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, the registration errors can seriously degrade the global surveillance system performance by increasing tracking errors and even introducing ghost tracks. The relative sensor registration (or grid-locking) process aligns remote data to local data under the assumption that the local data are bias free and that all biases reside with the remote sensor. In this paper, we consider all registration errors involved in the grid-locking problem, i.e., attitude, measurement, and position biases. A linear least squares (LS) estimator of these bias terms is derived and its statistical performance compared to the hybrid Cramér-Rao lower bound (HCRLB) as a function of sensor locations, sensors number, and accuracy of sensor measurements

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Radar tracking of a ballistic target is a topic presenting very complex issues especially when the characteristics of the objects\ud flying into the Earth’s atmosphere are poorly known. As the target identification is required to obtain good performance, a technique\ud frequently employed in the practical applications is the multiple model filter. This study, thought the increasing interest for this class of\ud estimators, aims to identify the key parameters of the problem by making recognisable their effects on the solution. Thus – along with\ud the ideal operative conditions – the relative geometry target-sensor, the presence of missing plots, the measurement uncertainties and\ud the credibility of filter predictions are investigated. In order to have a strict benchmark of evaluation the multiple model approach\ud performance is compared with the optimal estimate given by posterior Cramér–Rao lower bounds