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Ground penetrating radar (GPR) is a remote geophysical sensing method that has been applied in the localization of underground utilities, bridge deck survey, localization of landmines, mapping of terrain for aid in driverless cars, etc. Multistatic GPR can deliver a faster survey, wider spatial coverage, and multiple viewpoints of the subsurface. However, because of the transmit and receive antennas spatial offset, formation of 3D GPR image by simple stacking of the acquired A-scans is inaccurate. Also, averaging of different receivers data may lead to destructive interference of back-scattered waves due to different time delays implied by the spatial offset, so averaging does not lead to higher SNR in general. Furthermore, the energy back-scattered by scatter points are spread in hyperbolas in the GPR raw data. Migration or imaging algorithms are employed to increase SNR by focusing the hyperbolas. This focusing process also leads to better accuracy in target localization. In this paper, a computationally efficient synthetic aperture radar (SAR) imaging algorithm that properly integrates multistatic GPR data in both ground and air-coupled cases is presented. The algorithm is successfully applied on two synthetic datasets. 

In this work we show how we can build a technology platform for cognitive imaging sensors using recent advances in recurrent neural network architectures and training methods inspired from biology. We demonstrate learning and processing tasks specific to imaging sensors, including enhancement of sensitivity and signal-to-noise ratio (SNR) purely through neural filtering beyond the fundamental limits sensor materials, and inferencing and spatio-temporal pattern recognition capabilities of these networks with applications in object detection, motion tracking and prediction. We then show designs of unit hardware cells built using complementary metal-oxide semiconductor (CMOS) and emerging materials technologies for ultra-compact and energy-efficient embedded neural processors for smart cameras.