## 2 The Consecutive Mean Excision Based on Signal Cancellation (SCCME) Algorithm [35]  
The other GNSS frequency domain anti-jamming improvement algorithm is the SCCME algorithm. The concept behind the SCCCME algorithm involves iteratively approximating and fitting the power spectrum of strong signals. This process aims to eliminate the impact of strong signal power on the input signal, thereby transforming the anti-jamming issue in strong-signal scenarios into a conventional interference detection problem in weak-signal scenarios. The authors considered that the power spectrum of the input signal (signal and noise) in strong-signal scenarios follows a chi-square distribution. Consequently, they derived a first-order expression relating the power spectrum of the input signal to the power spectral density of the signal. Subsequently, they constructed an approximation model:  
\[Y_{e}(f)=aG_{s}(f)+b \tag{1}\]  
where \(Y_{e}(f)\) represents the estimated power spectrum of the input signal, \(G_{s}(f)\) denotes the power spectrum of the signal, and a and b are parameter variables.  
Assuming that the input signal power spectrum is denoted as \(P(f)\), the authors employed the gradient descent method. They minimized the mean square error between \(P(f)\) and the model estimate \(Y_{e}(f)\). The estimated values of parameters a and b in Equation (1) were obtained through iterations. Consequently, they derived an estimate of the mean of the input signal power spectrum \(Y_{e}(f)\). In the subsequent step, the estimated power spectrum mean \(Y_{e}(f)\) obtained from the model was subtracted from the input signal power spectrum \(P(f)\). This operation was interpreted as removing the power spectrum values corresponding to signal components in the input signal, retaining only the noise and jamming signal components.  
According to the flow of this algorithm, it is evident that during the iterative estimation of \(Y_{e}(f)\), the algorithm tends to overestimate the signal power spectral density due to the criterion of minimizing the mean square error between \(P(f)\) and \(Y_{e}(f)\) and considering that jamming power is often much greater than signal or noise power in practical scenarios. The process of canceling out the estimated \(Y_{e}(f)\) from \(P(f)\) may lead to the removal of some noise power, making it challenging to statistically characterize the remaining noise power distribution. However, in frequency domain anti-jamming, the premise for setting detection thresholds involves the statistical characteristics of the amplitude spectrum. In other words, the statistical characteristics of spectral lines at different frequencies should be consistent. In this case, due to the over-removal of noise power, spectral lines' statistical characteristics at different frequencies become inconsistent and are correlated with the frequency. Setting the interference detection thresholds according to the original statistical characteristics results in an inflated false alarm probability or a reduced detection probability. Inflated false alarm probabilities may cause more interference-free spectral lines to be misjudged as jamming signals and zeroed out, introducing greater anti-jamming losses. Conversely, a reduced detection probability may result in residual interference spectral lines, affecting the anti-jamming performance.  
Such interference detection errors may be tolerable when confronting satellite navigation signals with downlink bandwidths, commonly in the tens of megahertz. However, due to the generally narrow downlink signal bandwidth of LEO satellites (e.g., Iridium system with a bandwidth of 500 kHz and Orbcomm system with only 25 kHz), errors in interference detection can significantly impact the signal quality after anti-jamming processing.