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Real radar returns from four small scale commercial aircraft models are used to train and test a convolutional neural network target recognition system. Many target recognition systems convert the one dimensional stepped-frequency features into two-dimensional using tools such as spectrograms and scalograms, and thereby utilize a two-dimensional CNN. In this paper, a one-dimensional convolutional neural net is used. The unknown target’s azimuth position may be known completely or within a certain range. The recognition performance is compared with that of an optimal Bayesian classifier assuming complete statistical knowledge. A discussion of the advantages and disadvantages of using 1D-CNN is presented.
I. Jouny
"Stepped frequency radar target recognition using 1D-CNN", Proc. SPIE 12096, Automatic Target Recognition XXXII, 120960C (31 May 2022); https://doi.org/10.1117/12.2618613
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I. Jouny, "Stepped frequency radar target recognition using 1D-CNN," Proc. SPIE 12096, Automatic Target Recognition XXXII, 120960C (31 May 2022); https://doi.org/10.1117/12.2618613