Downward-Looking Linear Array 3D SAR Imaging Based on Multiple Measurement Vectors Model and Continuous Compressive Sensing

Abstract EN

This paper concerns the problems of huge data and off-grid effect of cross-track direction in downward-looking linear array (DLLA) 3D SAR imaging.

Since the 3D imaging needs a great deal of memory space, we consider the methods of downsampling to reduce the data quantity.

In the azimuth direction, we proposed a method based on the multiple measurement vectors (MMV) model, which can enhance computational efficiency and elevate the performance of antinoise, to recover the signal.

Further, in cross-track direction, since the resolution is restricted by the length of array, as well as platform size, the influence of off-grid effect is more serious than azimuth direction.

Continuous compressive sensing (CCS), which can solve the off-grid effect of the classical compressive sensing (CS), is presented to obtain the precise imaging result under the noise scenarios.

Finally, we validate our method by extension numerical experiments.