Continuous Wave Radio Frequency Interference Research Articles

Radio Frequency Interference Detection for SMAP Radiometer Using Convolutional Neural Networks

Passive remote sensing is a crucial technology for climate studies and Earth science. NASA's soil moisture active passive (SMAP) is a remote sensing observatory that uses passive microwave radiometer measurements to estimate soil moisture and detect the freeze or thaw state. Despite operating in the protected band of the radio spectrum (1400-1427 MHz), the radiometer's measurements are nonetheless tainted by radio frequency interference (RFI). An increasing number of radio-frequency transmissions such as those from air surveillance radars, 5G wireless communications, and unmanned aerial vehicles are contributing to RFI through either out-of-band emissions or operating in-band illegally. Physical modeling to detect RFI globally might prove to be challenging as RFI can be generated from single as well as multiple sources and these can be divided as pulsed or continuous wave RFI. In this study, a deep learning (DL) based RFI detection method is proposed with a novel convolutional neural network framework that can detect different types of RFI on a global scale. This is a data-driven approach where the detection framework learns directly from the SMAP data products to make a decision whether a certain footprint is RFI contaminated or not. SMAP's level 1A data products containing antenna counts of different raw moments along with Stokes parameters are used in this study to produce spectrograms and level 1B data products containing the quality flags are used to dynamically label those spectrograms. This study's robust DL framework provided the highest accuracy with the raw moments of horizontal polarization (99.99%) to detect RFI globally.

Analysis of RFI Issue Using the CAROLS L-Band Experiment

In this paper, different methods are proposed for the detection and mitigation of the undesirable effects of radio-frequency interference (RFI) in microwave radiometry. The first of these makes use of kurtosis to detect the presence of non-Gaussian signals, whereas the second imposes a threshold on the standard deviation of brightness temperatures in order to distinguish natural-emission variations from RFI. Finally, the third approach is based on the use of a threshold applied to the third and fourth Stokes parameters. All these methods have been applied and tested, with the cooperative airborne radiometer for ocean and land studies radiometer operating in the L-band, on the data acquired during airborne campaigns made in the spring of 2009 over the southwest of France. The performance of each approach, or of two combined approaches, is analyzed with our database. We thus show that the kurtosis method is well suited to detect pulsed RFI, whereas the method based on the second moment of brightness temperatures seems to be better suited to detect continuous-wave RFI in airborne brightness-temperature measurements.

Investigation on RFI effects in bandgap voltage references

In this paper the susceptibility of integrated bandgap voltage references to Radio Frequency Interference (RFI) is investigated by on-chip measurements carried out on Kuijk and Tsividis bandgap circuits. These measurements highlight the offset in the reference voltage induced by continuous wave RFI and the complete failures which may be experienced by bandgap circuits. The role of the susceptibility of the startup circuit and of the operational amplifier which are included in such circuits is also focused.

Performance degradation of pseudonoise code lock detector due to radio frequency interference

This paper computes the probabilities of detection and false alarm for a pseudonoise (PN) sequence code lock detector when a strong radio frequency interference (RFI) hits the communications link. A soft-limiter, a hard-limiter, and no limiter at the front-end of the receiver are considered. Both continuous wave (CW) type RFI and pulse type RFI are analyzed. It is shown that a soft-limiter or a hard-limiter can protect the PN-code lock detector against a typical pulse RFI, but in a CW RFI environment, the PN-code lock detector performance with a soft-limiter or a hard-limiter will be worse than that with no limiter. >