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

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6 Author(s)
Fortunati, S. ; Dept. of Ing. dell''Inf., Univ. of Pisa, Pisa, Italy ; Gini, F. ; Greco, M.S. ; Farina, Alfonso
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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).

Published in:

Advances in Radar and Remote Sensing (TyWRRS), 2012 Tyrrhenian Workshop on

Date of Conference:

12-14 Sept. 2012