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Statistical Approach to Spectrogram Analysis for Radio-Frequency Interference Detection and Mitigation in an L-Band
Myeonggeun Oh1, Yong-Hoon Kim2
1School of Mechatronics, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, Korea. mgoh1815@gmail.com.
Sensors (Basel, Switzerland)
|January 17, 2019
Summary
This study introduces a novel spectrogram technique to detect and mitigate radio-frequency interference (RFI) in passive microwave radiometers. The method accurately retrieves brightness temperature, minimizing errors caused by RFI contamination.
Area of Science:
- Remote Sensing
- Radiometry
- Signal Processing
Background:
- Passive microwave radiometers are crucial for Earth observation.
- Radio-frequency interference (RFI) significantly contaminates brightness temperature data.
- Existing RFI thresholding methods can lead to retrieval errors.
Purpose of the Study:
- To develop and validate a new method for detecting and mitigating RFI in passive microwave radiometer data.
- To improve the accuracy of retrieved brightness temperatures in the presence of RFI.
Main Methods:
- A spectrogram technique utilizing statistical criteria (mean, skewness, kurtosis) is proposed.
- Mean and skewness spectrograms are generated to differentiate thermal emissions from RFI.
- Kurtosis is calculated on symmetric distributions to refine brightness temperature retrieval.
Main Results:
- The proposed method effectively detects and mitigates RFI contamination.
- Experimental data show maximum and root-mean-square errors below 3 K and 1.7 K, respectively.
- Performance is robust across various RFI levels and cases.
Conclusions:
- The spectrogram-based statistical method offers a significant improvement for RFI mitigation in passive microwave radiometry.
- Accurate brightness temperature retrieval is achievable even with RFI presence.
- This technique enhances the reliability of data for Earth observation applications.
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