A Method for Radio Frequency Interference Space Angle Sparse Bayesian Estimating in Synthetic Aperture Microwave Radiometer

ZHANG Juan; ZHUANG Lehui; LI Yinan; LI Hong; DOU Haofeng

doi:10.11999/JEIT231367

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ZHANG Juan, ZHUANG Lehui, LI Yinan, LI Hong, DOU Haofeng. A Method for Radio Frequency Interference Space Angle Sparse Bayesian Estimating in Synthetic Aperture Microwave Radiometer[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231367

Citation:

ZHANG Juan, ZHUANG Lehui, LI Yinan, LI Hong, DOU Haofeng. A Method for Radio Frequency Interference Space Angle Sparse Bayesian Estimating in Synthetic Aperture Microwave Radiometer[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231367

Citation:

ZHANG Juan, ZHUANG Lehui, LI Yinan, LI Hong, DOU Haofeng. A Method for Radio Frequency Interference Space Angle Sparse Bayesian Estimating in Synthetic Aperture Microwave Radiometer[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231367

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A Method for Radio Frequency Interference Space Angle Sparse Bayesian Estimating in Synthetic Aperture Microwave Radiometer

doi: 10.11999/JEIT231367

1.
National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
2.
China Academy of Space Technology (Xi’an), Xi’an 710100, China

Funds: The Laboratory Stabilization Support Program Project (HTKJ2022KL504015)

Received Date: 2023-12-11
Rev Recd Date: 2024-03-14

Available Online: 2024-03-29

Abstract

Abstract

A sparse Bayesian estimation for spatial Radio Frequency Interference (RFI) of synthetic aperture microwave radiometers is proposed in this paper. Firstly, an interferometry measurement model of the visibility function for synthetic aperture microwave radiometers is established. The observed data are expressed as the product of the observation matrix of the aperture synthesis antenna baseline correlation steering vector and the brightness temperature of the field of view. Due to the orthogonality of the observation matrix and the sparsity of the RFI spatial angle distribution, the transformation coefficients of brightness temperature in the support domain are sparse. Under the Sparse Bayesian Learning (SBL) framework, brightness temperature is sparsely reconstructed. Notably, this method can obtain high reconstruction performance without the prior information of sparsity and regularization parameters. The effectiveness of this method is verified through computer simulations.
- Synthetic aperture microwave radiometers,
- Radio Frequency Interference(RFI),
- Sparse Bayesian,
- Spatial angle estimation.

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References(19)

References

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