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Ultrawideband (UWB) radar sensors are an emerging biosensing modality that can be used to assess the dielectric properties of internal tissues. Antenna effects, including antenna body interactions limit the sensors ability to isolate the weak returns from the internal tissues. In this paper we develop a data driven calibration method for recovering Green’s function of the multilayered media model of the tissue profiles using an Invertible Neural Network (INN). The proposed INN structure is trained to invert the antenna transfer function to form estimates of the Green’s function modeling returns from internal tissues. We use simulation experiments to assess the effectiveness of the trained INN in antenna transfer function inversion. 

The wavelength used for illumination dictates the scale of the mechanisms that interact with the incident electromagnetic (EM) energy. We model the synthetic Aperture Radar Image of a target as a superposition of the returns from scattering mechanisms that depend on the wavelength of the illuminating waveform and the viewing angle. In this work, we present a method to jointly model the scattering responses of the target over a wide aperture of measurements and a wide swath of frequencies spanning the C to X Band. Specifically, we estimate the location of the scattering centers and their azimuth-dependent responses normalized by the wavelength, jointly for low and high bands. We verify the validity of the proposed model using simulated data from a backhoe and Civilian vehicle data domes dataset over two non-overlapping frequency bands centered at 7GHz and 12 GHz. 

Generative models learned from training using deep learning methods can be used as priors in under-determined inverse problems, including imaging from sparse set of measurements. In this paper, we present a novel hierarchical deep-generative model MrSARP for SAR imagery that can synthesize SAR images of a target at different resolutions jointly. MrSARP is trained in conjunction with a critic that scores multi resolution images jointly to decide if they are realistic images of a target at different resolutions. We show how this deep generative model can be used to retrieve the high spatial resolution image from low resolution images of the same target. The cost function of the generator is modified to improve its capability to retrieve the input parameters for a given set of resolution images. We evaluate the model's performance using three standard error metrics used for evaluating super-resolution performance on simulated data and compare it to upsampling and sparsity based image super-resolution approaches. 

Sensors that can rapidly assess physiology in the clinic and home environment are poised to revolutionize research and practice in the management of chronic diseases such as heart failure. Ultrawideband (UWB) radar sensors provide a viable and unobtrusive alternative to traditional sensor modalities for physiological sensing. In this paper, we consider the problem of estimation of multilayer tissue profiles using an ultrawideband radar sensor. We pose the joint estimation of the ultrawideband pulse waveform and the multilayer tissue profile as a blind deconvolution problem. We show that constraints on the pulse waveform (bandwidth and time duration) and the structure of tissue range profile (sparsity) can be used to regularize the inversion. We derive both convex and non-convex algorithms for the joint estimation of the pulse waveform and the tissue reflectivity profile and demonstrate the effectiveness of the proposed methods with measured and simulated data experiments.