Real time FPGA implemnation of SAR radar reconstruction system based on adaptive OMP compressive sensing

Abstract

<p><span style="font-size: 9pt; font-family: 'Times New Roman', serif;">Synthetic Aperture Radar (SAR) is an imaging system based on the processing of radar echoes. The produced images have a huge amount of data which will be stored onboard or transmitted as a digital signal to the ground station via downlink to be processed. Therefore, some methods of compression on the raw images provides an attractive option for SAR systems design. One of these techniques which used for image reconstruction is the Orthogonal Matching Pursuit (OMP). OMP is an iterative algorithm which need high computational operations. The computational complexity of the iterative algorithms is high due to updating operations of the measurement vector and large number of iterations that are used to reconstruct the images successfully. This paper presents a new adaptive OMP algorithm to overcome this issue by using certain threshold. The new adaptive OMP algorithm is compared with the classical OMP algorithm using the Receiver Operating Characteristic (ROC) curves. The MATLAB simulations show that the new adaptive OMP algorithm improves the probability of detection at lower SNRs, reduce the computational operations as well as the number of required iterations. FPGA implementation of both the classical OMP and the adaptive OMP algorithm are also presented in this paper.</span></p>

Blending of three-dimensional geometric model shapes

“…Finally, the authors reconstruct the 3D building using the four corner points of the plane. On the other hand, Ashraf et al [30] implemented synthetic aperture radar (SAR) for reconstruction by processing of radar echoes based on adaptive orthogonal matching pursuit (OMP) compression sensing.…”

Analysis of the visualizing changes in radar time series using the REACTIV method through satellite imagery

“…Later 2014, following the success of European Space Agency (ESA) Copernicus acquisition programs with open-source product development, remote sensing processes and the search for new potential applications has been an unprecedented success [1]. Among the potentialities offered by this change of context, the exploitation of the temporal axis in particular is found where once obtaining a temporal series of images on the same site was rare and difficult [2], today many download platforms make it possible to build such batteries very quickly and anywhere in the world [3]. In parallel with obtaining these data, solutions for treating them with large computing powers are developing [4].…”