## 4 Results  
This section validates the proposed algorithm through simulation experiments. The experiment simulates a typical Badious B1 signal (1561.1 MHz) with a sampling rate of 20 MHz. The RF front-end performs down-conversion on the received satellite RF signal to generate an intermediate frequency (IF) analog signal, which is then digitized by an A/D converter to produce a digital IF sequence. The noise-free signal at the \(n\)-th sampling instant is modeled as  
\[S_{I}[n]=\sum_{k=1}^{K}A_{k}d_{k}(nT-\tau_{k})c_{k}(nT-\tau_{k})\mathrm{cos} \big{(}2\pi\big{(}f_{I}+f_{d_{k}}\big{)}nT+\phi_{k}\big{)} \tag{33}\]  
In this expression, \(T=1/f_{s}\) is the sampling period, \(f_{s}\) is the sampling frequency, and \(f_{I}\) is the intermediate frequency. \(K\) denotes the number of visible satellites, and \(A_{k}\) and \(\tau_{k}\) represent the amplitude and time delay of the \(k\)-th satellite signal, respectively. \(c_{k}(\cdot)\) and \(d_{k}(\cdot)\) are the pseudorandom spreading sequence and the navigation data modulation waveform, respectively. \(f_{d_{k}}\) and \(\phi_{k}\) denote the Doppler shift and phase offset of the \(k\)-thsignal, respectively. After downsampling, the signal is transformed to baseband. Assuming \(f_{1}=0\), the sampled signal at time index \(n\) is re-expressed as  
\[y[n]=\sum\limits_{k=1}^{K}A_{k}h_{k}[n]d_{k}(nT-\tau_{k})c_{k}(nT-\tau_{k})\text {cos}\big{(}2\pi f_{d_{k}}nT+\phi_{k}\big{)}+z[n]+v[n] \tag{34}\]  
Here, \(h_{k}[n]\) denotes the time-varying channel response of the \(k\)-th propagation path, \(z[n]\) is the interference component, and \(v[n]\) is independent and identically distributed Gaussian noise with zero mean and variance \(\sigma^{2}=N_{0}f_{s}/2\), where \(N_{0}\) is the one-sided power spectral density of the noise. Each signal may include 1 to 5 multipath components with decreasing power, and their delays are uniformly distributed within one pseudocode period. It is assumed that there is a single interference source in the environment. All interference signals are randomly generated within predefined parameter spaces and superimposed with the GNSS signal to form composite samples. Table 3 defines the parameter space for each type of interference, where \(\mathcal{U}(\text{a},\text{b})\) denotes a uniform distribution over the interval \([\text{a},\text{b}]\). By comparing the performance of AFF, TDPB, FDPB, WPT-based interference suppression, KLT-based interference suppression, and the proposed method under various types of interference, the effectiveness of the proposed approach is validated. To evaluate the interference mitigation performance of the proposed model, three key metrics are considered:  
1. SINR: used to assess the steady-state performance after interference suppression;
2. FID: measures the degree to which the model preserves GNSS signal integrity. \[\text{FID}_{\text{dB}}=10\text{log}_{10}\left(\frac{\sum\limits_{n}y^{2}(n)}{ \sum\limits_{n}(\vartheta(n)-y(n))^{2}}\right)\] (35)
3. CAF: employs the cross-ambiguity function to evaluate signal acquisition capability. The nominal \(C/N_{0}\) of BDS B1 code is \(43\text{ dB}/\text{Hz}\) (corresponding to an SNR of approximately \(-20\text{ dB}\)). Although its direct sequence spread spectrum (DSSS) scheme provides nearly \(30\text{ dB}\) of spreading gain, signal acquisition remains highly susceptible to suppression interference. When the in-band interference power exceeds the dynamic range of the receiver, an increase in JSR leads to a sharp decline in the equivalent carrier-to-noise ratio \(\left[C/N_{0}\right]_{eq}\), which manifests as follows:  
\[\left[C/N_{0}\right]_{eq}=-10\text{log}\bigg{[}10^{-\left(C/N_{0}\right)/10} +\frac{10^{\left(I/S\right)/10}}{QR_{c}}\bigg{]} \tag{36}\]