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).

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

GINI, FULVIO;GRECO, MARIA;
2012-01-01

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 Cramér-Rao lower bound (HCRLB).
2012
9781467324434
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/238476
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