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17 July 1998 Survey of radar superresolution methods with applications to automatic target recognition
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Abstract
Resolution is a fundamental limitation of any processing based on radar data. Conventional radar imaging techniques, in general, make use of the FFT to determine the spatial location of a target from its scattered field. The resolution of these images is limited by the bandwidth of the interrogating radar system and the aspect angle sector over which the target is observed. In such cases, superresolution offers the potential to improve system performance by increasing the resolution. Superresolution is the process of increasing the effective bandwidth of an image (or time series) by introducing collateral data to augment the dataset; thus the Rayleigh resolution imposed by the size of the dataset is overcome by the introduction of the synthetic collateral data. This paper presents a state of the art survey of radar superresolution, applicable to both 1-D and 2-D data and to both the discrete and distributed cases. It presents a comparison superresolution algorithms using real and simulated data sets. It also presents specific applications of superresolution for air-to-ground surveillance, data resolution enhancement, SAR ATR and FOPEN ATR.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Raman K. Mehra, Ravi B. Ravichandran, and Melvyn Huff "Survey of radar superresolution methods with applications to automatic target recognition", Proc. SPIE 3374, Signal Processing, Sensor Fusion, and Target Recognition VII, (17 July 1998); https://doi.org/10.1117/12.327096
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