Traditional Synthetic Aperture Radar (SAR) platforms use narrow radar beams, forcing the user to choose between two image types: larger, low resolution images or smaller, high resolution images. SAR platforms also usually operate in a monostatic conf...
Traditional Synthetic Aperture Radar (SAR) platforms use narrow radar beams, forcing the user to choose between two image types: larger, low resolution images or smaller, high resolution images. SAR platforms also usually operate in a monostatic configuration, transmitting and receiving radar echoes from the same antenna.
Switching to a wide-angle multistatic approach dramatically improves SAR performance. The wide beam enables simultaneous high resolution image production over large ground swaths. The multistatic configuration provides additional data diversity and promotes platform survivability. Combining these two attributes results in an approach termed Wide-Angle Multistatic Synthetic Aperture Radar (WAM-SAR).
Unfortunately, WAM-SAR suffers from two significant implementation problems. First, wavefront curvature effects, non-linear flight paths, and warped ground planes lead to image defocusing with traditional SAR processing methods. A new 3-D monostatic/bistatic image formation routine solves the defocusing problem, correcting for all relevant wide-angle effects. This routine consists of a variable bistatic tomographic imaging algorithm with near-field and warped ground plane corrections. Inverse Synthetic Aperture Radar (ISAR) imagery produced using Radar Cross Section (RCS) chamber data validates this approach.
The second implementation problem stems from the large Doppler spread in the wide-angle scene, leading to severe aliasing problems. This research effort develops a new anti-aliasing technique using randomized Stepped-Frequency (SF) waveforms. The SAR imaging process coherently combines the individual waveform ambiguity functions, resulting in a |sinc|2 structure which places Doppler nulls at aliasing artifact locations. This approach does not increase the image formation algorithm's computational complexity. Both simulation and laboratory results demonstrate effective aliasing artifact mitigation, eliminating more than 99% of the aliased energy.