## 1 Introduction
These techniques can be classified according to the specific processing domain. Considering time domain techniques, temporal filtering can be usually adopted. This method can be implemented at the digital intermediate frequency (IF) level after the ADC in the front-end of the GNSS receiver, which is effective only against narrow band RFI sources, because a wide band interference cannot be easily discriminated from the thermal noise by adopting temporal filtering. Frequency domain techniques are generally based on spectral estimation of the incoming signal, which is obtained by applying signal processing techniques, such as the discrete Fourier transform (DFT). These frequency domain techniques are typically performed by comparing the spectrum of the received signal with a theoretical threshold, which is usually determined according to a statistical model representing the received signal [12]. Recently, the research topic on transformed domain techniques (e.g., TF transform) has obtained increasing attention in the ID&M for GNSS receivers [13, 14, 15, 16]. These techniques allow one to observe the received GNSS signals in a joint domain. In many cases, interferences may appear for a limited time and present a very variable behavior in frequency. In comparison to the GNSS signal, interferences are extremely different in terms of time and frequency characteristics. The presence of an interfering signal is limited to a region of the two-dimensional TF plane, and the adoption of the TF analysis is allowed to detect different types of disturbing signals. TF representations (TFRs) map a one-dimensional time signal into a two-dimensional function of time and frequency [17], which have found significant applications in non-stationary signal analysis. An interference mitigation technique based on the TFR approach has been described [18], where the TFR of the received GNSS signal is obtained by performing an orthogonal-like Gabor expansion on the samples at the output of the ADC of the GNSS receiver front-end. Another class of ID&M algorithm is reported aiming at obtaining a representation of the received signal in a different domain by making use of the time-scale transformation, which can be performed by means of wavelet transform [19]. Interfering signals are usually concentrated in a limited area of the TF plane, while noise is spread over the entire plane. The TF analysis approaches are very appealing as countermeasures in the detection of a large variety of interfering signals for GNSS receivers. There are different tools representing TF distributions, and the commonly-used TFRs in interference detection for GNSS applications include spectrogram, Wigner-Ville distribution (WVD) [13, 16] and Choi-Williams transform (CWT) [16]. The spectrogram and WVD have been considered in the interference detection for GNSS applications [13]. The spectrogram approach presents the TF resolution trade-off problems according to the uncertainty principle, providing poor TF localization properties. In order to overcome the TF resolution trade-off problems of the spectrogram, WVD has been used in interference detection for GNSS receivers [13, 16]. WVD is well known, since it provides nearly the best TF resolution among all of the TF distributions and also satisfies a large number of good properties [20], but it presents very severe cross-interfering terms without any physical meaning between true signal components (auto-terms), due to the interaction of different frequency components [16]. Most of the previous works on interference detection for GNSS receivers based on TF analysis are concerned with various TFR plots. Nevertheless, the quadratic TF distribution is usually a biased estimator for signal instantaneous frequency due to the presence of cross-term problems or makes a trade-off of temporal and frequency resolution due to the limitation of the uncertainty principle.