# Interference Mitigation for GNSS Receivers Using FFT Excision Filtering Implemented on an FPGA
## 2 Related Work  
There has been a large amount of progress made not only in GNSS receiver technology but also regarding its application in which they perform relevant functions such as in the field of military, aircraft, maritime, space, and land applications. Furthermore, many studies have investigated the methods of mitigation of GNSS receiver interference. The aim of these studies is to remove or reduce the interference effects on the GNSS receiver and realise the desired carrier-to-noise density ratio \((C/No)\). As a result, receiver manufacturers and researchers have investigated different kinds of GNSS interference mitigation techniques. The first and most widely used method is an adaptive notch filter. A notch or bandstop filter is a good choice for mitigating CW interference in the passband of the GNSS signals. This sort of interference is generally either self-generated or from a fixed source nearby such as a transmitting antenna. Analogue notch filters can be applied at the RF path or digital filters can be employed at the sampled IF signal to provide more flexibility. An adaptive notch filter is able to detect the centre frequency of the interference and adjust the notch filter to match. This can provide a trade-off in terms of power consumption and processing power [21, 22, 23]. The authors in [24] present two frequency lock loop (FLL) equivalent models of an adaptive notch filter to detect sweeping jammers: one is standard and the second one is exponential filtering FLLs. They showed that the standard FLL has a better performance regarding the interference than the FLL exponential filter. This research work was extended in [25] by the proposal of an FLL-equivalent adaptive notch filter (ANF) to mitigate different types of interference. The ANF process consists of three parts. The first part is the adaption unit, which estimates the centre frequency to be removed. The second part is the detection unit, which decides whether to remove any narrowband interference. The third part is the notch filter, which is activated by the detector to process the input signal by suppressing a narrowband frequency around the rejected frequency set by the adaption unit. The drawback of this study is the power consumption issue, as the proposed detection method is based on the output-input power ratio. This detection method results in high-power consumption being efficient for interference mitigation; the stronger the interference is, the higher the suppression power that is required. In [13], the estimation of the jammer's frequency band is performed with a Prony estimator instead of FFT/IFFT in order to decrease the complexity of the whole algorithm. The adaptive notch filter shows small performance improvements (~10 dB) at the level of interference that can be tolerated with sweeping continuous-wave (CW) interference. This small improvement is due to the relatively simple implementation of using one or two-pole notch filters, which can only achieve a very limited depth in the null of the notch filter. Similar levels of mitigation can be seen in mass-market receivers as investigated in [13]. They considered a low level of interference while this paper aimed to achieve greater levels of resistance to interference using higher-order filtering. The second method is empirical mode decomposition (EMD) and blind source separation (BSS) [26]. In this method, the signal is decomposed iteratively by interpolating the min and max of the signal and removing this interpolated signal from the original signal. The work published in [26] developed two-stage algorithms. In the first stage, EMD is used to find the pseudo-periodic signals amid the received signal. This works well for stationary signals. However, nonstationary signals are more complicated, and the use of a second stage is required. The second stage is a blind source separation algorithm (or BSS). The drawback of this stage is that BSS is a complex algorithm (e.g., matrix inversion, determinant), hence requiring a large number of computations.