## 4 Experimental Results and Analysis
### Simulation 1: The Selection of Scaling Factor \(\gamma\) in the Contribution Degree Function  
In this simulation, we verified the impact of the scaling factor \(\gamma\) on the separation performance of the proposed algorithm. The simulation was conducted under the condition of additive white Gaussian noise with a signal-to-noise ratio (SNR) of 16.4 dB. It is assumed that the three signals with different modulation (2 ASK/BPSK/QPSK) are mixed instantaneously, in which the symbol rate of the signals are \(5\times 10^{6}\), \(5\times 10^{6}\), \(10\times 10^{6}\) B, respectively. The modulation carrier frequencies are 13.5, 20, and 26.5 MHz, respectively, with the \(\lambda\) of each signal being 35%. The mixing matrix \(A\) is  
\[A=\begin{bmatrix}0.21&0.63&0.89\\ 0.92&0.58&0.22\end{bmatrix} \tag{16}\]  
The separation performance with different \(\gamma\) is shown in Figure 8; the bigger value is, the better the separation performance is. It can be seen that, with \(\gamma\) increasing, the SIR decreased significantly while the SAR and SDR remained almost constant. Thus we take \(\gamma\) as 0.5 in the following simulation experiments. Figures 9 and 10 show the waveforms of the source signals and their mixed signals, respectively. Comparatively, Figure 11 shows the waveforms of the signals separated by the proposed algorithm with \(\gamma=0.5\). It can be seen that the waveforms of the separated signals are basically consistent with the source signals. Although the waveforms have some distortion at the junction of bit 0 and bit 1, which is caused by the \"boundary effect\" of STFT, it does not affect the subsequent demodulation and judgment. Figure 12 represents the waveforms after coherent demodulation. The bit data can be recovered by setting the appropriate judgment level.  
The second channel of signals arrived with delay; the signal propagation delay matrix and the corresponding mixing matrix are  
\[\mathbf{T}=\begin{bmatrix}0.1&0.05&0.2\\ 0.18&0.1&0.04\end{bmatrix} \tag{18}\]  
\[\mathbf{A}_{2}=\begin{bmatrix}0.22&0.42&0.37\\ 0.41&0.17&0.28\end{bmatrix} \tag{19}\]  
The SDR and BER of the separated signals with the increase in \(\lambda\) are shown in Figures 13 and 14. From the simulation results, it can be seen that in the environment with the SNR of 15 dB, as the source signal spectrum aliasing gradually intensifies, the separation performance of the proposed algorithm and the comparison algorithms are reduced to varying degrees. However, for signals with the same BPSK modulation (Figure 13), the average SDR of the separated signals by the proposed algorithm is increased by 1.05 and 2.35 dB compared with the comparison algorithms. For signals with different modulation (Figure 14), the average SDR of the separated signals by the proposed algorithm is increased by 0.68 and 2.48 dB. The BER is significantly lower than that of the comparison algorithms. As a result, the anti-aliasing performance of the algorithm in this paper is excellent.  
Figure 11: The waveforms of separated signals.  
Figure 12: The waveforms of separated signals after coherent demodulation.  
Figure 10: The waveforms of mixed signals.