In this paper, we consider automatic radar pulse detection and intra-pulse modulation classification for cognitive electronic warfare applications. In this manner, we introduce an end-to-end framework for detection and classification of radar pulses. Our approach is complete, i.e., we provide raw radar signal at the input side and produce categorical output at the output. We use short time Fourier transform to obtain timefrequency image of the signal. Hough transform is used to detect pulses in time-frequency images and pulses are represented with a single line. Then, convolutional neural networks are used for pulse classification. In experiments, we provide classification results at different SNR levels.
Abstract-Near-field ultrawideband imaging is a promising remote sensing technique in various applications such as airport security, surveillance, medical diagnosis, and through-wall imaging. Recently, there has been increasing interest in using sparse multiple-input-multiple-output (MIMO) arrays to reduce hardware complexity and cost. In this paper, based on a Bayesian estimation framework, an optimal design method is presented for two-dimensional MIMO arrays in ultrawideband imaging. The optimality criterion is defined based on the image reconstruction quality obtained with the design, and the optimization is performed over all possible locations of antenna elements using an algorithm called clustered sequential backward selection algorithm. The designs obtained with this approach are compared with that of some commonly used sparse array configurations in terms of image reconstruction quality for various noise levels.
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