A system configured for generating a radar image of a scene, the system comprising: a set of antennas, at least one processor; and a memory having instructions stored thereon that, when executed by the at least one processor, cause the system to: receive radar measurements of the scene collected from the set of antennas, wherein the radar measurements are measurements associated with reflections of a radar pulse transmitted to the scene; generate the radar image of the scene by solving a sparse recovery problem, wherein the sparse recovery problem is configured to: determine a set of image shifts of the radar image corresponding to different uncertainties of the set of antennas, wherein the set of antennas are under uncertainties caused by one or a combination of position ambiguities and clock ambiguities of each of the antennas, and update an estimate of the radar image, based on the determined set of image shifts of the radar image, until a termination condition is met, such that, for each of the antennas, the estimate of the radar image shifted by the corresponding shift of the radar image fits the radar measurements of the antenna, and wherein the sparse recovery problem is solved with a neural network denoiser that denoises a filtering of the estimate of the radar image; and render the radar image when the termination condition is met; characterized in that the neural network denoiser is trained with a training dataset, wherein the training dataset includes input-output pairs z m,x m, where training input images z m are back projected images formed by applying a back projection operator A m H to example radar measurements y m = A m x m + n, such that z m =A m H y m, output images x m are ground truth object radar images, and n represents added noise; the filtering of the estimate of the radar image is performed by applying a forward radar operator and an adjoint radar operator to the estimate of the radar image; the forward radar operator and the adjoint radar operator are configured to filter noise from the estimate of the radar image that the neural network denoiser is not trained on, to produce a filtered radar image; and the filtered radar image is applied to the neural network denoiser. receive radar measurements of the scene collected from the set of antennas, wherein the radar measurements are measurements associated with reflections of a radar pulse transmitted to the scene; generate the radar image of the scene by solving a sparse recovery problem, wherein the sparse recovery problem is configured to: determine a set of image shifts of the radar image corresponding to different uncertainties of the set of antennas, wherein the set of antennas are under uncertainties caused by one or a combination of position ambiguities and clock ambiguities of each of the antennas, and update an estimate of the radar image, based on the determined set of image shifts of the radar image, until a termination condition is met, such that, for each of the antennas, the estimate of the radar image shifted by the corresponding shift of the radar image fits the radar measurements of the antenna, and wherein the sparse recovery problem is solved with a neural network denoiser that denoises a filtering of the estimate of the radar image; and determine a set of image shifts of the radar image corresponding to different uncertainties of the set of antennas, wherein the set of antennas are under uncertainties caused by one or a combination of position ambiguities and clock ambiguities of each of the antennas, and update an estimate of the radar image, based on the determined set of image shifts of the radar image, until a termination condition is met, such that, for each of the antennas, the estimate of the radar image shifted by the corresponding shift of the radar image fits the radar measurements of the antenna, and wherein the sparse recovery problem is solved with a neural network denoiser that denoises a filtering of the estimate of the radar image; and render the radar image when the termination condition is met; characterized in that the neural network denoiser is trained with a training dataset, wherein the training dataset includes input-output pairs z m,x m, where training input images z m are back projected images formed by applying a back projection operator A m H to example radar measurements y m = A m x m + n, such that z m =A m H y m, output images x m are ground truth object radar images, and n represents added noise; the filtering of the estimate of the radar image is performed by applying a forward radar operator and an adjoint radar operator to the estimate of the radar image; the forward radar operator and the adjoint radar operator are configured to filter noise from the estimate of the radar image that the neural network denoiser is not trained on, to produce a filtered radar image; and the filtered radar image is applied to the neural network denoiser.