Principles of Modern Radar - Volume 2 1891121537

15.5 Further Reading 69515.4 SUMMARYTarget tracking consists of two main steps: measurement-to-track data association andtrack filtering. When multiple targets are present in proximity, both steps are more prone toerrors. The more the measurement covariances for multiple targets overlap, the greater thedata association ambiguity. This chapter presented three sets of techniques for achievinggood performance in the face of this ambiguity. One option is to incorporate features intothe measurement-to-track cost matrix. This can be a reasonable approach, if the chosenfeature is readily and accurately observable and if it segregates the target set. Regardless ofthe chosen cost function, another approach to resolving measurement-to-track ambiguityis to defer the decision until additional scans of measurements have been collected. TheMHT uses this approach, which works well in cases where the ambiguity is likely toresolve over time. In some cases, the targets may be unresolved or very closely-spaced forlong periods of time, necessitating cluster tracking.Track filtering can also be more challenging in multitarget scenarios; the more objectsin track, the more likely it may be that the set of targets will include some with disparatedynamics. Track accuracy, and even track maintenance, suffer when the track filter predictionsfail to match the true target dynamics. The IMM estimator is a common approach forcoping with this problem, and can address both a single object that has multiple potentialdynamic modes and a set of objects with potentially disparate dynamic modes. While theIMM estimator provides a good tradeoff between track accuracy and track maintenance, itsperformance can suffer when a large number of modes are required to adequately addressthe observed target classes. The VS-IMM combats this problem by allowing the numberof modes in the IMM to adapt over time for each track.Multisensor tracking (or sensor fusion) is increasingly common, for a number ofreasons. Many applications require coverage of a region larger than that which can beviewed by a single sensor; hence, sensor fusion arises out of necessity to cover the entirearea of interest. Fusion of data from spatially-distributed sensors also offers improvedtarget observability. The capacity of the system also increases via multisensor tracking;the more radars in the system, the more objects that it can track. Finally, multisensortracking improves system robustness, as the system can withstand loss (due to reliabilityfailures of malicious adversary actions) of a subset of its sensors.A number of multisensor architectures are possible, with measurement-level fusionand track-level fusion being the most common choices. Certain challenges are inherent toeach and must be addressed to ensure that the sensor fusion result is actually better thanthat of the individual contributing sensors.15.5 FURTHER READINGThe literature on multitarget, multisensor tracking is vast and rapidly evolving. With this inmind, the Journal for Advances in Information Fusion is recommended for the reader whowishes to stay abreast of advances in sensor fusion. This excellent, free, on-line resourceis updated twice each year and contains peer-reviewed, journal-quality papers on sensorfusion.While far from exhaustive, the following references are also recommended for thereader who is interested in multitarget tracking metrics, performance prediction, and non-Gaussian/nonlinear filtering applications.