Group and extended object tracking

Much of the literature on target tracking is based on the assumption that the observing sensor produces a single point measurement of the target. In practice, this is often not the case. For example, a high resolution sensor may be able to resolve features on an extended target. An analogous problem is that of tracking a group of point targets moving in formation. In both cases, there is a strong interdependency between the individual sensor measurements. In this paper, we handle both of these cases via the same model of individual motion superposed on a common "bulk" effect. A common approach to tracking groups of (dependent) points is to perform the update operation in two distinct steps. The first stage is to find the "best" match or registration between the set of measurements and the predicted pattern, while the second step is to generate an estimate based on the assumption that the selected association is correct. The complexity of managing multiple hypotheses with strongly interdependent groups is usually avoided. However, particularly in stressing scenarios, where the measurement-object association is highly uncertain, a multiple hypothesis approach should have significant advantages. We present a Bayesian multiple hypothesis solution using the bootstrap or particle filter technique in which the probability distribution of the problem state vector is represented by a set of random samples or "particles". The great advantage of this approach is that the sample set implicitly includes information on previous hypotheses and so awkward hypothesis management is avoided. Only the feasible association hypotheses for the current update of the filter need to be considered for construction of the measurement likelihood function.