Clutter Mitigation for Target Tracking
Edmund Brekke · 2012
Traditionally, the literature on target tracking assumes that the targets of interest are embedded in homogenous Rayleigh distributed background noise. It is most often assumed that purely kinematic point measurements are extracted from the sensor images, so that the tracking problem can be phrased in terms of data association. The tracker has to decide which point measurements are likely to have been caused by the target, and update the track correspondingly. All other measurements are discarded as clutter. This thesis concerns clutter which does not conform to this framework. The following three challenges are addressed: dim targets, heavy-tailed clutter and wakes. For very dim targets it is impossible to extract point measurements, and tracking can only be done by working directly on the raw sensor images. Several methods for such track-before-detect can be found in the literature. In recent years methods based on sequential Monte Carlo have gained strong popularity. This thesis demonstrates that such methods cannot be expected to perform well unless the amplitudes of sensor cells are treated in a robust way. More precisely, it is demonstrated that a satisfactory performance only can be achieved when the uncertainty of the background noise estimate is taken into