A multi-channel phase calibration method based on deep neural network

Zhang Yuxin; Yao Xin; Zhang Yilong

doi:10.1117/12.2631453

1 May 2022 A multi-channel phase calibration method based on deep neural network

Zhang Yuxin, Yao Xin, Zhang Yilong

Proceedings Volume 12171, Thirteenth International Conference on Signal Processing Systems (ICSPS 2021); 121710J (2022) https://doi.org/10.1117/12.2631453
Event: Thirteenth International Conference on Signal Processing Systems (ICSPS 2021), 2021, Shanghai, China

Abstract

In order to address problem about the channel phase error, a channel phase calibration method based on deep learning is proposed. Using data mining to replace the traditional method can not only improve the flexibility and stability of the method, but also achieve better results. Firstly, we use the frequency response function to model the channel characteristics, and the channel mismatch model is established to simulate the errors of the channel. Secondly, the error generated by the channel is introduced into the signal to generate the analog data set. Through the training and fitting, we achieved the all-phase calibration. At the same time, a variety of different channel parameters are simulated, and the generalization ability of different channel parameters get verified. Finally, the model network is evaluated in the form of test standard deviation. According to the results, the standard deviation can be controlled within 3°, which proves the effectiveness of this method. In this paper, Octave was used to generate the simulated data set for preprocessing, PyCharm platform was used to build the neural network, and the model was trained based on TensorFlow.

Citation Download Citation

Zhang Yuxin, Yao Xin, and Zhang Yilong "A multi-channel phase calibration method based on deep neural network", Proc. SPIE 12171, Thirteenth International Conference on Signal Processing Systems (ICSPS 2021), 121710J (1 May 2022); https://doi.org/10.1117/12.2631453

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available