Picture compilation/emitter mapping and navigation using passive RF sensors in complex unstructured environments

"Picture compilation/mapping of emitters and/or navigation using passive RF sensors is emerging as a key capability for future autonomous systems to be deployed in complex unstructured environments. While there has been extensive research over decades spanning data fusion, tracking and classification of objects, their extension to passive sensor applications remains challenging on a number of counts:
The poor resolution, incompleteness and ambiguity of typical passive sensor measurements.
The associated lack of control on the timing of measurements, e.g. both a single rapid source or several less frequent sources may both explain the observed data.
The dense and cluttered environments with many 'clutter' emission sources.
The high degree of ambiguity and uncertainty associated with prior knowledge of the emitter characteristics.
The effects of the environment, e.g. terrain to screen emitters and multipath problems.
The aim of the PhD is to investigate data-fusion approaches which fuse all of the available information (e.g. Kinematic, prior knowledge, geographical, contextual and temporal/historical) to improve capability, performance and robustness of mapping/navigation techniques for passive RF sensors. For example, these investigations could focus on: the passive joint estimation & classification problem; generation of models of normality and subsequent anomaly detection; and/or consideration of the exploitation and limitations of employing 'weak' contextual or geographical information alongside stronger measured data
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