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Ground Penetrating Radar (GPR) Imaging is, and has been, a challenge to perfect on deeply buried targets. Developing a method to choose the best single frequency to image an object at depth has taken a back seat to developing better compositing techniques. Several methods have been explored to define a weighting structure to be applied to data taken at many frequencies to solve this problem. None of the methods have explored using an optimization problem solver. In this paper, we explore using the Expectation-Maximization Algorithm (EM), an optimization problem solver, to define the weights used to combine multiple frequency GPR scans of the same target area. Our method exploits the Gaussian Mixture Model feature of the EM algorithm, and shows promise as a measured improvement toward producing a sharper image at depth.

Extending Ground Penetrating Radar Imaging Capabilities Through Signal Processing