Efficient High Resolution 3D SAR Imaging via Super-resolution Spectral Estimation Methods

The volumetric scattering of radar waves allows for three-dimensional SAR imaging of a scene. As is the case in two-dimensions, traditional Fourier methods are easier to implement, but have limitations in terms of quality with respect to speckle, scintillation, and side lobe artifacts. Other methods that have shown promise, such as superresolution methods like the Minimum Variance Method (MVM) and the Multiple Signal Classification (MUSIC) algorithm, have been shown to produce high quality images. However, these algorithms are computationally intense as they require the estimation of a correlation matrix, and manipulations thereof, as well as computing the image spectrum through computation of a quadratic form for each image pixel. This paper presents an efficient method for computing these superresolution techniques for 3D SAR images.