## 1 Introduction  
The performance of microwave radiometers can be seriously degraded by the presence of radio-frequency interference (RFI). This is especially important in populated land areas [1-3]. If RFI produces an increase in the power detected by the radiometer, which is larger than the natural variability of the geophysical phenomena, it can be flagged and the data discarded, but when the RFI is comparable or smaller than the natural variability, it is difficult to detect, and the retrieved geophysical parameters are erroneous [3]. In the last decade, RFI detection and mitigation methods have been devised to address this problem. The three approaches that have been used are: time, frequency or statistical analyses of the received signal. Time analysis looks for anomalous behavior of the received signal in form of power peaks above the expected range of values. These values are blanked (discarded) and only the RFI-free data are left [4,5]. However, since the detected power is a smoothed version of the instantaneous one, if the RFI peaks are shorter than the integration time they may not be detected. Frequency analysis looks for the spectrum of the received signal within the radiometer's band, and discards sub-bands with power higher than the rest [6]. Finally, since for a zero-mean Gaussian signal, such as thermal noise, all the odd moments must be zero, and the even ones must satisfy \(\left\langle x^{\ast}\left(t\right)\right\rangle=\left(n-1\right)\cdot...3 \cdot 1\cdot\sigma^{\ast}\) (where \(\sigma\) is the signal's standard deviation), statistical tests analyze some mathematical properties of the received signal and compare them to the theoretical values within some error margin, discarding the data series that do not satisfy some pre-defined criteria. Among these test the most widely used is the Kurtosis (ratio of the fourth and the square of the second moments) which must be equal to 3 in RFI-free conditions [7-9]. The sixth order moment [10], and other algorithms [11,12] have also been studied. Most of the previous studies have focused on pulsed sinusoidal signals within the radiometer's band. In radio-astronomy some techniques have been proposed to deal with GLONASS RFI [13], but since the RFI signal is much lower than the noise, some _a priori_ knowledge of the interferent signal must be known. These are called \"physical modeling\" in communications' terminology and a different model is required for each type of RFI. Another type of analyses fall within the so-called \"statistical-physical modeling\" category, that provide universal model for natural and man-made RFI. The main models are the [PERSON]'s class A (narrowband RFI within receiver's band), class B (broadband RFI wider than receiver's band), and class C (mixture of class A and B) canonical models for which the mathematical form is independent of the physical environment [14]. The RFI mitigation approach is then based on the estimation of the model parameters and apply a linear optimal filtering (Wiener filter) or optimal detection rules [15]. In this work we propose a different approach to mitigate the effect of RFI in microwave radiometry. It is based on the use of the power of the wavelet transform to denoise (remove noise from a signal). The interfering signal \(\hat{s}(t)\) (RFI) is estimated without any _a priori_ knowledge of it. This signal is then subtracted from the received signal \(x(t)\), to obtain a quasi RFI-free noise signal \(\hat{n}(t)=x(t)-\hat{s}(t)\) from which the power is detected (Figure 1). In the following sections the principles of denoising are briefly reviewed. The optimum parameters to detect and mitigate four different types of RFI (sinusoidal, Doppler-like1, chirp2, and pseudo-random noise) are studied: type of thresholding, sequence length, decomposition level, and type of wavelet (among 75 different types).