4,535 research outputs found
Convolutional Radio Modulation Recognition Networks
We study the adaptation of convolutional neural networks to the complex
temporal radio signal domain. We compare the efficacy of radio modulation
classification using naively learned features against using expert features
which are widely used in the field today and we show significant performance
improvements. We show that blind temporal learning on large and densely encoded
time series using deep convolutional neural networks is viable and a strong
candidate approach for this task especially at low signal to noise ratio
Practical classification of different moving targets using automotive radar and deep neural networks
In this work, the authors present results for classification of different classes of targets (car, single and multiple people, bicycle) using automotive radar data and different neural networks. A fast implementation of radar algorithms for detection, tracking, and micro-Doppler extraction is proposed in conjunction with the automotive radar transceiver TEF810X and microcontroller unit SR32R274 manufactured by NXP Semiconductors. Three different types of neural networks are considered, namely a classic convolutional network, a residual network, and a combination of convolutional and recurrent network, for different classification problems across the four classes of targets recorded. Considerable accuracy (close to 100% in some cases) and low latency of the radar pre-processing prior to classification (∼0.55 s to produce a 0.5 s long spectrogram) are demonstrated in this study, and possible shortcomings and outstanding issues are discussed
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