A Method of Nonlinearity Estimation and Correction Based on Difference Filtering

ZHAO Bo; ZHANG Yueyi; XU Yuanhong; LIU Xiaojun; FANG Guangyou

doi:10.11999/JEIT211193

Volume 44 Issue 4

Apr. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(4): 1295-1302

ZHAO Bo, ZHANG Yueyi, XU Yuanhong, LIU Xiaojun, FANG Guangyou. A Method of Nonlinearity Estimation and Correction Based on Difference Filtering[J]. Journal of Electronics & Information Technology, 2022, 44(4): 1295-1302. doi: 10.11999/JEIT211193

Citation:

ZHAO Bo, ZHANG Yueyi, XU Yuanhong, LIU Xiaojun, FANG Guangyou. A Method of Nonlinearity Estimation and Correction Based on Difference Filtering[J]. Journal of Electronics & Information Technology, 2022, 44(4): 1295-1302. doi: 10.11999/JEIT211193

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A Method of Nonlinearity Estimation and Correction Based on Difference Filtering

doi: 10.11999/JEIT211193

1.
Aerospace Information Research Institute, Chinese Academy of Science, Beijing 100094, China
2.
Key Laboratory of Electromagnetic Radiation and Sensing Technology, Chinese Academy of Sciences, Beijing 100190, China
3.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

Funds: The National Natural Science Foundation of China (41776204)

Received Date: 2021-10-29
Accepted Date: 2022-03-10
Rev Recd Date: 2022-03-08

Available Online: 2022-03-11

Publish Date: 2022-04-18

Abstract

Abstract

In the linear Frequency Modulation Continuous Waveform (FMCW) Radar system, due to the non-ideal characteristics of each instrument, the phase of the signal produces various distortions, which affects seriously the radar's ranging accuracy and imaging quality. It requires certain correction methods to obtain high-precision measurement results. In view of the characteristics of FMCW signal and deterministic nonlinear phase, this paper establishes a FMCW signal model subject to nonlinear interference, and proposes a new nonlinear estimation method based on difference filtering, which can simultaneously estimate periodic and non-periodic nonlinearities, and uses Matching Fourier Transform (MFT) method to correct the nonlinear phase. Simulation and comparative analysis show that this method has higher estimation accuracy than other methods, and it can also have a good correction effect when the nonlinearity is large. Finally, the actual measurement data of the radar is used to verify the effectiveness of the algorithm.

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References(18)

References

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