Unsupervised constrained radar imaging of low resolution targets |
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| Authors: | Andrey Semichaevsky Markus E. Testorf Robert V. McGahan Michael A. Fiddy |
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| Affiliation: | a Department of Electrical and Computer Engineering, University of Massachusetts-Lowell, Lowell, MA, USAb Thayer School of Engineering, Dartmouth College, Hanover, NH, USAc Air Force Research Laboratory AFRL/SNH, Hanscom AFB, MA, USAd Center for Optoelectronics and Optical Communications, University of North Carolina at Charlotte, Charlotte, NC, USA |
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| Abstract: | A linear spectral estimation technique, the PDFT algorithm, is used as part of a nonlinear iterative reconstruction scheme to obtain improved radar images. The iterative PDFT algorithm is used to address the limited resolution problem inherent to imaging objects buried in soil and hidden under foliage. This is achieved by subsequent application of two properties of the PDFT algorithm: the energy parameter of the PDFT algorithm is used to determine the target shape, while the shape information in turn is used to obtain super-resolved images. We describe algorithms able to exploit both properties automatically and without manual intervention. Two methods are investigated in particular, one iteratively optimizing the constraints by monitoring the energy parameter, the other method computing energy values for all points, from which a weighted prior function is determined. In addition, we discuss variants of both algorithm which provide an optimized trade-off between computation time and performance. Additional attention is given to situations, where a known target is embedded in an unknown background. Imaging results are presented for both synthetic and measured data. |
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