Clutter Mitigation for Target Tracking

Abstract

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 account. This is done by means of marginalization over a flat prior distribution for the unknown background power.

Similar issues arise in the treatment of heavy-tailed clutter. A heavy-tailed background is a background which generates more frequent occurrences of target-like outliers than what one would expect under the Rayleigh assumption. In other words, more false alarms must be accepted if we shall have any hope of detecting the target, and this causes an inevitable degradation of performance. This thesis presents the first systematic study of this performance loss from a target tracking point of view. It is demonstrated that the performance loss may be substantially mitigated by usage amplitude information, depending on how this information is treated.

Another troublesome source of clutter is wakes that appear behind the target. This problem arises in sonar tracking of human divers, in the tracking of ships using surveillance radars, and also in radar tracking of ballistic missiles. This thesis suggests a new solution to this problem. While previous research has used an approach described as probabilistic editing, the new solution solves the wake problem in a Bayesian framework by means of marginalization.

These three advances are all tested and compared to traditional approaches using Monte-Carlo simulations. A theoretical analysis of the gains from amplitude information in heavy-tailed clutter is also carried out by means of the modified Riccati equation.

The recurrent theme of this thesis is that clutter in general increases uncertainty, and that this uncertainty should be marginalized out by tracking method. The results of this thesis therefore contribute to the viewpoint that tracking methods should be developed by careful probabilistic modeling. The gains from appropriate modeling of the clutter are considerable, even with very simple models and in the presence of large estimation uncertainties. Track-loss rates are typically reduced by half compared to existing methods, and often by more.