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This paper describes the spectral kurtosis detector as an additional indicator for radio frequency interference, RFI in passive remote sensing systems. The estimator is based on continuous Fast Fourier Transformation of samples, followed by evaluation of each frequency bin in subsequent data blocks, using a kurtosis estimator. The spectral kurtosis response is simulated and tested in a laboratory setup for various RFI types, and the estimator response is evaluated and compared to the response of the traditional time domain kurtosis estimator. Results show that great benefits of spectral kurtosis is reached for continuous wave RFI or for RFI with high duty-cycle compared to the radiometer integration time. Typically the spectral kurtosis is superior for duty-cycles above 15%, while standard kurtosis is more efficient for lower duty-cycles, down to a few percent. In combination, the two estimators provide a very good indicator for most RFI types. [ABSTRACT FROM PUBLISHER]

Man-made Radio Frequency Interference (RFI) is an increasingly threatening problem for passive microwave radiometry from space. The problem is presently very evident in L-band data from SMOS, but it is realized that it is already now a problem at other traditional radiometer bands at C, X, and Ku bands. Studies of data from existing radiometer systems have revealed this, and clearly indicates increasing RFI intensity over time. Thus, future missions have to take this into consideration, and dedicated hardware and algorithms to safely detect and mitigate RFI must be included. The design of such an RFI processor is discussed, and resource demands on the spacecraft are indicated. [ABSTRACT FROM PUBLISHER]

ESA's soil moisture and ocean salinity mission, SMOS, has been found to suffer much more from radio frequency interference (RFI) than expected, and methods for detecting RFI in SMOS data are of vital importance. This paper describes a method using the 3rd and 4th Stokes parameters for the purpose. Obvious hot-spots are detected, but also smaller, yet still detrimental RFI, spreading out over for example the ocean, is detected. It is also discussed how detected and flagged samples statistically deviate from their surroundings. [ABSTRACT FROM PUBLISHER]