Survey of Maneuvering Target Tracking. Part I: Dynamic Models

The key to successful target tracking lies in the effective extraction of useful information about the target’s state from observations. A good model of the target will certainly facilitate this information extraction to a great extent. In general, one can say without exaggeration that a good model is worth a thousand pieces of data. This statement has an even stronger positive connotation in target tracking where observation data are rather limited. Most tracking algorithms are model based because knowledge of target motion is available and a good model-based tracking algorithm will greatly outperform any model-free tracking algorithm if the underlying model turns out to be a good one. As such, it is hard to overstate the importance of the role of a good model here. Various mathematical models of target motion have been developed over the past three decades. They are, however, scattered in the literature. Many of them have never appeared in any periodical in the public domain. As a result, few people have a good knowledge of these models. This is partly due to a lack of a comprehensive survey. The importance of such a survey for both practitioners and researchers in the tracking community is evident. The single best source so far is, in our opinion, the recent book by Blackman and Popoli [1], which is nonetheless far from complete. Some more or less standard models for target motion can be found in established books on target tracking and/or estimation, such as [2–12]. This paper is the first part of a comprehensive and up-to-date survey of the techniques for maneuvering target tracking. The survey is an ongoing project. The conference versions of its first several parts have appeared in [13–17]. It is well known that the so-called measurement-origin uncertainty and target motion uncertainty are two major challenges in target tracking. To limit the scope of the work, this survey deals only with the second uncertainty, leaving the techniques unique for the data-association problems untouched. Target detection, tracking, and recognition are closely interrelated areas, with significant overlaps. It is not easy to draw a clear line to separate them. To be relatively more focused, this part covers mainly dynamic models of a “point target,” that is, those of the dynamic (temporal) behaviors, rather than spatial characteristics, of a target. While many of these models are also useful for target detection and recognition, this survey is only concerned with their value for target tracking. This of course does not prevent us from developing or applying a model that describes both the temporal evolution and spatial characteristics of a target. Needless to say, target dynamic models and tracking algorithms have intimate ties. The