## 4 Simulation Result  
In this section, the real-time interference cancellation technique described above is applied to analyze in the noiseless and the presence of white Gaussian noise. In the conventional method using the MPM, when determining the singular value, it is determined by the user's discretion. When there are \(N\) singular values generated among MPM internal operations, only \(\sigma_{c}\) values satisfying the following formula are selected based on the largest value \(\sigma_{max}\). \[\frac{\sigma_{c}}{\sigma_{\max}}\geq 10^{-p} \tag{22}\]  
where \(p\) is the number of significant decimal digits in the data, as an example, if the simulation results are accurate up to 3 significant digits, then the singular values which have less than 0.001 compared to the largest singular value are noise singular values. They should not be used in the reconstruction of the data. Therefore, based on this \(p\) value, it is determined how many signals are combined. However, to simplify the calculation of MPM, the simulation was performed, assuming that there were only two signals to analyze. Figure 7 shows the result of estimating the signal using MPM under noiseless conditions. At this time, the signal formula and conditions are shown in Equation (23) and Table 1.  
\[y(t)=A_{I}\cos(2\pi ft+\phi)+A_{ac}\cos(2\pi(f+\Delta f)t+\phi^{\prime}) \tag{23}\]  
Figure 6: Newly proposed active interference cancellation algorithm. The blue line in the Figure 7 is the result of the combination of the interference signal and the internal AC signal at 0.5 s after the external IEMI is incident, and before the MPM is applied. In the MPM method, the cancellation was completed after a total of 100 ms of time, and the result was a red line. At this time, if the number of sampling was reduced to reduce the blind time, accurate estimation was not made, and the signal was amplified after cancellation. When applying the MPM, singular values are shown in Figure 8. As mentioned above, only two of them were selected. On the other hand, in the case of simulations using the proposed method, accurate cancellation could be performed through a single sampling immediately after sensing an external signal. This is shown in Figure 9. The process requires 1 ms of computation time on the time axis and can be calculated 100 times faster than with conventional methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Symbol** & **Value** & **Unit** \\ \hline Magnitude & \(A_{I}\) & 1 & V/m \\ Phase & \(\phi\) & 30 & \(\circ\) \\ Frequency & \(f\) & 2 & GHz \\ Frequency difference & \(\Delta f\) & 20 & Hz \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Variables used in Equation (23). Figure 8: The normalized singular value obtained when calculating MPM. Figure 7: Canceling results using the MPM. In noisy situations, accurate estimation is not possible with only one step. Figure 10 is the same situation as before, but with the white Gaussian noise applied. At this time, the signal formula and conditions are shown in Equation (24) and Table 2.  
\[y(t)=A_{I}\cos(2\pi ft+\phi)+A_{ac}\cos(2\pi(f+\Delta f)t+\phi^{\prime}) \tag{24}\]  
Even when the Signal to Noise Ratio (SNR) is 20 dB, Figure 10 shows that the MPM can be applied to cancel the system accurately. At this point, the singular values, as demonstrated in Figure 11, are higher than before due to white Gaussian noise. If you set \(p=3\), it is impossible to make an accurate estimate when using MPM. This result is because too many singular values exceed the reference value, \(10^{-3}\), which determines that there are approximately 25 signals. On the other hand, even in this situation, the proposed method accurately predicts the frequency difference using only 50 sampled data with the averaging. The result is 5 ms on the time axis and provides a 20\(\times\) time efficiency gain over conventional methods that require 100 ms of computation time.