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A self-adaptive generic IMM data fusion algorithm

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Category: Information technologies, systems analysis and administration

Last Updated on 02 April 2016

Published on 02 April 2016

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Authors:

YingminYi, Xi’an University of Technology, Xi'an, Shaanxi, China

Weiduo Chen, Xi’an University of Technology, Xi'an, Shaanxi, China

Abstract:

Purpose. For the problem of hybrid estimation, this paper proposes the self-adaptive generic interacting multiple-model (IMM) data fusion algorithm for solving the model selection problem of IMM. To find the optimal solution of the hybrid estimation problem, the history information of all the models is considered.

Methodology. According to the priori knowledge, the parameter describing the model is mapped to the model set. According to the similarity of the parameter variations, the parameter space is divided into several sub-spaces. And each sub-space is mapped to a sub-model set. The model transition of each sub-model obeys the Markov Chain.

Findings. The center model of every sub-space is calculated out self-adaptively. The center models are organized as the model set of the IMM algorithm.

Originality. The final output of the algorithm is the data fusion of the model set estimations using IMM algorith. At last, the simulation experiments show that the proposed algorithm is superior to the traditional IMM algorithms under the condition of equivalent computation quantity.

Practical value. The experimental results show that, the performance of the algorithm proposed in this paper is improved notably under the condition of equivalent computation.

References:

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2. Jian Lan, Li, X. R., Vesselin P. Jilkov, Chundi Mu.(2013), "Second-Order Markov Chain Based Multiple-Model Algorithm for Maneuvering Target Tracking", IEEE Transactions on Aerospace and Electronic Systems ,vol. 49, no.1, pp.3−19.

3. Kichun Jo, Keounyup Chu, Myoungho Sunwoo (2012), "Interacting Multiple Model Filter-Based Sensor Fusion of GPS With In-Vehicle Sensors for Real-Time Vehicle Positioning", IEEE Transactions on Intelligent Transportation Systems ,vol. 13, no.1, pp.329−343.

4. Weiyi Liu, Jian Wei, Mengchen Liang, Yi Cao, Inseok Hwang (2013), "Multi-Sensor Fusion and Fault Detection using Hybrid Estimation for Air Traffic Surveillance" , IEEE Aerospace and Electronic Systems, vol. 49, no.4, pp.2323− 2339.

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6. Jian Lan, Li, X.R., Chundi Mu (2011), "Best Model Augmentation for Variable-Structure Multiple-Model Estimation", IEEE Aerospace and Electronic Systems ,vol. 47, no.3, pp.2008−2025.

7. Li-Qiang Hou, Heng-Nian Li, Fu-Ming Huang, Pu Huang (2011), "Tracking micro reentering USV with TDRS and ground stations using adaptive IMM method", IEEE Information and Automation, pp.1−7, 6−8.

8. Jian Lan, Li, X.R. (2013), "Equivalent-Model Augmen-tation for Variable-Structure Multiple-Model Estimation", IEEE Aerospace and Electronic Systems ,vol. 49, no.4, pp. 2615−2630.

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