## 3 Proposed Jamming Suppression Method
### A Framework for Unknown Interference Open Set Detection and Conservative Suppression  
Since the domain alignment module significantly tightens the feature distribution of known classes, the SoftMax layer may produce high-confidence misclassifications for unknown interference. To address this, a lightweight OpenMax-Lite rejection module is appended to the output of the interference diagnosis network. This module relies solely on the distance between a sample and the predicted class center and estimates the probability of unknown interference using the Weibull distribution fitted to the tail of known class features. If the estimated unknown probability exceeds a predefined threshold or the predicted confidence falls below the set limit, the input is classified as unknown interference, and the system switches to conservative broadband suppression. Assume the interference set is defined as \(\mathcal{C}=\left\{\text{AM, FM, Chirp, CW, Pulse, spoofing}\right\}\). The logits output vector from the back end of the diagnostic network is denoted as \(\mathbf{z}(x)\in\mathbb{R}^{M}\), and the domain-aligned intermediate feature is \(\mathbf{f}(x)\in\mathbb{R}^{D}\). The decision is made as follows:  
\[J(x)= \begin{cases}1,x\in C\\ 0,x\
otin C\end{cases}  \tag{21}\]  
If \(J=0\), the system switches to the conservative broadband suppression path. 1. Offline phase: class center and tail distribution modeling. \[\text{Feature center:}\] (22)  
Here, \(N_{j}\) is the number of samples for class \(j\) in the training set, and the distance is measured as \(d_{j}(x)=\left\|\mathbf{f}(x)-\mathbf{c}_{j}\right\|_{2}\). For each class, the top 20% of the largest values from the distance sequence \(\left\{d_{j}\right\}\) are selected to form the set \(\mathcal{T}_{j}\). The cumulative distribution function (CDF) of the Weibull distribution is  
\[F_{j}(d)=1-\exp\left[-\left(\frac{d}{\lambda_{j}}\right)^{k_{j}}\right],d\geq 0 \tag{23}\]  
Here, \(\left(\lambda_{j},k_{j}\right)\) are the scale and shape parameters of the Weibull distribution, obtained via maximum likelihood estimation. (2) Online detection phase:  
For the SoftMax output vector \(\mathbf{p}(x)=[p_{1},p_{2},\ldots,p_{M}]\), the predicted label is \(\hat{y}=\underset{j}{\text{argmax}}p_{j}(x)\), with confidence threshold \(\tau_{l}\). Based on the Weibull distribution parameters \(\left(\lambda_{\hat{y}},k_{\hat{y}}\right)\) fitted from the tail samples of class \(\hat{y}\), the probability that the sample falls outside the trusted region is calculated as \(P_{\text{unk}}(x)=F_{\hat{y}}\big{(}d_{\hat{y}}(x)\big{)}\). If \(\tau_{\text{con}}<\tau_{l}\) or \(P_{\text{unk}}(x)\geq\tau_{f}\), the input is classified as unknown interference. To determine \(\tau_{l}\) and \(\tau_{f}\), a separate validation dataset is used to compute the true positive rate (TPR) and false positive rate (FPR) under different thresholds, generating the ROC curve. The optimal threshold is selected based on the maximum Youden index:  
\[\underset{\text{x}}{\text{argmax}}[\text{TPR}(x)-\text{FPR}(x)] \tag{24}\]  
KLT suppression does not rely on the interference type or spectral priors. Instead, it estimates the overall covariance of the received signal and performs eigen-decomposition to extract the dominant energy directions that form the interference subspace. Although its suppression performance is inferior to that of targeted suppressors when the interference type is known, it can still significantly reduce leakage of the dominant power components and suppress broadband interference backgrounds in unknown interference scenarios. The procedure is as follows:  
1. For the sample data block \(\mathbf{y}_{n}\in\mathbb{C}^{L\times 1}\) within the \(n\)-th window, the covariance matrix is updated as \[\mathbf{\hat{R}}_{n}=\alpha\mathbf{\hat{R}}_{n-1}+(1-\alpha)\mathbf{y}_{n} \mathbf{y}_{n}^{\text{H}},0<\alpha<1\] (25)
2.