An exact maximum likelihood registration algorithm for data fusion

Data fusion is a process dealing with the association, correlation, and combination of data and information from multiple sources to achieve refined position and identity estimates. We consider the registration problem, which is a prerequisite process of a data fusion system to accurately estimate a...

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Bibliographic Details
Published in:IEEE transactions on signal processing Vol. 45; no. 6; pp. 1560 - 1573
Main Authors: Yifeng Zhou, Leung, H., Yip, P.C.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.06.1997
Institute of Electrical and Electronics Engineers
Subjects:
Applied sciences
Convergence
Coordinate measuring machines
Error correction
Exact sciences and technology
Information, signal and communications theory
Least squares methods
Mathematical methods
Maximum likelihood estimation
Newton method
Noise measurement
Recursive estimation
Sensor phenomena and characterization
Sensor systems
Telecommunications and information theory
Signal processing
Data fusion
Maximum likelihood
Algorithm
Radar
ISSN:1053-587X
Online Access:Get full text
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Summary:Data fusion is a process dealing with the association, correlation, and combination of data and information from multiple sources to achieve refined position and identity estimates. We consider the registration problem, which is a prerequisite process of a data fusion system to accurately estimate and correct systematic errors. An exact maximum likelihood (EML) algorithm for registration is presented. The algorithm is implemented using a recursive two-step optimization that involves a modified Gauss-Newton procedure to ensure fast convergence. Statistical performance of the algorithm is also investigated, including its consistency and efficiency discussions. In particular, the explicit formulas for both the asymptotic covariance and the Cramer-Rao bound (CRB) are derived. Finally, simulated and real-life multiple radar data are used to evaluate the performance of the proposed algorithm.
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ISSN:1053-587X
DOI:10.1109/78.599998