## 2 Related Works  
ANFs have been widely used with mitigation systems in different areas, such as biomedical (e.g., cancel 60 Hz interference) [16; 17], communications (e.g., reject NBI in the BPSK spread-spectrum communication system), and GNSS applications [18] and control (e.g., controlling the howling in speakerphone systems) [19]. In the literature, various mitigation techniques already exist to reduce and remove the negative effects of RFIs, which can be classified into three groups: adaptive antenna-based techniques [20], Time-Frequency Filtering (TFF)-based methods [21; 22], and adaptive-filtering-based techniques [15; 23; 24].  
Figure 2: The received PS signal with various \(E_{b}/N_{o}\) Parameters.  
Adaptive antenna techniques are useful for WB and NB jammers, but their main disadvantage is their accompanying computational complexity [20; 25; 26].  
TFF-based techniques provide useful information in both the Time Domain (TD) and Frequency Domain (FD) simultaneously [27]. TFFs are mainly used to mitigate NB jamming. Bilinear signal distribution [28], filter banks [29], Short-Time Fourier Transforms (STFTs) [30], and Wavelet Transforms (WTs) [31; 32; 33] are examples of TFF tools. Filter banks and STFTs cannot simulate most nonstationary signals, due to the use of fixed windows. By adjusting the trade-off between frequency and time, the WT solves the drawbacks of filter banks and STFTs; however, for each type of jamming, it must be tuned to perfection [34].  
Adaptive-filtering-based techniques for NB jamming rejection are commonly used, and can be classified into two types: FD and TD. In the FD approach, ref. [35] proposed using automatic gain control (AGC) to mitigate CWI and NB; ref. [36] proposed a method to detect CWI by statistical inference, while [11] proposed an IIR NF for detecting and mitigating MCW by implementing it as a first-order IIR NF and using a simplified Welch algorithm. However, computational complexity and higher costs are the main drawbacks of the FD approach. In the TD approach, the adaptive lattice NF is used to detect and mitigate CWI [18; 37]. Moreover, ref. [37] described an ANF method for detecting and reducing MCWI in GPS systems. In GNSS Inter-Satellite Links (ISLs), ref. [38] proposed an adaptive NBI suppression approach based on the code-aided technique; refs. [13; 15; 39] proposed ANF methods based on a lattice form to mitigate the CWI. A low-complexity method based on a second IIR NF for GNSS to mitigate CWI was proposed in [40]. To reduce and mitigate CWI, ref. [41] presented a new low-complexity anti-jamming NF for general wireless communications, while [42] derived FIR excision filters that optimize receiver SNR for NB interference. In GPS, ref. [42] proposed a method to mitigate CWI using adaptive FIR and ACM filters, while [43] proposed a method based on the lattice-form structure for detecting and mitigating an MCWI by controlling the depth of the NF.  
Kalman filters [44], ANFs [15; 39; 45], Approximate Conditional Mean (ACM) filters [46], neural network-based predictors [44; 47], and augmented state ACM (ASACM) filters [48] are examples of adaptive-filtering-based techniques and tools. However, adaptive-filtering-based methods have certain drawbacks. ACM and Kalman filters do not work effectively when a priori knowledge of the jamming model parameters is unavailable. The computational complexity of the ASACM filter increases exponentially in the case of MCWI rejection.