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|Title:||ISAR signal reconstruction using compressive sensing in MATLAB||Authors:||Meiklejohn, Stewart||Keywords:||DRNTU::Engineering::Electrical and electronic engineering::Antennas, wave guides, microwaves, radar, radio||Issue Date:||2016||Abstract:||The project aim was to examine the suitability of utilising compressive sensing in radar applications, while highlighting how such efforts can be examined and performed within MathWorks’ MATLAB software. As compressive sensing is a relatively new field in sampling, with regard to the traditional Shannon-Nyquist sampling theorem, it is required that the suitability to different applications be examined. Initially an understanding of compressive sensing and how it is applied in theory was established through the examination of papers previously published on the subject. In order to be a suitable candidate for compressive sensing sparsity is the required within the signal being sampled, while an incoherence with the sensing matrix being used is also needed. Simple examples and problems were carried out within MATLAB in order to understand how the techniques could be applied to the radar problem, while examining whether or not the signals associated with radar match the prerequisistes associated with compressive sensing. Again, the uses and theory of radar was examined to supply a suitable background upon which to establish these problems. Furthermore it was necessary for the author to develop the skills required for this project in MATLAB. Once the background information had been established MATLAB was utilised to compress supplied radar data in a manner that provided a distinguishable image that could then be compressively sensed. The compressive sensing operation can be carried out by a user of the program created in MATLAB by using the graphical user interface. While this method does not directly sample the signals as they return to the radar receiver, sampling the data that has been sampled at the Nyquist rate provides an adequate basis for the purposes of this report. As compressive sensing focuses on the idea of undersampling a signal, while still being able to recover it accurately, different undersampling values were used in order to examine how the results were affected. The benefits and effects of these different unsampling values were analysed to provide the reader with an understanding of which range of undersampling values adequately recover the information stored within the original continuous signal.||URI:||http://hdl.handle.net/10356/67057||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Student Reports (FYP/IA/PA/PI)|
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Updated on Jun 22, 2021
Updated on Jun 22, 2021
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