Principles of Modern Radar - Volume 2 1891121537

686 CHAPTER 15 Multitarget, Multisensor TrackingFinally, the measurement update step (e.g., described by (15.11)–(15.16) for an EKF)is performed for the tracks that received updates in the surviving hypotheses. Redundanttracks (i.e., duplicate tracks on the same object) are also sought at this stage, so that theycan be deleted. The process then repeats the next time that measurements are collected.Summary Regardless of the particular implementation, the key advantage of the MHT isthat it allows data association ambiguities to resolve naturally over time, often producingtracks that are more accurate and pure than they would be using on a single-hypothesistracker. The MHT does not dictate a particular cost function of assignment algorithm(although the cost in (15.20) and the JVC assignment algorithm are popular choices); itsimply populates and solves these cost matrices using data collected over a longer periodof time than single-hypothesis trackers. This translates into significant computationalrequirements, suggesting that MHTs be used only when the application includes sufficientassociation ambiguity to require them.15.2.1.3 Cluster TrackingThe MHT (from Section 2.1.2) defers data association decisions, in the hope that additionalscans of data will mitigate the ambiguity. An alternative approach when faced with a densetarget scene is to capitulate. Rather than attempting to resolve these ambiguities, clustertracking (presented in this section) leverages the fact that multiple targets are closelyspacedand tracks them as a group (i.e., a cluster), thereby reducing the computationaldemands and requisite communications bandwidth [43]. This may be preferable to anMHT if the data association ambiguity isn’t likely to resolve quickly or if the computationsrequired by the MHT cannot be accommodated. It could also be used in tandem with aMHT (e.g., by arranging hypotheses that include cluster tracks); although often treatedthis way in the literature, they need not be mutually exclusive.Approaches to cluster tracking documented in the literature fall into three main categories[43]. On one extreme are the papers that propose creating and maintaining onlycluster track information. Tracks on individual objects in the cluster are not created ormaintained. On the other end of the spectrum are cluster tracking algorithms that createand maintain individual tracks, using the cluster information only as a supplement for thoseindividual tracks. In between these two extremes are approaches that create and maintainboth cluster and individual tracks; the emphasis in these more-moderate approaches tocluster tracking is on the cluster track, and individual tracks supplement the cluster track,rather than the other way around.While it appears to have potential for addressing dense target scenes, cluster trackingis fraught with challenges [43]. Cluster management algorithms are required to effectivelyinitiate, merge, split, and delete clusters as sets of closely-spaced (and possibly unresolved)targets shift over time. Addressing objects that enter and exit a cluster requires similardeftness. Clusters of objects may also cross or overlap at times, complicating matters.Nevertheless, the potential computational and communications bandwidth savings rendercluster tracking an area ripe for further research.15.2.2 Modeling Target DynamicsIn the standard EKF, the same propagation function is applied to all tracks, to bring thestates and covariances from the previous measurement time forward to the new measurementtime. The more objects in track, the more likely it becomes that one (or more) ofthem will have dynamics not well-modeled by the propagator and process noise. When this