Radar Signal Processing for Target Tracking

Last-generation radar systems are provided with a considerable abundance of computation power, which was inconceivable a few decades ago and has led to more and more sophisticated processing schemes at different system levels. In addition, technological development has allowed for a reduction in the costs of remote sensing devices and an increasing proliferation of sensing systems for both civilian and military applications facing challenging scenarios, which may include outliers, intentional interference, etc. In this context, one of the main tasks accomplished by a radar system is the active/passive tracking of multiple targets. Such a function can be fed by either compressed data, namely, detections along with the associated rough measurements at the output of the signal processing unit, or raw data at the output of the matched filter (track-before-detect paradigm and/or synthetic aperture radar tracking) and collected by means of sensor networks or multistatic radar systems with either a fusion center or distributed tracking architecture. Additionally, realistic and outlier unexpected effects may be detrimental to some model assumptions, paving the way to a need for (and possible integration with) data-driven approaches. Finally, it is important to highlight that tracking functions may play a primary role in several operating contexts as, for instance, space debris monitoring (due to high traffic density of satellites), tracking of icebergs (which are very spread because of climate changes), UAV (or agile targets) detection and tracking, etc.

This Special Issue is focused on the design of modern tracking algorithms for multiple targets that take advantage of both enhanced available computational power and recent approaches to statistical signal processing based upon machine learning and/or compressed sensing over possibly distributed system architectures.

Prof. Danilo Orlando
Dr. Filippo Biondi
Dr. Domenico Ciuonzo
Dr. Carmine Clemente
Guest Editor

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