multi-Sensor Target Tracking
- 1
- 2
- 3
Abstract
Interacting Multiple Model (IMM) estimator can provide better performance of target tracking than mono model Kalman filter. In multi-sensor system ordinarily, availability of measurement from different sensors is stochastic, and it is difficult to construct uniform global observation vector and observation matrix appropri-ately in existing method. An IMM estimator for uncertain measurement is presented. By the method invalid measurement is regarded as outlier, and approximation is reconstructed by feedback of system state estima-tion of fusion center. Then nominally generalized certain measurement can be obtained by substituting re-constructed one for invalid one. The generalized certain measurement can be centralized to construct global measurement and provided to IMM estimator, and existing multi-sensor IMM estimation method is general-ized to uncertain environment. Theoretical analysis and simulation results show the effectiveness of the method.
- S. Tokoro, K. Moriizumi, T. Kawasaki, T. Nagao, K. Abe, and K. Fujita, “Sensor fusion system for pre-crash safety system,” Proceedings of 2004 IEEE Intelligent Vehicles Symposium, pp. 945–950, 2004.
- A. Polychronopoulos, M. Tsogas, A. J. Amditis, and L. Andreone, “Sensor fusion for predicting vehicles’ path for collision avoidance systems,” IEEE Transactions on Intelligent Transportation Systems, Vol. 8, No. 3, pp. 549–562, 2007.
- G. Alessandretti, A. Broggi, and P. Cerri, “Vehicle and guard rail detection using radar and vision data fusion,” IEEE Transactions on Intelligent Transportation Systems, Vol. 8, No. 1, pp. 95–105, 2007.
- X. R. Li and V. P. Jilkov, “Survey of maneuvering target tracking, Part I: Dynamic models,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 39, No. 4, pp. 1333–1364, 2003.
- H. Zhou, Z. Jing, and P. Wang, “Maneuver target tacking,” National Defence Industry Press, Beijing, 1991.
- X. R. Li and V. P. Jilkov, “Survey of maneuvering target tracking, Part V: Multiple-model methods,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 41, No. 4, pp. 1255–1321, 2005.
- E. Mazor, A. Averbuch, Y. Bar-Shalom, and J. Dayan, “Interacting multiple model methods in target tracking: A survey,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 34, No. 1, pp. 103–123, 1998.
- M. Mallick and B. F. La Scala, “IMM estimator for ground target tracking with variable measurement sampling intervals,” Proceedings of 9th International Conference Information Fusion, pp. 1–8, 2006.
- H. Chen and P. Meer, “Robust fusion of uncertain information,” IEEE Transactions on Systems, Man and Cybernetics, Part B, Vol. 35, No. 3, pp. 578–586, 2005.
- X. Liu and A. Goldsmith, “Kalman filtering with partial observation losses,” Proceedings of 43rd IEEE Conference Decision and Control, Vol. 4, pp. 4180–4186, 2004.
- Z. Wang, D. W. C. Ho, and X. Liu, “Variance-constrained filtering for uncertain stochastic systems with missing measurements,” IEEE Transactions on Automatic Control, Vol. 48, No. 7, pp. 1254–1258, 2003.
- M. Cen and C. Fu, “Combined measurement fusion of unequal dimensional measurement vectors,” Journal of Sichuan University (Engineering Science Edition), Vol. 38, No. 4, pp. 45–149 (Ch), 2006.
- G. Qiang and C. J. Harris, “Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 37, No. 1, pp. 273–280, 2001.