Abstract: Methods for mitigating sidelobes and aliases, providing levels of suppression in excess of 20 dB. The methods may include 1) a version of the CLEAN algorithm developed in radio astronomy, modified to work on sub-aperture images; 2) weighting functions based on the phase and amplitude statistics of the sub-aperture image pixels to select points in the CLEAN algorithm; and 3) weighting functions based on the phase and amplitude statistics of the sub-aperture image pixels to mitigate sidelobes and aliases, in conjunction with CLEAN or separately. The methods may be used with all synthetic aperture techniques and are not limited to SAR.

Abstract: Methods for mitigating sidelobes and aliases, providing levels of suppression in excess of 20 dB. The methods may include 1) a version of the CLEAN algorithm developed in radio astronomy, modified to work on sub-aperture images; 2) weighting functions based on the phase and amplitude statistics of the sub-aperture image pixels to select points in the CLEAN algorithm; and 3) weighting functions based on the phase and amplitude statistics of the sub-aperture image pixels to mitigate sidelobes and aliases, in conjunction with CLEAN or separately. The methods may be used with all synthetic aperture techniques and are not limited to SAR.

Abstract: Methods for mitigating sidelobes and aliases, providing levels of suppression in excess of 20 dB. The methods may include 1) a version of the CLEAN algorithm developed in radio astronomy, modified to work on sub-aperture images; 2) weighting functions based on the phase and amplitude statistics of the sub-aperture image pixels to select points in the CLEAN algorithm; and 3) weighting functions based on the phase and amplitude statistics of the sub-aperture image pixels to mitigate sidelobes and aliases, in conjunction with CLEAN or separately. The methods may be used with all synthetic aperture techniques and are not limited to SAR.

Abstract: A collected data is divided into M single valued subsets, where M is greater than zero. A two-dimensional subset image is formed from each single valued subset. Then, a fast Fourier transform is performed on each image to obtain a two-dimensional subset frequency space. Next, a one-dimensional discrete Fourier transform in z, where z is an integer equal to or greater than zero, is performed. Lastly, a two-dimensional discrete Fourier transform in (x,y) for each value of z is performed, thereby forming the three-dimensional volume from the collected data set.

Abstract: A collected data is divided into M single valued subsets, where M is greater than zero. A two-dimensional subset image is formed from each single valued subset. Then, a fast Fourier transform is performed on each image to obtain a two-dimensional subset frequency space. Next, a one-dimensional discrete Fourier transform in z, where z is an integer equal to or greater than zero, is performed. Lastly, a two-dimensional discrete Fourier transform in (x,y) for each value of z is performed, thereby forming the three-dimensional volume from the collected data set.