Exploiting intervoxel covariance as prior information in millimeter-wave computational imaging systems

Naren Viswanathan; David Schurig

doi:10.1117/12.2559385

18 May 2020 Exploiting intervoxel covariance as prior information in millimeter-wave computational imaging systems

Naren Viswanathan, David Schurig

Proceedings Volume 11396, Computational Imaging V; 113960L (2020) https://doi.org/10.1117/12.2559385
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Computational imaging techniques that rely on a compressed set of measurements and exploit prior information such as target size, scene sparsity, transceiver radiation pattern, etc are rapidly gaining popularity in areas such as medical and security imaging, remote sensing, and automotive radar as they can significantly reduce SWAP-C (Size, Weight, Power, and Cost) of hardware modules, especially at millimeter-wave frequencies. In this article, we propose using the covariance matrix of a large ensemble of representative targets to form a diagonalizing basis in which the transformed scene voxels are uncorrelated. In this basis, we introduce a method of image reconstruction, Covariance Likelihood based Regularization (CLR), where transformed voxels, with low likelihood according to the ensemble statistics, are penalized. We also discuss another method, Thresholded Eigenbasis (TE), which involves thresholding the eigenvalues of the covariance matrix and reconstructing the transformed scene voxels in a lower dimensional approximate basis. We use these techniques to reconstruct images from simulations of measurements made using a W-band (75 - 110 GHz) imaging system, where the linear imaging matrix is carefully designed based on vector electromagnetics and realistic hardware. Based on these reconstruction results, we discuss the opportunities and challenges for these methods, including scenarios where TE provides improved reconstruction speed and CLR provides improved accuracy.

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Naren Viswanathan and David Schurig "Exploiting intervoxel covariance as prior information in millimeter-wave computational imaging systems", Proc. SPIE 11396, Computational Imaging V, 113960L (18 May 2020); https://doi.org/10.1117/12.2559385

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KEYWORDS

Imaging systems

Computational imaging

Computing systems

Image restoration

Receivers

Transmitters

Compressed sensing

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