While convolutional neural networks (CNNs) are powerful tools in machine learning, their construction is far from a science. In addition, instantiations of CNNs are highly memory expensive and typically require large training sets. Wavelet scattering networks (WSNs) could provide a simple means of testing quantization schemes for CNNs, without the added complexity of adjustable parameters. Using the MSTAR database, the performance of a WSN in combination with several quantization schemes is examined.
Much work has been done designing transmit waveforms for target identification, classification, and detection. In addition, these have also been studied in both single and multiple-antenna scenarios. In this work, we study the construction of a waveform when multiple radar sensors are used to image a target scene. The scene is assumed to have a prior distribution given by a Compound Gaussian (CG) - a model that has proven very useful in the field of image processing. Waveform optimization is done with the objective of optimizing mutual information, while reconstruction was performed using sparsity based reconstruction techniques. In our work, the waveform is tailored for a particular target of interest in the scene while suppressing the clutter. Using our waveform techniques, we demonstrate statistically significant improvements in the quality of the reconstructed image in peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM). We validate our algorithms using the MSTAR database.
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