Analysis of RFI Identification and Mitigation in CAROLS Radiometer Data Using a Hardware Spectrum Analyser

A method to identify and mitigate radio frequency interference (RFI) in microwave radiometry based on the use of a spectrum analyzer has been developed. This method has been tested with CAROLS L-band airborne radiometer data that are strongly corrupted by RFI. RFI is a major limiting factor in passive microwave remote sensing interpretation. Although the 1.400–1.427 GHz bandwidth is protected, RFI sources close to these frequencies are still capable of corrupting radiometric measurements. In order to reduce the detrimental effects of RFI on brightness temperature measurements, a new spectrum analyzer has been added to the CAROLS radiometer system. A post processing algorithm is proposed, based on selective filters within the useful bandwidth divided into sub-bands. Two discriminant analyses based on the computation of kurtosis and Euclidian distances have been compared evaluated and validated in order to accurately separate the RF interference from natural signals

Radio-frequency interference mitigating hyperspectral L-band radiometer

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

Radio frequency interference in microwave radiometry: statistical analysis and study of techniques for detection and mitigation

Microwave radiometry field has been increasing its performance with higher accuracy measurements, leading to a more presence in the remote sensing field. Several space-borne, air-borne and ground-based radiometers have been developed to perform measurement campaigns; however, the actual sensitivity of a radiometer is often limited by man-made radio emissions such as radars, broadcasting emissions, wireless communications and many other communication systems based on electromagnetic waves, limiting the improvement in the radiometers¿ performance. Consequently, in order to maintain the accuracy in the radiometric measurements, it has been researched in the Radio Frequency Interference (RFI) detection and mitigation systems and algorithms for the microwave radiometry field. The scope of this doctoral thesis is the development and testing of RFI detection and mitigation algorithms in order to enhance radiometric measurements performed by the Multifequency Experimental Radiometer with Interference Tracking for Experiments over Land and Littoral (MERITXELL). The MERITXELL has been developed during this thesis with the idea studying the RFI present in several radiometric bands and the way to mitigate it, as well as to obtain data from diverse frequency bands and devices in only one measurement campaign

Analysis of RFI identification and mitigation in CAROLS radiometer data using a hardware spectrum analyser

A method to identify and mitigate radio frequency interference (RFI) in microwave radiometry based on the use of a spectrum analyzer has been developed. This method has been tested with CAROLS L-band airborne radiometer data that are strongly corrupted by RFI. RFI is a major limiting factor in passive microwave remote sensing interpretation. Although the 1.400-1.427 GHz bandwidth is protected, RFI sources close to these frequencies are still capable of corrupting radiometric measurements. In order to reduce the detrimental effects of RFI on brightness temperature measurements, a new spectrum analyzer has been added to the CAROLS radiometer system. A post processing algorithm is proposed, based on selective filters within the useful bandwidth divided into sub-bands. Two discriminant analyses based on the computation of kurtosis and Euclidian distances have been compared evaluated and validated in order to accurately separate the RF interference from natural signals