TY - GEN
T1 - Signal Frequency Estimation Based on RNN
AU - Huang, Bin
AU - Lin, Chun Liang
AU - Chen, Weihai
AU - Juang, Chia Feng
AU - Wu, Xingming
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/8
Y1 - 2020/8
N2 - Signal frequency estimation is a fundamental issue in the domain of signal processing. In this paper, we proposed a novel framework, named FreqEnet (Frequency estimation network), for estimating frequency based on deep learning method. The signal frequency estimation refers to as a regression issue and predict it with LTSM module. The framework is exceedingly concise, consisted of only three LSTM and one fully connect layers, and the running time is less than 0.3 ms on CPU (i7-7700, 3.60 GHz). Two periodic signals are generated for training our model. In addition, uniform and Gauss white noise are introduce to original signal for evaluating the robustness and generalization of the framework. In addition, the proposed method performs extremely excellence in processing latent. Even if given only one periodic piece of signal, the method could predicts a precise result. Extensive experiments demonstrate that FreqEnet achieves competitive performance of estimating frequency.
AB - Signal frequency estimation is a fundamental issue in the domain of signal processing. In this paper, we proposed a novel framework, named FreqEnet (Frequency estimation network), for estimating frequency based on deep learning method. The signal frequency estimation refers to as a regression issue and predict it with LTSM module. The framework is exceedingly concise, consisted of only three LSTM and one fully connect layers, and the running time is less than 0.3 ms on CPU (i7-7700, 3.60 GHz). Two periodic signals are generated for training our model. In addition, uniform and Gauss white noise are introduce to original signal for evaluating the robustness and generalization of the framework. In addition, the proposed method performs extremely excellence in processing latent. Even if given only one periodic piece of signal, the method could predicts a precise result. Extensive experiments demonstrate that FreqEnet achieves competitive performance of estimating frequency.
KW - Frequency Estimation
KW - LSTM
KW - RNN
KW - Signal Processing
UR - https://www.scopus.com/pages/publications/85091589368
U2 - 10.1109/CCDC49329.2020.9164504
DO - 10.1109/CCDC49329.2020.9164504
M3 - 会议稿件
AN - SCOPUS:85091589368
T3 - Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020
SP - 2030
EP - 2034
BT - Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 32nd Chinese Control and Decision Conference, CCDC 2020
Y2 - 22 August 2020 through 24 August 2020
ER -