ISSN 0253-2778

CN 34-1054/N

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Sensor data association using relative positions among targets and bias estimation between separate sensors

  • Department of Automation, University of Science and Technology of China, Hefei 230027, China

Funds:  National Natural Science Foundation of China (61273112), the Youth Innovation Promotion Foundation of CAS.
Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2014.01.004
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  • Author Bio:

    YU Zhaohua, male, born in 1990, master candidate. Research filed: Wireless sensor network, distributed signal processing. E-mail: zhhyu@mail.ustc.edu.cn

  • Corresponding author: LING Qiang
  • Received Date: 17 July 2013
  • Accepted Date: 04 September 2013
  • Rev Recd Date: 04 September 2013
  • Publish Date: 30 January 2014
    Abstract

  • Abstract

    Sensor data association is an important problem in modern multi-sensor systems. The purpose to solve this problem is to decide which measurements from the different sensors belong to the same target. The traditional methods for data association usually form the association matrix and find the optimal solution in different ways. These solutions are, however, sensitive to the characteristics of the sensors. A novel method which uses the relative positions among the targets and extracts the relative positions pattern to compare and search for the matching pairs between the separate sensor systems is proposed. An improved algorithm which is suitable for sensor data association using relative positions is also presented. The sensor bias has little influence upon this association algorithm due to the inherent characteristics of the relative positions. Simulation results show that association using relative positions is robust against sensor bias and has an overall improvement.

    Abstract

    Sensor data association is an important problem in modern multi-sensor systems. The purpose to solve this problem is to decide which measurements from the different sensors belong to the same target. The traditional methods for data association usually form the association matrix and find the optimal solution in different ways. These solutions are, however, sensitive to the characteristics of the sensors. A novel method which uses the relative positions among the targets and extracts the relative positions pattern to compare and search for the matching pairs between the separate sensor systems is proposed. An improved algorithm which is suitable for sensor data association using relative positions is also presented. The sensor bias has little influence upon this association algorithm due to the inherent characteristics of the relative positions. Simulation results show that association using relative positions is robust against sensor bias and has an overall improvement.
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    • References(12)
  • [1]
    Hall D L, Llinas J. Handbook of Multisensor Data Fusion[M]. Boca Raton, USA: CRC Press, 2001.
    [2]
    Waltz E L, Llinas J. Multisensor Data Fusion[M]. Boston, USA: Artech House, 1990.
    [3]
    Blackman S, Popoli R. Design and Analysis of Modern Tracking Systems[M]. Boston, USA: Artech House, 1999.
    [4]
    Konstantinova P, Udvarev A, Semerdjiev T. A study of a target tracking algorithm using global nearest neighbor approach[C]// Proceedings of the International Conference on Computer Systems and Technologies. New York, USA, 2003, E-line.
    [5]
    Stone L D, Williams M L, Tran T M.Track-to-track association and bias removal[C]// Proceedings of International Society for Optical Engineering. Culver, USA: SPIE Press, 2002: 315-328.
    [6]
    Levedahl M. An explicit pattern matching assignment algorithm[C]// Proceedings of International Society for Optical Engineering. Culver, USA: SPIE Press, 2002: 461-469.
    [7]
    Papageorgiou D J, Sergi J D. Simultaneous track-to-track association and bias removal using multistart local search[C]// Aerospace Conference. Big Sky, USA: IEEE Press, 2008: 1-14.
    [8]
    Ferry J P. Exact bias removal for the track-to-track association problem[C]// 12th International Conference on Information Fusion. Seattle, USA: IEEE Press, 2009: 1 642-1 649.
    [9]
    Shi Y, Wang Y, Shan X M. A novel fuzzy pattern recognition data association method for biased sensor data[C]// 9th International Conference on Information Fusion.Florence, Italy: IEEE Press, 2006: 1-5.
    [10]
    Du X J, Wang Y, Shan X M. Track-to-track association using reference topology in the presence of sensor bias[C]// Proceedings of the 2010 International Conference on Signal Processing. Beijing, China: IEEE Press, 2010: 2 196-2 201.
    [11]
    Chang S H, Cheng F H, Hsu W H, et al. Fast algorithm for point pattern matching: Invariant to translations, rotations and scale changes[J]. Pattern Recognition, 1997, 30(2): 311-320.
    [12]
    Kay S M. Fundamentals of Statistical Signal Processing: Estimation Theory[M]. Upper Saddle River, USA: Prentice-Hall, 1998.
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    [1]
    Hall D L, Llinas J. Handbook of Multisensor Data Fusion[M]. Boca Raton, USA: CRC Press, 2001.
    [2]
    Waltz E L, Llinas J. Multisensor Data Fusion[M]. Boston, USA: Artech House, 1990.
    [3]
    Blackman S, Popoli R. Design and Analysis of Modern Tracking Systems[M]. Boston, USA: Artech House, 1999.
    [4]
    Konstantinova P, Udvarev A, Semerdjiev T. A study of a target tracking algorithm using global nearest neighbor approach[C]// Proceedings of the International Conference on Computer Systems and Technologies. New York, USA, 2003, E-line.
    [5]
    Stone L D, Williams M L, Tran T M.Track-to-track association and bias removal[C]// Proceedings of International Society for Optical Engineering. Culver, USA: SPIE Press, 2002: 315-328.
    [6]
    Levedahl M. An explicit pattern matching assignment algorithm[C]// Proceedings of International Society for Optical Engineering. Culver, USA: SPIE Press, 2002: 461-469.
    [7]
    Papageorgiou D J, Sergi J D. Simultaneous track-to-track association and bias removal using multistart local search[C]// Aerospace Conference. Big Sky, USA: IEEE Press, 2008: 1-14.
    [8]
    Ferry J P. Exact bias removal for the track-to-track association problem[C]// 12th International Conference on Information Fusion. Seattle, USA: IEEE Press, 2009: 1 642-1 649.
    [9]
    Shi Y, Wang Y, Shan X M. A novel fuzzy pattern recognition data association method for biased sensor data[C]// 9th International Conference on Information Fusion.Florence, Italy: IEEE Press, 2006: 1-5.
    [10]
    Du X J, Wang Y, Shan X M. Track-to-track association using reference topology in the presence of sensor bias[C]// Proceedings of the 2010 International Conference on Signal Processing. Beijing, China: IEEE Press, 2010: 2 196-2 201.
    [11]
    Chang S H, Cheng F H, Hsu W H, et al. Fast algorithm for point pattern matching: Invariant to translations, rotations and scale changes[J]. Pattern Recognition, 1997, 30(2): 311-320.
    [12]
    Kay S M. Fundamentals of Statistical Signal Processing: Estimation Theory[M]. Upper Saddle River, USA: Prentice-Hall, 1998.
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