Recursive solution to the sensor registration problem in a multiple-sensor-tracking scenario

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Abstract
This paper present an on-line solution to the aircraft tracking problem with a network of spatially distributed sensor units that are imperfectly registered. We consider errors in the relative positions and alignments of the measurement units. The sensor errors (or uncertainties) are estimated by an extended Kalman filter, along with the track variables. This optimal solution is compared through Monte- Carlo simulations to a suboptimal filter, that neglects the sensor uncertainties. The measurements are taken by 2D search sensor units or 3D track sensor units. When using track sensor data, the registration errors degrade substantially the accuracy of the aircraft position estimates and the optimal filter provides position estimates that are more accurate than those of the suboptimal filter. In the search sensor case, the registration errors are small compared to the search sensor measurement noise level and have a lower impact on the tracking performance.

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