## 3 Proposed Jamming Suppression Method
### Directed Suppression Strategies with Known Interference Types  
This section innovatively establishes the mapping relationship between the interference and suppression algorithms and proposes a decision switching criterion based on signal quality evaluation. We design a targeted suppression strategy based on the classification label and the confidence score \(\tau_{con}\) output by the interference diagnosis module. When the interference is of a known type and \(\tau_{con}\geq\tau_{t}\), the system activates a parameterized suppressor matched to the interference category; otherwise, it switches to an open-set conservative suppression mode. According to the degree of alignment between the interference types and suppression methods, a mapping relationship is constructed, as shown in Table 2. Since spoofing interference involves the imitation and confusion of false satellite signals, once spoofing is confirmed, the system excludes the contaminated satellite signals from the positioning solution and activates the integrity protection algorithm to recalculate the protection level, thereby ensuring the trustworthiness of the navigation output. Considering that parameter estimation errors and the dynamic range of excessive interference may lead to failure of the initial suppression strategy, this paper constructs a two-level anti-jamming decision framework by adding a signal quality feedback control layer above the diagnostic layer (see Figure 6). Within each monitoring window \(\mathcal{W}_{k}=\{t_{k}-mT_{s},\ldots,t_{k}\}\), sliding estimates of the frequency-domain \(C/N_{0}(k)\), DLL discriminator variance \(\sigma^{2}_{\text{DLL}}(k)\), and residual interference power ratio \(\hat{\eta}(k)\) are performed, and a loss function is constructed. \[L(k)=\sum_{i=1}^{3}w_{i}(k)\cdot\frac{v_{i}(k)-v_{i,\text{nom}}}{v_{i,th}-v_{i, \text{nom}}},\sum_{i=1}^{3}w_{i}(k)=1 \tag{17}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Jamming** & **Preferred** & **Alternative** & \begin{tabular}{c} **Estimated** \\ **Parameters** \\ \end{tabular} & \
\begin{tabular}{c} **Estimation** \\ **Method** \\ \end{tabular} \\ \hline AM & AFF & FDPB & \(f_{p},M_{a}\) & FFT + Hilbert \\ FM & FDPB & FDPB & \(f_{c}(t)\), \(\hat{f}_{c}\) & FFT + PLL \\ Chirp & FDPB & AFF & \(f_{start},f_{stop},\hat{f}_{c}\) & STFT + Hough \\ NB & AFF & FDPB & \(f_{0}\) & FFT single-peak \\ Pulse & TDPB & FDPB & \(t_{k}\), \(\eta_{k}\) & Time-domain \\  & & & &
\begin{tabular}{c} CFAR \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mapping between interference types and suppression algorithms. Figure 6: Anti-jamming decision switching process based on comprehensive signal quality assessment. \(\
u_{\text{nom}}\) denotes the nominal value, and \(\
u_{th}\) is the lower bound under failure conditions. Based on historical data \(\left\{x_{ij}\right\}\), the probability distribution is calculated as \(p_{ij}=x_{ij}/\sum\limits_{i=1}^{n}x_{ij}\), which is then used to compute the initial weight allocation:  
\[w_{j}(0)=\frac{1-E_{j}}{\sum\
olimits_{l=1}^{3}(1-E_{l})},\ E_{j}=-\frac{1}{ \ln n}\sum\limits_{i=1}^{n}p_{ij}\text{ln}p_{ij} \tag{18}\]  
The weights are updated every \(T_{ad}=20\) s using a sliding mean. \[\overline{v}_{j}=\frac{1}{m_{ad}}\sum\limits_{i=k-m_{ad}+1}^{k}\
u_{j}(i) \tag{19}\]  
Here, \(m_{ad}=T_{ad}/T_{s}\), and the weight update is given by  
\[w_{j}(k)=\frac{w_{j}(k-1)\left[1+\alpha\left(\
u_{j}(k)-\overline{v}_{j} \right)\right]}{\sum\
olimits_{l=1}^{3}w_{l}(k-1)[1+\alpha\left(\
u_{l}(k)- \overline{v}_{l}\right)]} \tag{20}\]  
The adjustment factor \(\alpha\) is set within the range [0.05, 0.1]. Three suppression states are defined: Primary, Backup, and Conservative broadband. After obtaining the composite loss \(L(k)\) within the monitoring window, it is compared against fixed thresholds. 1. Upgrade condition: if \(L(k)\geq 1\), the suppression state escalates to the next level (Primary \(\rightarrow\) Backup, or Backup \(\rightarrow\) Conservative). 2.