CLC number: TP73
On-line Access: 2018-11-11
Received: 2018-04-07
Revision Accepted: 2018-09-24
Crosschecked: 2018-09-25
Cited: 0
Clicked: 6010
Zhi-hua Lu, Meng-yao Zhu, Qing-wei Ye, Yu Zhou. Performance analysis of two EM-based measurement bias estimation processes for tracking systems[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(9): 1151-1165.
@article{title="Performance analysis of two EM-based measurement bias estimation processes for tracking systems",
author="Zhi-hua Lu, Meng-yao Zhu, Qing-wei Ye, Yu Zhou",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="19",
number="9",
pages="1151-1165",
year="2018",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1800214"
}
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%T Performance analysis of two EM-based measurement bias estimation processes for tracking systems
%A Zhi-hua Lu
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%A Qing-wei Ye
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%J Frontiers of Information Technology & Electronic Engineering
%V 19
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%P 1151-1165
%@ 2095-9184
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1800214
TY - JOUR
T1 - Performance analysis of two EM-based measurement bias estimation processes for tracking systems
A1 - Zhi-hua Lu
A1 - Meng-yao Zhu
A1 - Qing-wei Ye
A1 - Yu Zhou
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 19
IS - 9
SP - 1151
EP - 1165
%@ 2095-9184
Y1 - 2018
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1800214
Abstract: In target tracking, the measurements collected by sensors can be biased in some real scenarios, e.g., due to systematic error. To accurately estimate the target trajectory, it is essential that the measurement bias be identified in the first place. We investigate the iterative bias estimation process based on the expectation-maximization (EM) algorithm, for cases where sufficiently large numbers of measurements are at hand. With the assistance of extended Kalman filtering and smoothing, we derive two EM estimation processes to estimate the measurement bias which is formulated as a random variable in one state-space model and a constant value in another. More importantly, we theoretically derive the global convergence result of the EM-based measurement bias estimation and reveal the link between the two proposed EM estimation processes in the respective state-space models. It is found that the bias estimate in the second state-space model is more accurate and of less complexity. Furthermore, the EM-based iterative estimation converges faster in the second state-space model than in the first one. As a byproduct, the target trajectory can be simultaneously estimated with the measurement bias, after processing a batch of measurements. These results are confirmed by our simulations.
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