## 3 Proposed Jamming Suppression Method
### A Framework for Unknown Interference Open Set Detection and Conservative Suppression
Decompose the updated covariance matrix \(\mathbf{\hat{R}}_{n}\) as \[\mathbf{\hat{R}}_{n}=\mathbf{U}_{n}\mathbf{\Lambda}_{n}\mathbf{U}_{n}^{\text{ H}},\mathbf{\Lambda}_{n}=\text{diag}(\lambda_{1}\geq\ldots\geq\lambda_{L})\] (26)
3. The interference subspace dimension is estimated using the minimum description length (MDL) criterion: \[\hat{q}=\delta\underset{q}{\text{argmin}}\left[-\text{log}\left(\prod_{i=q+1 }^{L}\lambda_{i}\right)+(L-q)\cdot\text{log}\left(\frac{1}{L-q}\underset{i=q+1 }{\sum}\lambda_{i}\right)+\frac{1}{2}q(2L-q)\text{log}N_{R}\right]\] (27) where \(N_{R}=1/(1-\alpha)\). 4. Let the eigenvectors corresponding to the interference subspace be \(\mathbf{U}_{q}=\left[\mathbf{u}_{1},\ldots,\mathbf{u}_{q}\right]\), and its orthogonal complement be \(\mathbf{U}_{q}^{\perp}=\left[\mathbf{u}_{q+1},\ldots,\mathbf{u}_{L}\right]\). The projection matrix is constructed as \[\mathbf{P}_{\text{KLT}}=\mathbf{U}_{q}^{\perp}\left(\mathbf{U}_{q}^{\perp} \right)^{\text{H}}\] (28)The filtered output is then given by  
\[\widetilde{\mathbf{y}}_{n}=\mathbf{P}_{KL}\mathbf{\gamma}_{n} \tag{29}\]  
The KLT suppression method primarily targets low-rank, high-power interference, but it may still exhibit blind spots when dealing with sparsely distributed multi-tone peak interference in the frequency spectrum. To address this limitation, a wideband adaptive frequency filter (AFF-WB) module is further introduced. This module performs compensation-based suppression of spectral peak interference without relying on prior parameters, using autonomous spectral peak detection and fixed-template notch filter construction. The processing flow is as follows:  
1. Let \(S_{n}[k]=\mathrm{FFT}_{K}\Big{\{}\widetilde{\mathbf{y}}_{n}\Big{\}}\) and the average power spectrum be \(\overline{P}_{n}\). A frequency bin k is selected if it satisfies \[10\mathrm{log}_{10}\Big{(}|S_{n}[k]|^{2}/\overline{P}_{n}\Big{)}\geq\gamma_{ \mathrm{pk}}\] (30) Record the spectral peak as \(f_{m}=kF_{s}/N\), where \(\gamma_{\mathrm{pk}}\) is the threshold for peak detection in the spectrum. 2. For each peak frequency, construct a pair of fixed-bandwidth poles as \(a_{m}=re^{\beta\pi f_{m}/F_{s}}\). All notch filters are cascaded to form the overall transfer function \[H(z)=\prod_{m=1}^{M}\frac{1-a_{m}z^{-1}}{1-\frac{a_{m}}{|a_{m}|^{2}-1}}\] (31)
3. The time-domain filtered output is given by \[F_{j}(d)=1-\exp\left[-\left(\frac{d}{\lambda_{j}}\right)^{k_{j}}\right],d\geq 0 \tag{23}\]0 (32)  
To address potential processing overhead issues, the proposed framework was designed with computational efficiency and hardware deployment feasibility in mind. The classification module employs a lightweight OpenMax-Lite rejection mechanism, achieving inference times within 20 milliseconds on an ARM Cortex-A72 CPU. The decision switching logic is based on low-complexity signal quality statistics and is updated every 100 milliseconds, which is fully within the standard GNSS positioning cycle update interval. For the suppression module, all algorithms (such as AFF, FDPB, and TDPB) are implemented using minimized window sizes and optimized matrix operations.