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.

Variable Speed Estimation for Wheel-Bearings with STFT and BP Neural Network

2014 ◽  
Vol 945-949 ◽  
pp. 1112-1115
Author(s):  
Yuan Zhou ◽  
Bin Chen ◽  
Bao Cheng Gao ◽  
Si Jie Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Background Noise ◽  
Speed Estimation ◽  
Variable Speed ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Novel Method ◽  
Instantaneous Frequencies ◽  
Short Time

For the variable speed estimation of wheel-bearings in strong background noise, a novel method with the short-time Fourier transform and BP neural network (STFT-BPNN) is proposed. In the method, it calculates the time-frequency spectrum with STFT technique. Then the instantaneous frequency is estimated by peak detection. Taking the instantaneous frequencies as the input vectors, the BP neural network is trained to fit the discrete instantaneous frequencies. The effectiveness of proposed method is demonstrated by simulation. Experimental results show that proposed method provides better performance on variable speed estimation for wheel-bearings.

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 A Novel Method of Temporomandibular Joint Hypermobility Diagnosis Based on Signal Analysis

2021 ◽  
Vol 10 (21) ◽  
pp. 5145
Author(s):  
Justyna Grochala ◽  
Dominik Grochala ◽  
Marcin Kajor ◽  
Joanna Iwaniec ◽  
Jolanta E. Loster ◽  
...  
Keyword(s):  
Temporomandibular Joint ◽  
Joint Hypermobility ◽  
Joint Movement ◽  
Electronic Stethoscope ◽  
Time Frequency ◽  
Novel Method ◽  
Short Time ◽  
Frequency Features ◽  
Digital Time ◽  
Comprehensive Study

Despite the temporomandibular joint (TMJ) being a well-known anatomical structure its diagnosis may become difficult because physiological sounds accompanying joint movement can falsely indicate pathological symptoms. One example of such a situation is temporomandibular joint hypermobility (TMJH), which still requires comprehensive study. The commonly used official research diagnostic criteria for temporomandibular disorders (RDC/TMD) does not support the recognition of TMJH. Therefore, in this paper the authors propose a novel diagnostic method of TMJH based on the digital time–frequency analysis of sounds generated by TMJ. Forty-seven volunteers were diagnosed using the RDC/TMD questionnaire and auscultated with the Littmann 3200 electronic stethoscope on both sides of the head simultaneously. Recorded TMJ sounds were transferred to the computer via Bluetooth® for numerical analysis. The representation of the signals in the time–frequency domain was computed with the use of the Python Numpy and Matplotlib libraries and short-time Fourier transform. The research reveals characteristic time–frequency features in acoustic signals which can be used to detect TMJH. It is also proved that TMJH is a rare disorder; however, its prevalence at the level of around 4% is still significant.

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 Performance Evaluation of Radio Frequency Interference Measurements from Microwave Links in Dense Urban Cities

2021 ◽  
Author(s):  
Michael Adedosu Adelabu ◽  
Agbotiname Lucky Imoize ◽  
Glory Uzuazobona Ughegbe
Keyword(s):  
Fourier Transform ◽  
Performance Evaluation ◽  
Radio Frequency ◽  
Statistical Error ◽  
Noise Removal ◽  
Cumulative Distribution ◽  
Radio Frequency Interference ◽  
Short Time Fourier Transform ◽  
Frequency Scan ◽  
Short Time

Radio frequency interference (RFI) constitutes a significant problem in achieving a good quality of service in radio links. Several techniques have been proposed to identify and mitigate RFI in wireless networks. However, most of these techniques are not generalized for all propagation environments due to their varying geographical features. The need for extensive frequency scan measurements on the links to identify the available channels, evaluate the performances of the links, and detect RFI in the channels becomes imperative. In this study, performance evaluation of frequency scan measurements from active microwave links comprising eighteen base stations is presented. The measurements equipment comprises a spectrum analyzer and a 0.6-meter antenna dish. The frequency scans were taken at 6GHz, 7GHz, and 8GHz with full azimuth coverage of the horizontal and vertical polarization. Measured data were processed to determine the available frequencies and RFI in the channels. The histogram and probability density function of the frequency scans were computed. The cumulative distribution functions were determined, and the statistical error characteristics of the frequency scans for the estimated normal distribution and the estimated fitness curve were derived. The short-time Fourier transform of the noisy signal was obtained, and the signal without noise was recovered using the inverse short-time Fourier transform. Analysis of the scanned signals before and after the noise removal is demonstrated. The denoised signals compare favorably with related results in the preliminary literature. Overall, the frequency scans would be highly useful in evaluating RFI measurements and spectrum planning.

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