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Waveform Recognition in Multipath Fading Using Autoencoder and CNN with Fourier Synchrosqueezing Transform
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A4 Artikkeli konferenssijulkaisussa
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en
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6
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2020 IEEE International Radar Conference, RADAR 2020, pp. 612-617
Abstract
In this paper the problem of recognizing radar waveforms is addressed for multipath fading channels. Waveform classification is needed in spectrum sharing, radar-communications coexistence, cognitive radars, spectrum monitoring and signal intelligence. Different radar waveforms exhibit different properties in time-frequency domain. We propose a deep learning method for waveform classification. The received signal is first equalized to mitigate the effect of multipath fading channels by using a denoising auto-encoder (DAE). Then, the equalized signal is processed with Fourier synchrosqueezing transform that has excellent properties in revealing time-varying behavior, rate of, strength and number of oscillatory components in signals. The resulting time-frequency description is represented as a bivariate image that is fed into a convolutional neural network. The proposed method has superior performance over the widely used the Choi-Williams distribution (CWD) method in distinguishing among different radar waveforms even at low signal-to-noise ratio regime.
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Kong, G, Jung, M & Koivunen, V 2020, Waveform Recognition in Multipath Fading Using Autoencoder and CNN with Fourier Synchrosqueezing Transform. in 2020 IEEE International Radar Conference, RADAR 2020., 09114783, IEEE, pp. 612-617, IEEE Radar Conference, Washington, District of Columbia, United States, 28/04/2020. https://doi.org/10.1109/RADAR42522.2020.9114783