## 3 Proposed Jamming Suppression Method  
### Analysis of Performance Degradation Under a Unified Suppression Strategy  
Different interference scenarios exhibit markedly distinct statistical characteristics, making it difficult for a single suppression strategy--lacking a tailored design for specific interference mechanisms--to achieve global optimality. Even for the same type of interference, different algorithms can yield significantly different suppression results. Without incorporating specialized reception structures, such as antenna arrays or polarization-based systems, conventional positioning receivers typically rely on three representative interference suppression techniques: frequency domain pulse blanking (FDPB), time domain pulse blanking (TDPB), and adaptive FIR filtering (AFF). (1) FDPB  
The received signal is mapped from the time domain to the frequency domain using the discrete Fourier transform (DFT) method:  
\[X(k)=\sum_{n=0}^{N-1}x(n)e^{-\jmath\pi\frac{\
u u}{N}} \tag{7}\]  
Here, \(N\) denotes the transform length. If the interference exhibits sparsity in the frequency domain, appearing as localized spectral anomalies, effective suppression can be achieved by eliminating spectral distortion through nonlinear filtering. However, threshold selection is constrained by the non-stationary nature of receiver noise. Following the approach in [21], let \(Y(k)=X(k)/|X(k)|\) to remove the dependence on prior information. The processed spectrum is then transformed back to the time domain using the inverse discrete Fourier transform (IDFT) mtehod, yielding the interference-suppressed signal. \[y(n)=\frac{1}{N}\sum_{k=0}^{N-1}Y(k)e^{\jmath 2\pi\frac{\
u u}{N}} \tag{8}\]  
(2) TDPB  
TDPB operates directly on the sampled signal using a threshold-free strategy, with the processing procedure given by [21]:  
\[y(n)=\frac{x(n)}{|x(n)|} \tag{9}\]  
(3) AFF  
AFF adaptively adjusts the filter coefficients to automatically generate notches in the interference bands for suppression. Let L be the order of the FIR filter; its output is expressed as [22]  
\[y(n)=\sum_{k=0}^{L-1}x(n-k)h^{(n)}(L-k) \tag{10}\]  
Here, \(h^{(n)}(k)\) denotes the \(k\)-th coefficient of the FIR filter at time \(n\). The filter coefficients are iteratively updated using the normalized least mean squares (NLMS) algorithm [22]:  
\[h^{(n+1)}(k)=h^{(n)}(k)-\frac{\mu}{|x(n)|^{2}}x(n)(y(n))^{*} \tag{11}\]  
\(\mu\) is the normalized step-size factor, and \((\cdot)^{*}\) denotes the complex conjugate operation. The coefficient update is driven by \(y(n)\); a larger \(\mu\) results in faster convergence but highersteady-state error, while a smaller \(\mu\) leads to slower convergence with improved steady-state accuracy. In the simulation experiments, \(\mu\) = 0.1 is selected as an empirical value. To comprehensively evaluate the performance of various suppression methods under different interference conditions, three typical jamming scenarios are constructed and subjected to 1000 Monte Carlo simulations: narrowband jamming (bandwidth: 0-4 MHz, occupying 20% of the BDS receiver signal bandwidth); pulse jamming (repetition period: 0.04-1 ms, duty cycle: 0-40%, modulated by band-limited noise); chirp jamming (sweep bandwidth: 4-20 MHz, sweep period: 0.01-1 ms). For each type of interference, suppression is performed using the FDPB, AFF, and TDPB strategies, respectively. The output signal's \(C/N_{0}\) is statistically analyzed to assess the suppression performance of each algorithm. Figure 2 compares the algorithm performance across the three interference scenarios. Under narrowband jamming, AFF and FDPB exhibit similar \(C/N_{0}\) improvement, while TDPB yields the lowest \(C/N_{0}\) due to insufficient frequency-domain resolution. In the case of pulse jamming, TDPB performs the best, owing to its advantage in time-domain pulse shaping, achieving a mean \(C/N_{0}\) of 37.7 dB/Hz; FDPB follows, while AFF performs the worst due to its lack of transient response capability.