# Adaptive Satellite Navigation Anti-Interference Algorithm Based on Inverse Cosine Function
## 4 Simulation Analysis
### Navigation Interference Suppression Effect Analysis  
#### 4.2.1 Simulation Verification of Interference Suppression Performance  
Three interfering signals are added to the satellite navigation signal for the suppression simulation. The BII satellite signal power is \(-160\) dBW, the IF signal bandwidth is \(4.092\) MHz, the center frequency is \(1.546\) MHz, the sampling frequency is \(20\) MHz, three narrowband interfering signals are added with frequencies of \(1.4\) MHz, \(2.4\) MHz, and \(2.8\) MHz, and they dry signal ratio is \(50\) dB. Four algorithms are employed for the purpose of attenuating interfering signals and retrieving the original satellite navigation signal. By effectively mitigating the interference present in the satellite navigation signal, these algorithms successfully suppress the interference and yield a power spectrum that accurately represents the reduced interference level.  
Figure 6 shows the spectrum of the normal satellite navigation signal, the satellite navigation signal joined with the interference, the proposed ICVS-LMS algorithm, the LVVS-LMS algorithm, the VVS-LMS, algorithm and the NDVS-LMS algorithm when suppressing the three interfering signals. According to Figure 6, it is evident that the spectral line of the narrowband interference exhibits a considerably greater magnitude compared to that of the normal signal, and the four algorithms have inhibitory effects on narrowband interference signals. Compared with the three existing algorithms in the literature, the adaptive filtering using the ICVS-LMS algorithm proposed in this paper is more effective in suppressing the three narrowband interfering signals.  
Figure 6: Comparison of interference suppression results of improved algorithms. All circles are marked as the spectral lines of the interference signals.  
Figure 7 displays the amplitude comparison diagram of the three interference signals processed by different algorithms. After implementing the algorithm proposed in this paper, the amplitude of the three interference signals is reduced compared to that of other algorithms and maintains a consistently low level.  
Table 2 shows the comparison of the suppression weakening of interference signals between the ICVS-LMS algorithm and the existing algorithms. Regarding interference suppression 1, the three existing literature algorithms exhibit similar suppression capabilities, hovering around 38%. In contrast, this paper's algorithm achieves a notable 46.4% suppression rate, demonstrating superior performance in weakening and mitigating interference signals compared to the other three algorithms; in the suppression of interference 2, the lowest suppression ability of the LVS-LMS algorithm is 57.98%. The VVS-LMS and the NDVS-LMS algorithms interference suppression abilities are slightly improved compared to the LVS-LMS algorithm, but the effect is limited, while the ICVS-LMS algorithm can improve the interference suppression ability by more than 10% compared to the lowest level; in the suppression of interference 3, the suppression ability of the four algorithms is improved compared with that in interference 1 and 2, and the interference suppression ability of the algorithm in this paper is still the most advantageous among the four algorithms.  
In summary, the suppression effect of the proposed ICVS-LMS algorithm on all three interfering signals is better than the three existing algorithms, and the ability to suppress interference is improved by 8.41-12.22%, which fully proves the superiority of the algorithm's performance in interference suppression.