Abstract
A novel approach is proposed for the estimation of likelihood on Interacting Multiple-Model (IMM) filter. In this approach, the actual innovation, based on a mismatched model, can be formulated as sum of the theoretical innovation based on a matched model and the distance between matched and mismatched models, whose probability distributions are known. The joint likelihood of innovation sequence can be estimated by convolution of the two known probability density functions. The likelihood of tracking models can be calculated by conditional probability formula. Compared with the conventional likelihood estimation method, the proposed method improves the estimation accuracy of likelihood and robustness of IMM, especially when maneuver occurs.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
X. R. Li and V. P. Jilkov. Survey of maneuvering target tracking. Part V: multiple-model methods. IEEE Transactions on Aerospace and Electronic Systems, 41(2005)4, 1255–1321.
X. R. Li, Z. Zhao, and X. B. Li. General model-set design methods for multiple-model approach. IEEE Transactions on Automatic Control, 50(2005)9, 1260–1276.
Fan Xiaojun and Liu Feng. A new IMM method for tracking maneuvering target. Journal of Electronics & Information Technology, 29(2007)3, 532–535 (in Chinese). 范小军, 刘锋. 一种新的机动目标跟踪的多模型算法. 电子与信息学报, 29(2007)3, 532–535.
D. Svensson and L. Svensson. A new multiple model filter with switch time conditions. IEEE Transactions on Signal Processing, 58(2010)1, 11–25.
J. Liu and R. Li. Hierarchical adaptive interacting multiple model algorithm. IET Control Theory and Application, 2(2008)6, 479–487.
T. Cheng, Z. He, and T. Tang. Adaptive update interval tracking based on adaptive interacting multiple model. IET Radar Sonar & Navigation, 2(2008)2, 104–110.
P. H. Foo and G. W. Ng. Combining the interacting multiple model method with particle filters for maneuvering target tracking. IET Radar Sonar & Navigation, 5(2011)3, 234–255.
Liu Guixi, Gao Enke, and Fan Chunyu. Tracking algorithms based on improved interacting multiple model particle filter. Journal of Electronics & Information Technology, 29(2007)12, 2810–2813 (in Chinese). 刘贵喜, 高恩克, 范春宇. 改进的交互式多模型粒子滤波跟踪算法. 电子与信息学报, 29(2007)12, 2810–2813.
Z. Yan, M. B. Yeary, S. Cheng, et al. An object tracking algorithm based on multiple model particle filtering with state partitioning. IEEE Transactions on Instrumentation and Measurement, 58(2009)5, 1797–1809.
V. P. Jilkov and X. R. Li. Online Bayesian estimation of transition probabilities for Markovian jump systems. IEEE Transactions on Signal Processing, 52 (2004)6, 1620–1630.
U. Orguner and M. Demirekler. Maximum likelihood estimation of transition probabilities of jump Markov linear systems. IEEE Transactions on Signal Processing, 56(2008)10, 5093–5108.
X. Fu, Y. Jia, J. Du, et al. New interacting multiple model algorithm for the tracking of the maneuvering target [Brief Paper]. IET Control Theory and Application, 4(2010)10, 2184–2194.
J. F. Ru, V. P. Jilkov, X. R. Li, et al. Detection of target maneuver onset. IEEE Transactions on Aerospace and Electronic Systems, 45(2009)2, 536–554.
Y. T. Chan, A. G. C. Hu, and J. B. Plant. A Kalman filter based tracking scheme with input estimation. IEEE Transactions on Aerospace and Electronic Systems, AES-15(1979)2, 237–244.
Author information
Authors and Affiliations
Corresponding author
Additional information
Supported by the National Natural Science Foundation of China (No. 60736045) and the Fundamental Research Funds for the Central Universities (No. 103.1.2. E022050205).
Communication author: Sun Jie, born in 1986, male, Master.
About this article
Cite this article
Sun, J., Jiang, C., Chen, Z. et al. Interacting multiple model algorithm based on joint likelihood estimation. J. Electron.(China) 28, 427–432 (2011). https://doi.org/10.1007/s11767-012-0734-x
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11767-012-0734-x