Deep Learning for Passive Synthetic Aperture Radar

Abstract

We introduce a deep learning (DL) framework for inverse problems in imaging, and demonstrate the advantages and applicability of this approach in passive synthetic aperture radar (SAR) image reconstruction. We interpret image recon- struction as a machine learning task and utilize deep networks as forward and inverse solvers for imaging. Specifically, we design a recurrent neural network (RNN) architecture as an inverse solver based on the iterations of proximal gradient descent optimization methods. We further adapt the RNN architecture to image reconstruction problems by transforming the network into a recurrent auto-encoder, thereby allowing for unsupervised training. Our DL based inverse solver is particularly suitable for a class of image formation problems in which the forward model is only partially known. The ability to learn forward models and hyper parameters combined with unsupervised training approach establish our recurrent auto-encoder suitable for real world applications. We demonstrate the performance of our method in passive SAR image reconstruction. In this regime a source of opportunity, with unknown location and transmitted waveform, is used to illuminate a scene of interest. We investigate recurrent auto- encoder architecture based on the 1 and 0 constrained least- squares problem. We present a projected stochastic gradient descent based training scheme which incorporates constraints of the unknown model parameters. We demonstrate through extensive numerical simulations that our DL based approach out performs conventional sparse coding methods in terms of computation and reconstructed image quality, specifically, when no information about the transmitter is available.

Submitted to IEEE Journal of Selected Topics in Signal Processing

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• Deep Convolutional Neural Network for Inverse Problems in Imaging
• Understanding Deep Convolutional Networks
• Recurrent Inference Machines for Solving Inverse Problems
• A Probabilistic Theory of Deep Learning
• A Deep Learning Approach to Block-based Compressed Sensing of Images
• Visual Scene Representations: Contrast, Scaling and Occlusion

Cited by in corpus (4)

• An Overview of Advances in Signal Processing Techniques for Classical and Quantum Wideband Synthetic Apertures
• Deep Learning for Waveform Estimation and Imaging in Passive Radar
• Unrolled Wirtinger Flow with Deep Decoding Priors for Phaseless Imaging
• RDAnet: A Deep Learning Based Approach for Synthetic Aperture Radar Image Formation