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@article{144008,
        author = {LINGIREDDY VIJAYAPURNA and G.Srinivasa Rao and S.V.Jagadeesh Chandra and A.Narendra Babu},
        title = {RADAR IMAGING BY SPARSITY AND COMPRESSED SENSING},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {3},
        number = {5},
        pages = {80-84},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=144008},
        abstract = {Radar imagery is the time frequency plane which was discretized in to an NxN grid, which was a proposed technique of compressed sensing radar.Here we consider the value of k as small, which transmit sufficiently, which is used for the reconstruction of the scene. This novel compressed sensing approach offers great potential for better resolution over classical radar. Recently, the signal processing/ mathematics community has seen a paradigmatic shift in the way information is represented, stored, transmitted and recovered. This area is often referred to as Sparse Representations and Compressed Sensing. Current reconstruction methods include using greedy algorithms such as orthogonal matching pursuit (OMP). we can only consider recovering signals which are less than N-sparse. Indeed, we hope to recover any K-sparse signal s. our stylized compressed sensing radar which under appropriate conditions can “beat” the classical radar uncertainty principle By comparing traditional uncertainty principles with those of compressed sensing, this novel approach reveals great potential for better resolution over classical radar which provides a high resolution map of the spatial distribution of targets and terrain based on a significant reduction in the number of transmitted and/or received electromagnetic waveforms. This new imaging scheme, which requires no new hardware components, allows the aperture to be compressed and presents many important applications and advantages among which include resolving ambiguities, strong resistance to counter measures and interception, and reduced on-board storage constraints.},
        keywords = {Compressed sensing, sparse recovery, OMP},
        month = {},
        }