<title>Feedback in track fusion without process noise</title>

Abstract

Abstract Track fusion is one of the algorithm architectures for tracking multiple targets with data from multiple sensors. In trackfusion for example, sensor-level tracks can be combined to form global-level tracks that are based on data from all thesensors. These multiple sensor, global-level tracks can then be fed back to the sensor-level trackers to reduce the dataassociation errors.The global tracks, however, are cross-correlated with the sensor-level tracks. A method is needed to take this track-to-trackcross-correlation into account. This cross-correlation of the global and sensor tracks must be considered when providing theglobal tracks to the sensor trackers as well as when providing the sensor tracks to the global tracker. Even without processnoise, the global and sensor tracks are cross-correlated because they are based on common data. With feedback, both theglobal tracks and the tracks from each sensor are based on prior data from not only the sensor itself, but also the othersensors.This paper presents a method for dealing with the cross-correlations ofthe tracks in track fusion for feeding back the globallevel tracks. New methods have been recently developed for track fusion without process noise. These new methods addresstrack fusion without feedback of global-level tracks to the lower levels. Application of these new methods are employed inthis paper to deal with the complex cross-correlations involved when global tracks are fed back to the sensor-level trackers.Keywords: tracking, multiple sensor tracking, multiple target tracking, track fusion, sensor fusion, Bayesian methods,Kalinan filter, estimation, decorrelation, equivalent measurement, inverse Kalman filter.