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

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 306 ◽  
Author(s):  
Myeonggeun Oh ◽  
Yong-Hoon Kim
Keyword(s):  
Radio Frequency ◽  
Brightness Temperature ◽  
Thermal Emission ◽  
Microwave Radiometer ◽  
Threshold Level ◽  
Maximum Error ◽  
Symmetric Distribution ◽  
Radio Frequency Interference ◽  
Time Frequency ◽  
The Mean

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.

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

2018 ◽  
Author(s):  
Myeonggeun Oh ◽  
Yong-Hoon Kim
Keyword(s):  
Radio Frequency ◽  
Brightness Temperature ◽  
Thermal Emission ◽  
Microwave Radiometer ◽  
Threshold Level ◽  
Mean Value ◽  
Symmetric Distribution ◽  
Radio Frequency Interference ◽  
Time Frequency ◽  
The Mean

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 error in the retrieved brightness temperature is observed to be less than approximately 3 K from a window with a size of 100 × 100 time-frequency bins according to the RFI levels and cases.

scholarly journals Radio-frequency interference mitigating hyperspectral L-band radiometer

2017 ◽  
Vol 6 (1) ◽  
pp. 39-51 ◽  
Author(s):  
Peter Toose ◽  
Alexandre Roy ◽  
Frederick Solheim ◽  
Chris Derksen ◽  
Tom Watts ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Thermal Emission ◽  
Absolute Error ◽  
Radio Frequency Interference ◽  
Frequency Range ◽  
Passive Remote Sensing ◽  
Extended Frequency ◽  
L Band ◽  
Radiometric Signal ◽  
Microwave Radiometers

Abstract. Radio-frequency interference (RFI) can significantly contaminate the measured radiometric signal of current spaceborne L-band passive microwave radiometers. These spaceborne radiometers operate within the protected passive remote sensing and radio-astronomy frequency allocation of 1400–1427 MHz but nonetheless are still subjected to frequent RFI intrusions. We present a unique surface-based and airborne hyperspectral 385 channel, dual polarization, L-band Fourier transform, RFI-detecting radiometer designed with a frequency range from 1400 through  ≈  1550 MHz. The extended frequency range was intended to increase the likelihood of detecting adjacent RFI-free channels to increase the signal, and therefore the thermal resolution, of the radiometer instrument. The external instrument calibration uses three targets (sky, ambient, and warm), and validation from independent stability measurements shows a mean absolute error (MAE) of 1.0 K for ambient and warm targets and 1.5 K for sky. A simple but effective RFI removal method which exploits the large number of frequency channels is also described. This method separates the desired thermal emission from RFI intrusions and was evaluated with synthetic microwave spectra generated using a Monte Carlo approach and validated with surface-based and airborne experimental measurements.

scholarly journals Multiclass Radio Frequency Interference Detection and Suppression for SAR Based on the Single Shot MultiBox Detector

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4034 ◽  
Author(s):  
Junfei Yu ◽  
Jingwen Li ◽  
Bing Sun ◽  
Jie Chen ◽  
Chunsheng Li
Keyword(s):  
Radio Frequency ◽  
Pulse Compression ◽  
Radio Frequency Interference ◽  
Single Shot ◽  
Separation Problem ◽  
Underdetermined Blind Source Separation ◽  
Time Frequency ◽  
Interference Signals ◽  
Novel Method ◽  
Short Time

Radio frequency interference (RFI) is known to jam synthetic aperture radar (SAR) measurements, severely degrading the SAR imaging quality. The suppression of RFI in SAR echo signals is usually an underdetermined blind source separation problem. In this paper, we propose a novel method for multiclass RFI detection and suppression based on the single shot multibox detector (SSD). First, an echo-interference dataset is established by randomly combining the target signal with various types of RFI in a simulation, and the time–frequency form of the dataset is obtained by utilizing the short-time Fourier transform (STFT). Next, the time–frequency dataset acts as input data to train the SSD and obtain a network that is capable of detecting, identifying and estimating the interference. Finally, all of the interference signals are exactly reconstructed based on the prediction results of the SSD and mitigated by an adaptive filter. The proposed method can effectively increase the signal-to-interference-noise ratio (SINR) of RFI-contaminated SAR echoes and improve the peak sidelobe ratio (PSLR) after pulse compression. The simulated experimental results validate the effectiveness of the proposed method.

scholarly journals Deep learning improves identification of Radio Frequency Interference

2020 ◽  
Vol 499 (1) ◽  
pp. 379-390
Author(s):  
Alireza Vafaei Sadr ◽  
Bruce A Bassett ◽  
Nadeem Oozeer ◽  
Yabebal Fantaye ◽  
Chris Finlay
Keyword(s):  
Deep Learning ◽  
Radio Frequency ◽  
Transfer Learning ◽  
Radio Astronomy ◽  
Real Data ◽  
Radio Frequency Interference ◽  
Time Frequency ◽  
Amplitude Data ◽  
Important Challenge ◽  
Telescope Arrays

ABSTRACT Flagging of Radio Frequency Interference (RFI) in time–frequency visibility data is an increasingly important challenge in radio astronomy. We present R-Net, a deep convolutional ResNet architecture that significantly outperforms existing algorithms – including the default MeerKAT RFI flagger, and deep U-Net architectures – across all metrics including AUC, F1-score, and MCC. We demonstrate the robustness of this improvement on both single dish and interferometric simulations and, using transfer learning, on real data. Our R-Net model’s precision is approximately $90{{\ \rm per\ cent}}$ better than the current MeerKAT flagger at $80{{\ \rm per\ cent}}$ recall and has a 35 per cent higher F1-score with no additional performance cost. We further highlight the effectiveness of transfer learning from a model initially trained on simulated MeerKAT data and fine-tuned on real, human-flagged, KAT-7 data. Despite the wide differences in the nature of the two telescope arrays, the model achieves an AUC of 0.91, while the best model without transfer learning only reaches an AUC of 0.67. We consider the use of phase information in our models but find that without calibration the phase adds almost no extra information relative to amplitude data only. Our results strongly suggest that deep learning on simulations, boosted by transfer learning on real data, will likely play a key role in the future of RFI flagging of radio astronomy data.

scholarly journals High-Precision Temperature Inversion Algorithm for Correlative Microwave Radiometer

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5336
Author(s):  
Jie Liu ◽  
Kai Zhang ◽  
Jingyan Ma ◽  
Qiang Wu ◽  
Zhenlin Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Brightness Temperature ◽  
Mean Square Error ◽  
High Precision ◽  
Phase Error ◽  
Microwave Radiometer ◽  
Maximum Error ◽  
Temperature Inversion ◽  
Mean Square ◽  
Inversion Algorithm

In order to achieve high precision from non-contact temperature measurement, the hardware structure of a broadband correlative microwave radiometer, calibration algorithm, and temperature inversion algorithm are innovatively designed in this paper. The correlative radiometer is much more sensitive than a full power radiometer, but its accuracy is challenging to improve due to relatively large phase error. In this study, an error correction algorithm is designed, which reduces the phase error from 69.08° to 4.02°. Based on integral calibration on the microwave temperature measuring system with a known radiation source, the linear relationship between the output voltage and the brightness temperature of the object is obtained. Since the metal aluminum plate, antenna, and transmission line will have a non-linear influence on the receiver system, their temperature characteristics and the brightness temperature of the object are used as the inputs of the neural network to obtain a higher accuracy of inversion temperature. The temperature prediction mean square error of a back propagation (BP) neural network is 0.629 °C, and its maximum error is 3.351 °C. This paper innovatively proposed the high-precision PSO-LM-BP temperature inversion algorithm. According to the global search ability of the particle swarm optimization (PSO) algorithm, the initial weight of the network can be determined effectively, and the Levenberg–Marquardt (LM) algorithm makes use of the second derivative information, which has higher convergence accuracy and iteration efficiency. The mean square error of the PSO-LM-BP temperature inversion algorithm is 0.002 °C, and its maximum error is 0.209 °C.

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