24 research outputs found

    Compressed Sensing for Block-Sparse Smooth Signals

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    We present reconstruction algorithms for smooth signals with block sparsity from their compressed measurements. We tackle the issue of varying group size via group-sparse least absolute shrinkage selection operator (LASSO) as well as via latent group LASSO regularizations. We achieve smoothness in the signal via fusion. We develop low-complexity solvers for our proposed formulations through the alternating direction method of multipliers

    Pseudo-Zernike Moments Based Sparse Representations for SAR Image Classification

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    We propose radar image classification via pseudo-Zernike moments based sparse representations. We exploit invariance properties of pseudo-Zernike moments to augment redundancy in the sparsity representative dictionary by introducing auxiliary atoms. We employ complex radar signatures. We prove the validity of our proposed methods on the publicly available MSTAR dataset

    Graph Signal Processing-Based Imaging for Synthetic Aperture Radar

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    In this paper, we propose graph signal processing based imaging for synthetic aperture radar. We present a modified version of fused least absolute shrinkage and selection operator to cater for graph structure of the radar image. We solve the cost function via alternating direction method of multipliers. Our method provides improved denoising and resolution enhancing capabilities. It can also accommodate the compressed sensing framework quite easily. Experimental results corroborate the validity of our proposed methodology

    Compressive Sensing Technique for Mitigating Nonlinear Memory Effects in Radar Receivers

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    Compressive sampling based differential detection for UWB impulse radio signals

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    Noncoherent detectors significantly contribute to the practical realization of the ultra-wideband (UWB) impulse-radio (IR) concept, in that they allow avoiding channel estimation and provide highly efficient reception capabilities. Complexity can be reduced even further by resorting to an all-digital implementation, but Nyquist-rate sampling of the received signal is still required. The current paper addresses this issue by proposing a novel differential detection (DD) scheme, which exploits the compressive sampling (CS) framework to reduce the sampling rate much below the Nyquist-rate. The optimization problem is formulated to jointly recover the sparse received signal as well as the differentially encoded data symbols, and is compared with both the separate approach and the scheme using the compressed received signal directly, i.e., without reconstruction. Finally, a maximum a posteriori based detector using the compressed symbols is developed for a Laplacian distributed channel, as a reference to compare the performance of the proposed approaches. Simulation results show that the proposed joint CS-based DD brings the considerable advantage of reducing the sampling rate without degrading the performance, compared with the optimal MAP detector

    Imaging for a Forward Scanning Automotive Synthetic Aperture Radar

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    Adaptive Subaperture Integration for Wide-Angle Synthetic Aperture Radar

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    In this article, we present an adaptive subaperture integration method for wide-angle synthetic aperture radar (SAR) for improved imaging, with emphasis on short-to-medium range applications. In order to avoid full-aperture integration, traditional approaches use fixed-width subapertures, which may not conform to the persistence angle of the scatterers. Coherent integration gains over the aperture are possible if integration is carried out over the persistence angle of the scatterers, because integrating shorter than the persistence angle may spread the scattering response across multiple subapertures or, conversely, integrating more than the persistence angle may cause noise accumulation along with the useful signal. In this article, we propose to use change-point detection methods to estimate the persistence widths of the scatterers, and consequently enhance the coherent integration gains, resulting in improved imaging. We compare our proposed methods with the standard integration approaches as well as a recently proposed adaptive integration approach. We provide qualitative and quantitative analyses to prove that our proposed methods outperform the existing approaches. We present experimental results on the real-data of our low-terahertz radar as well as a publicly available dataset to validate our claims.</p

    Pseudo-Zernike Moments Based Sparse Representations for SAR Image Classification

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    Compressive Sampling-Based Multiple Symbol Differential Detection for UWB Communications

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