Coherent L2S THz Imaging Systems

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The increasing performance of coherent imaging in the long wavelength region of the electromagnetic spectrum, like the mm-wave and THz frequency ranges, has opened-up the path to a wide uptake of such sensor technologies. Application potential is enormous, given the capability of such wavelengths to image and sense in optically inaccessible situations, like inter alia remote sensing in arbitrary environmental conditions, subsurface imaging non-destructive testing, resilient autonomous car vision systems, or security related imaging systems like hidden explosives detection at airport checkpoints. In all such application scenarios coherent imaging has the immense advantage to attain information not accessible by optical imaging systems, and to be fundamentally capable to derive 3D object and scene information directly from phase data. However, such advantages are attained at the fundamental cost associated with all coherent illumination and imaging systems of having to cope with interference artifacts like ringing, speckles and multi-path interferences, which can totally inhibit image formation, and which are particularly strong in this frequency range. Application uptake for such systems remains elusive, therefore, given such fundamental restrictions. This project plans to use artificial intelligence-based approaches to learn to cope with such fundamental limitations of coherent imaging systems, and to train and validate their adequacy in the mm-wave and THz frequency ranges, where the system parameter variability has a particularly large degree of freedom for the image generation process. Along the idea of L2S to establish a true end-to-end learning paradigm along the complete system realization and image analysis pipeline, following goals are foreseen:- Learning how physical knowledge containing network architectures can be used to develop adaptive synthetic image generation approaches that enhance the image quality and correct scene dependent interference artifacts for coherent 3D imaging.- Evaluating and understanding the robustness of machine-learning based segmentation of reconstructed 3D THz imaging data originating from sparse illumination and sensor arrangements, including differential imaging modes.- Assessing and learning if segmentation can be attained directly from raw sensory data, without the intermediate 3D image generation step by synthetic reconstruction. - Learning how a sensory task dependent system adaptation can maximize imaging and recognition capabilities, and at the same time minimize hardware and data acquisition effort.These goals will be experimentally addressed using THz imaging systems based on MIMO (multiple-input multiple-output) FMCW (frequency-modulated continuous-wave) synthetic image reconstruction approaches, in order to allow a maximum variability of the system configuration and multiple-illumination signal variability and as a test scenario for the L2S methodologies developed by the partners.