# Interference Mitigation for GNSS Receivers Using FFT Excision Filtering Implemented on an FPGA
## 2 Related Work
Moreover, this method works with jamming to signal power ratios (JSRs) of up to 45 dB with nonstationary jamming. However, the method starts to be degraded at 50 dB [26]. To overcome this issue, a suggested solution has been discussed of combining spatial filtering with this technique [27, 28]. Another solution is to integrate the Kalman filter with this algorithm to estimate the phase/code delay. As a result, this combination may enable processing in the presence of a stronger or higher level of jamming [26]. The third method is the wavelet filter. Recently, wavelet filters have been implemented to mitigate interference in GNSS as proposed in [29, 30]. In these references, interference detection is achieved through a time-scale representation of the GNSS-interfered received signal exploiting the wavelet functions. The wavelet transform provides decomposition of a signal by taking advantage of a group of orthogonal local basis functions [30]. The suppression is observed by the threshold in each wavelet scale. Each coefficient of the wavelet packet decomposition is compared to a predetermined threshold, and it is blanked or clipped when such a threshold is exceeded. Threshold determination is performed according to the required false alarm probability and a statistical characterization of the GNSS signal obtained in an interference-free environment [29]. However, the results are not compelling, and a wavelet filter would only be suitable for specific high-grade receivers because of the processing time and high complexity. The fourth method is zero-memory non-linearity. This algorithm, which is also called complex signum non-linearity, is another statistical approach, derived from the Laplace distribution. These techniques mitigate the interference by generalising the correlation process. This is achieved by extracting the sign of each sample, resulting in +1 for positive values and \(-1\) for negative values. The major advantage of this algorithm is that no signal estimation is required as it is often the weak point in many interference mitigation techniques. This also implies that no settings are required for the optimal operation of the filter. In addition, the algorithm is relatively efficient when it comes to computational costs. In fact, it only works if part of the samples is affected by the interference (pulsed jamming); therefore, it cannot mitigate continuous jamming [31]. The fifth method is pulse blanking, which is a widely used technique in mitigating the pulsed interference of GNSS receivers due to its simplicity and effectiveness. Pulse blanking (PB) uses the zeroing techniques of the received signal sample that is affected by the interferences [32]. PB is a pre-correlation mitigation technique and it is applied to the down-converted signal before the correlation process. The disturbing signal is removed by zeroing the samples corrupted by the interference pulses. In order to accomplish this, it is important to determine the pulse position. The techniques that are used to estimate the pulse position are ideal blanking and thresholding [32]. However, this method works with a combination of automatic gain control (AGC). AGC should be reduced in the case of no existing interference. Otherwise, this technique will remove parts of the original signal. This technique can be used in combination with other techniques to address the specific problem of pulsed interference. The sixth method is the fast Fourier transform (FFT) filter. FFT excision is a powerful tool for detection and interference removal. The ability to modify the received spectrum allows for rapid and efficient removal of jammers with different characteristics (bandwidth, frequency, pulsed, sweeping rate) compared to other algorithms [33, 34]. Additionally, it has the ability to act on multiple jamming sources and can even be used as an equalization filter of the receiver's RF front-end biases.