@inproceedings{76dd8ed108bb4de98feb3cba016ea4b3,
title = "Least squares estimation and hybrid Cram{\'e}r-Rao lower bound for absolute sensor registration",
abstract = "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 Cram{\'e}r-Rao lower bound (HCRLB).",
keywords = "Cram{\'e}r-Rao lower bound, Multisensor system, bias errors, grid-locking, least squares algorithm, sensor registration",
author = "Stefano Fortunati and Fulvio Gini and Greco, \{Maria S.\} and Alfonso Farina and Antonio Graziano and Sofia Giompapa",
year = "2012",
month = dec,
day = "1",
doi = "10.1109/TyWRRS.2012.6381098",
language = "English",
isbn = "9781467324434",
series = "Proceedings of the 2012 Tyrrhenian Workshop on Advances in Radar and Remote Sensing: From Earth Observation to Homeland Security, TyWRRS 2012",
pages = "30--35",
booktitle = "Proceedings of the 2012 Tyrrhenian Workshop on Advances in Radar and Remote Sensing",
note = "2012 Tyrrhenian Workshop on Advances in Radar and Remote Sensing: From Earth Observation to Homeland Security, TyWRRS 2012 ; Conference date: 12-09-2012 Through 14-09-2012",
}