Least squares estimation and hybrid Cramér-Rao lower bound for absolute sensor registration
Stefano Fortunati, Fulvio Gini, Maria Greco, Alfonso Maria Farina, A. Graziano, Sofia Giompapa · 2012
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).