## 4 Results
\begin{table}
\begin{tabular}{c c} \hline \hline
**Jamming** & **Parameter Setting** \\ \hline AM & \(f_{p_{k}}\sim\mathcal{U}(1558,1564)\text{ MHz}\), \(m\sim\mathcal{U}(0.2,0.8)\) \\ FM & \(f_{m_{k}}\sim\mathcal{U}(0.5,2.0)\text{ MHz}\), \(\beta_{k}\sim\mathcal{U}(0.2,1.0)\) \\ Chirp & \(f_{start}\sim\mathcal{U}(1558,1564)\text{ MHz}\), \(\Delta f\sim\mathcal{U}(1,25)\text{ MHz}\), \\ \(T_{\text{swap}}\sim\mathcal{U}(0.05,5)\text{ ms}\), \(\alpha=\pm 1\) \\ NB & \(f_{0}\sim\mathcal{U}(1558,1564)\text{ MHz}\), \(B\sim\mathcal{U}(50,300)\text{ kHz}\) \\ Pulse & \(\alpha_{k}\sim\mathcal{U}(0.8,12)\), \(\alpha_{k}\sim\mathcal{U}(0.02,1)\text{ \mu s}\), \\  & \(T_{p}\sim\mathcal{U}(2,10)\text{ \mu s}\), \(M_{p}=\lfloor 1\text{ ms}/T\rfloor\), \(D=1/T_{p}\sum_{k=1}^{M_{p}}a_{k}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Definition of interference parameter space. A spreading factor of Q = 1 indicates narrowband interference, while Q = 2 represents wideband interference. The signal acquisition threshold is set at 28 dB/Hz. Figure 7 illustrates the evolution curve of \(\left[C/N_{0}\right]_{eq}\) with increasing JSR. The simulation results show that when the interference power exceeds the signal power by approximately 34.9 dB, the receiver reaches a critical state of acquisition failure. This section evaluates the interference suppression performance of the proposed method through 100 independent Monte Carlo simulation runs. Figure 8a presents the SINR performance under a narrowband single-tone interference scenario, comparing the proposed method with several existing suppression algorithms, including AFF, FDPB, TDPB, KLT, and WPT, across varying interference intensities (JSR levels). The simulation results show that while all methods exhibit a certain degree of suppression capability against single-tone interference, the output SINR of each algorithm generally degrades as the interference power (JSR) increases. This trend is primarily due to the increasing difficulty of suppression under stronger interference conditions. In contrast, the proposed method incorporates a diagnostic analysis module that dynamically selects the most suitable suppression sub-algorithm based on the diagnosed interference type, thereby achieving more stable and superior performance across the entire JSR range. Compared to AFF, FDPB, and TDPB, the proposed method achieves average SINR improvements of 0.9 dB, 0.4 dB, and 16.9 dB, respectively. Notably, the substantial advantage over TDPB is attributed to its inherent weakness in dealing with narrowband interference, resulting in severely limited suppression effectiveness. It is worth noting that although the KLT method demonstrates slightly better SINR performance than the proposed method under high JSR conditions, it suffers from a noticeable decline in FID. Figure 8b further analyzes the variation in FID across different algorithms, revealing that KLT exhibits a rapid decrease in FID as JSR increases. This phenomenon stems from the fact that KLT employs a fixed threshold to determine the retention of eigen components. When interference power is high, it becomes difficult to distinguish between interference-dominated and signal-dominated components, leading to the erroneous removal of eigenvectors containing useful signal energy, thereby compromising signal integrity. In contrast, WPT achieves relatively high FID under low JSR conditions due to its multi-band decomposition strategy. However, as interference strength increases, the spread of interference energy renders WPT's fixed-band partitioning increasingly ineffective. Signal-containing bands are misclassified as interference, causing  
Figure 7: \(\left[C/N_{0}\right]_{eq}\) evolution curve with respect to JSR. significant nonlinear distortion to the signal and a rapid drop in FID. Compared with these methods, the proposed approach adaptively identifies interference frequency regions and applies targeted frequency-domain projection suppression.