An Efficient Gain Estimation in the Calibration of Noise-Adding Total Power Radiometers for Radiometric Resolution Improvement

Bonafoni, Stefania; Alimenti, F.; Roselli, L.

doi:10.1109/tgrs.2018.2812904

Cited by 4 publications

(1 citation statement)

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“…Specifically, the effect on the RFI adjacent to the land is critical for the ground-based microwave radiometer for remote sensing outdoor application when compared with the measurements from the open ocean using airborne and satellite [3,4,5,6]. Moreover, RFI at a higher frequency such as at the C- [7], X- [5,6,7,8], and Ku-bands [9] can arise in the microwave radiometry field. Recently, ground-based microwave radiometers have been used for Internet of Things (IoT) applications [10] and for effective detection and mitigation of RFI contamination.…”

Section: Introductionmentioning

confidence: 99%

Statistical Approach to Spectrogram Analysis for Radio-Frequency Interference Detection and Mitigation in an L-Band Microwave Radiometer

Kim

2019

Sensors

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For the elimination of radio-frequency interference (RFI) in a passive microwave radiometer, the threshold level is generally calculated from the mean value and standard deviation. However, a serious problem that can arise is an error in the retrieved brightness temperature from a higher threshold level owing to the presence of RFI. In this paper, we propose a method to detect and mitigate RFI contamination using the threshold level from statistical criteria based on a spectrogram technique. Mean and skewness spectrograms are created from a brightness temperature spectrogram by shifting the 2-D window to discriminate the form of the symmetric distribution as a natural thermal emission signal. From the remaining bins of the mean spectrogram eliminated by RFI-flagged bins in the skewness spectrogram for data captured at 0.1-s intervals, two distribution sides are identically created from the left side of the distribution by changing the standard position of the distribution. Simultaneously, kurtosis calculations from these bins for each symmetric distribution are repeatedly performed to determine the retrieved brightness temperature corresponding to the closest kurtosis value of three. The performance is evaluated using experimental data, and the maximum error and root-mean-square error (RMSE) in the retrieved brightness temperature are served to be less than approximately 3 K and 1.7 K, respectively, from a window with a size of 100 × 100 time–frequency bins according to the RFI levels and cases.

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