Time-varying Bias Estimation for Asynchronous Multi-sensor Multi-target Tracking Systems Using STF

HU Yanyan; ZHOU Donghua

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Article Navigation > Chinese Journal of Electronics > 2013 > 22(3): 525-529

HU Yanyan and ZHOU Donghua, “Time-varying Bias Estimation for Asynchronous Multi-sensor Multi-target Tracking Systems Using STF,” Chinese Journal of Electronics, vol. 22, no. 3, pp. 525-529, 2013,

Citation:

HU Yanyan and ZHOU Donghua, “Time-varying Bias Estimation for Asynchronous Multi-sensor Multi-target Tracking Systems Using STF,” Chinese Journal of Electronics, vol. 22, no. 3, pp. 525-529, 2013,

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Time-varying Bias Estimation for Asynchronous Multi-sensor Multi-target Tracking Systems Using STF

HU Yanyan^{1,2,3
,},
ZHOU Donghua^1,3

1.
Department of Automation, Tsinghua University, Beijing 100084, China;
2.
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China;
3.
Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China

Funds: This work is supported by the National Basic Research Program of China (973 Program) (No.2010CB731800, No.2009CB320602) and the National Natural Science Foundation of China (No.61210012, No.61021063, No.61290324).

Received Date: 2011-05-01
Rev Recd Date: 2012-05-01
Publish Date: 2013-06-15

Abstract

Abstract

Time-varying bias estimation problem was studied for multi-target tracking systems with asynchronous sensors without knowing bias dynamics. We considered general situations, where the number of sensors was arbitrary as well as their sampling rates and initial sampling instants. A two-layer fusion structure was adopted. For each target, a pseudo-measurement of sensor bias was generated by fusing sensor measurements of this target. To make the pseudo-measurement decoupled from the target state, the fusion coefficient matrix was determined to be a basis for the left null space of an augmented observation matrix. The bias estimation algorithm was proposed based on the Strong tracking filter (STF) by fusing pseudomeasurements. The proposed algorithm makes use of all available sensor information, has strong tracking ability to abrupt changes, and avoids the matrix inversion for large dimensional matrices. Finally, the performance of the proposed algorithm is illustrated by the numerical simulation.
- Asynchronous sensors,
- Bias estimation,
- Data fusion,
- Multi-target tracking,
- Strong tracking filter

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References(14)

References

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