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Fata fusion algorithm for multi-sensor dynamic system based on interacting multiple model

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Abstract

This paper presents a data fusion algorithm for dynamic system with multi-sensor and uncertain system models. The algorithm is mainly based on Kalman filter and interacting multiple model (IMM). It processes crosscorrelated sensor noises by using augmented fusion before model interacting. And eigenvalue decomposition is utilized to reduce calculation complexity and implement parallel computing. In simulation part, the feasibility of the algorithm was tested and verified, and the relationship between sensor number and the estimation precision was studied. Results show that simply increasing the number of sensor cannot always improve the performance of the estimation. Type and number of sensors should be optimized in practical applications.

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References

  1. Liang Yan, Cheng Yong-mei, Jia Yu-gang, et al. Analysis on the performance and properties of interacting multiple models algorithm [J]. Control Theory and Applications, 2001, 18(4): 488–492 (in Chinese).

    Google Scholar 

  2. Blom H A P, Bar-Shalom Y. The interacting multiple model algorithm for systems with Markovian switching coefficients [J]. IEEE Transactions on Automatic Control, 1988, 33(8): 780–783.

    Article  MATH  Google Scholar 

  3. Mazor E, Averbuch A, Bar-Shalom Y, et al. Interacting multiple model methods in target tracking: A survey [J]. IEEE Transactions on Aerospace and Electronic Systems, 1998, 34(1): 103–123.

    Article  Google Scholar 

  4. Xiong Z K, Jiang H Y, Li Q, et al. The research of maneuvering target tracking based on interacting multiple model [C]//International Conference on Automatic Control and Artificial Intelligence (ACAI 2012). Xiamen, China: IEEE, 2012: 2171–2174.

    Chapter  Google Scholar 

  5. Sun S L, Deng Z L. Multi-sensor optimal information fusion Kalman filter [J]. Automatica, 2004, 40(6): 1017–1023.

    Article  MATH  MathSciNet  Google Scholar 

  6. Guo Guan-bin, Fang Qing. Current status and development of radar netting technique [J]. Radar Science and Technology, 2005, 3(4): 193–202 (in Chinese).

    MathSciNet  Google Scholar 

  7. Mobus R, Kolbe U. Multi-target multi-object tracking, sensor fusion of radar and infrared [C]//2004 IEEE Intelligent Vehicles Symposium. Parma, Italy: IEEE, 2004: 732–737.

    Chapter  Google Scholar 

  8. Gao S S, Zhong Y M, Zhang X Y, et al. Multisensor optimal data fusion for INS/GPS/SAR integrated navigation system [J]. Aerospace Science and Technology, 2009, 13(4–5): 232–237.

    Article  Google Scholar 

  9. Bar-Shalom Y, Chen H M. IMM estimator with outof- sequence measurements [J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(1): 90–98.

    Article  Google Scholar 

  10. Liu J, Cai B G, Wang Y P, et al. A high integrity IMM-CKF method for multi-sensor based vehicle autonomous navigation [C]//Microwave, Antenna, Propagation, and EMC Technologies for Wireless Communications (MAPE). Beijing, China: IEEE, 2011: 691–694.

    Google Scholar 

  11. Cho T H, Lee C H, Choi S B. Multi-sensor fusion with interacting multiple model filter for improved aircraft position accuracy [J]. Sensors, 2013, 13(4): 4122–4137.

    Article  Google Scholar 

  12. Song Jin-ping, Wen Cheng-lin, Zhang Nan. An algorithm of correlated data fusion estimation based on multi-sensor multi-model dynamic system [J]. Journal of Henan University: Nature Science, 2002, 31(1): 25–29 (in Chinese).

    Google Scholar 

  13. Wen C L, Wen C B, Li Y. An optimal sequential decentralized filter of discrete-time systems with crosscorrelated noises [C]//Proceedings of the 17th World Congress The International Federation of Automatic Control. Seoul, Korea: IFAC. 2008: 7560–7565.

    Google Scholar 

  14. Yu An-xi, Liang Dian-nong, Yang Hong-wen, et al. Multisensor measurement fusion with colored noise [J]. Journal of Electronics & Information Technology, 2006, 28(11): 2051–2053 (in Chinese).

    Google Scholar 

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Authors and Affiliations

  1. Department of Automation, Shanghai Jiaotong University, Shanghai, 200240, China

    Zhi-feng Chen  (陈志锋) & Yun-ze Cai  (蔡云泽)

Authors
  1. Zhi-feng Chen  (陈志锋)
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  2. Yun-ze Cai  (蔡云泽)
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Corresponding author

Correspondence to Yun-ze Cai  (蔡云泽).

Additional information

Foundation item: the National Natural Science Foundation of China (No. 61374160), and the Shanghai Aerospace Science and Technology Innovation Fund (No. SAST201237)

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Chen, Zf., Cai, Yz. Fata fusion algorithm for multi-sensor dynamic system based on interacting multiple model. J. Shanghai Jiaotong Univ. (Sci.) 20, 265–272 (2015). https://doi.org/10.1007/s12204-014-1556-9

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  • DOI: https://doi.org/10.1007/s12204-014-1556-9

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