This enables fast notifications to staff if troublesome activities occur (such as falling) by the on-premise device, while the off-premise device captures activities missed or misclassified by the on-premise device. Next, a part of the calculation and the prediction is sent to a more capable off-premise machine (most likely in the cloud or a data center) where a backward RNN calculation is performed that improves the previous prediction sent by the on-premise device. First, a forward Recurrent Neural Network (RNN) calculation is performed on an on-premise device (usually close to the radar sensor) which already gives a prediction of what activity is performed, and can be used for time-sensitive use-cases. This work presents a framework that splits the processing of data in two parts. Often these networks are large and do not scale well when processing a large amount of radar streams at once, for example when monitoring multiple rooms in a hospital. This can be achieved by using Deep Neural Networks, which are able to effectively process the complex radar data. Comput.Radar systems can be used to perform human activity recognition in a privacy preserving manner. Junsheng, M., Youheng, T., Dongliang, X., Fangpei, Z., Xiaojun, J.: CNN and DCGAN for spectrum sensors over rayleigh fading channel. In: IEEE Journal on Selected Areas in Communications. ģ-D Deployment of UAV Swarm for Massive MIMO Communications. In: IEEE Wireless Communications Letters. Signal Processing 41(1) (1993)Īerial RIS-Assisted High Altitude Platform Communications. 25(10), 3301–3304 (2021)Ĭhandran, V., Elgar, S.L.: Pattern recognition using invariants defined from higher order spectra: one dimensional inputs. Junsheng, M., Gong, Y., Zhang, F., Cui, Y., Zheng, F., Jing, X.: Integrated sensing and communication-enabled predictive beamforming with deep learning in vehicular networks.
Kunming University of Science and Technology (2020) Yutao, H.: Radar Emitter Signal Recognition Based on Deep Learning and ambiguity function. Zhe, Z.: Radar Signal Recognition and Parameter Estimation Based on FRFT. Jiahuang, S., Jianchong, H., Yongcheng, Z.: Summary of rapid recognition of radar emitter signal. Journal of Physics: Conference Series, 1952 (2021) Jialu, L., Huaidong, S., Bin, Z.: Radar signal classification based on bayesian optimized support-vector machine. Experimental results show that radar signal classification based on bispectrum features and convolutional neural networks can effectively improve the effect of radar signal classification. Therefore, the images of the signal bispectrum after pre-processing and data enhancement can train convolutional neural networks to obtain deeper signal features. Aiming at the problem of low accuracy of radar signal classification in a low signal-to-noise ratio environment, a classification method based on bispectrum feature and convolutional neural network is proposed, it increases the accuracy of signal classification by taking advantage of bispectrum, which suppresses the Gaussian noise and retains the phase information. Radar signal classification is the key link in electronic information warfare, but as radar modulation becomes more sophisticated and the electromagnetic environment of the battlefield becomes complex, it is increasingly difficult to classify the radar signal.
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