Aspects of measurement scheduling for tracking

Many target tracking subsystems have the ability to schedule their own data rates: essentially they can "order" new information whenever they need it, and the cost is in terms of the sensor resource. Uniform sampling, in which a new measurement is requested periodically and regularly, is the most commonly-used sampling scheme. Nonuniform schemes are seldom studied: it is "common knowledge" that they exhibit inferior performance and are too bizarre to consider seriously. In this paper, however, we show that such schemes may have been discarded prematurely: a nonuniform sampling can have its benefits. Specifically, the nonuniform and uniform sampling schemes are compared for two kind of trackers: the PDAF (probabilistic data association filter), which updates its track based on a single scan of information at a time: and N-D assignment (an optimization-based implementation of the MHT (multi-hypothesis tracker)), in which the sliding window involves many scans of observations. We show that, given the ground rule of maintenance of the same overall scan rate (i.e. the same sensor effort), uniform sampling is always optimal for the single-scan tracker in the sense of track life. However, a nonuniform sampling can outperform uniform sampling if a more sophisticated multi-scan tracker is used, particularly when: (i) the target has a high process noise: and/or (ii) the false alarm density is high; and/or (iii) the probability of detection is high.