Abstract
Synthetic aperture radar based on the matched filter theory has the ability of obtaining two-di- mensional image of the scattering areas. Nevertheless, the resolution and sidelobe level of SAR imaging is limited by the antenna length and bandwidth of transmitted signal. However, for sparse signals (direct or indirect), sparse imaging methods can break through limitations of the conventional SAR methods. In this paper, we introduce the basic theory of sparse representation and reconstruction, and then analyze several common sparse imaging algorithms: the greed algorithm, the convex optimization algorithm. We apply some of these algorithms into SAR imaging using RadBasedata. The results show the presented method based on sparse construction theory outperforms the conventional SAR method based on MF theory.
Highlights
Due to the properties of all weather and day-night imaging [1], Synthetic aperture radar (SAR) plays an important role in military and civilian applications, such as earth observation, topographic mapping, target recognition, remote sensing and flight navigation etc [2]-[4]
In order to explain what kind of method is more accurate, we apply the conventional method and sparse construction method into SAR imaging
The conventional matched filter (MF) method and the sparse construction method are compared in the same situation
Summary
Due to the properties of all weather and day-night imaging [1], Synthetic aperture radar (SAR) plays an important role in military and civilian applications, such as earth observation, topographic mapping, target recognition, remote sensing and flight navigation etc [2]-[4]. AS a result, the resolution of the synthetic aperture radar through the conventional SAR imaging algorithm is limited by the bandwidth of the original transmitted signal and the antenna length. The imaging process of SAR based on CS theory can be that firstly using matched filter method to complete image construction and lastly removing the convolution kernel based on sparse characteristics of scattering coefficients of the target in the scene. The number of dominant scatters is much smaller than the total size of observed scene In such a situation, SAR raw echo can be regarded as a sparse signal. We propose some directions to discover in the following days
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