Least squares estimation and hybrid Cramér-Rao lower bound for absolute sensor registration

An important prerequisite for successful multisensor integration is that the data from the reporting sensors are transformed to a common reference frame free of systematic or registration bias errors. If not properly corrected, registration errors can seriously degrade the global surveillance system...

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Bibliographic Details
Main Authors: Fortunati, S., Gini, F., Greco, M. S., Farina, A., Graziano, A., Giompapa, S.
Format: Conference Proceeding
Language:English
Subjects:
bias errors
Coordinate measuring machines
Covariance matrix
Cramér-Rao lower bound
Equations
grid-locking
least squares algorithm
Measurement uncertainty
Multisensor system
Radar measurements
sensor registration
Vectors
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Summary:An important prerequisite for successful multisensor integration is that the data from the reporting sensors are transformed to a common reference frame free of systematic or registration bias errors. If not properly corrected, registration errors can seriously degrade the global surveillance system performance. The absolute sensor registration (or grid-locking) process aligns remote data coming from sensors to an absolute reference frame. In this paper we consider a multi-target scenario and we address the problem of jointly estimating registration errors involved in the absolute grid-locking problem with two radars. A linear Least Squares (LS) estimator is derived and its statistical performance compared to the hybrid Cramér-Rao lower bound (HCRLB).
DOI:10.1109/TyWRRS.2012.6381098