[O10] SAR processing with zeros
Synthetic Aperture Radar (SAR) provides the military with an extremely valuable means of remote imaging and plays an important role in target detection. SAR works by measuring the electromagnetic signal reflections from the ground. Processing the raw received data to generate the image is usually performed using linear frequency domain techniqes such as the Polar Format Algorithm. However when there is missing data, or when gaps in the data (either spatially or spectrally) are introduced the performance of such estimators deteriorates considerably. The resulting images exhibit distortion and aliasing artefacts.
Recently a new theory for signal reconstruction, called compressed sensing, has emerged. It explores the extent to which ill-posed sampling problems such as those discussed above can be made well-posed through the inclusion of strong signal models. These techniques have already been successfully applied to SAR image reconstruction for target detection and super resolution. The broad aim of this proposal is to explore the application of compressed sensing reconstruction techniques to SAR image formation when spatial and/or frequency notches are introduced into the transmitted/received signals. Ultimately we hope to gain a general understanding of the limits to which the SAR data acquisition system can be so modified without incurring serious performance degradation.
Project Supervisor
Mike Davies received the B.A. (Hons.) degree in Engineering from Cambridge University, Cambridge, U.K., in 1989 and the Ph.D. degree in nonlinear dynamics and signal processing from University College London, London (UCL), U.K., in 1993. Mike Davies was awarded a Royal Society Research Fellowship in 1993. He currently holds the Jeffrey Collins SFC funded chair in Signal and Image Processing at the University of Edinburgh and is the Director of the Joint Institute in Signal and Image Processing, part of the Edinburgh Research Partnership. He is currently pursuing a programme of research in the application of sparse representations to signal processing. Most recently his research has concentrated on the emerging field of compressed sensing.
