## 3 Open-Area Environmental Interference Suppression Model
### Feature Similarity Decision Criterion  
Although the actual signals in EMC tests exhibit stochastic properties due to their modulating characteristics or noise effect, the statistics of these stochastic signals might vary gradually with time due to the changes in the distance, working mode, switching, and man-made operations. Thus, it is wise to calculate the statistic of each signal over time, namely the SLTVSFs, in order to better evaluate the non-stationary random signal. With the separated signal time-frequency spectrum, we can calculate the SLTVSFs via the weighted sliding statistics method [23]. Four SLTVSFs are used in this paper, the average, standard deviation, maximum, and bandwidth, which are defined as follows:  
\[\mathrm{SLTVSF}_{\mathrm{mean}}\,=\,\frac{1}{n}\sum_{x_{i}\in\Omega}x_{i}, \tag{2}\]  
\[\mathrm{SLTVSF}_{\mathrm{std}}\,=\,\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x_{i}-\mu) ^{2}}, \tag{3}\]  
\[\mathrm{SLTVSF}_{\mathrm{max}}\,=\max_{x_{i}\in\Omega}\{x_{i}\}, \tag{4}\]  
\[\mathrm{SLTVSF}_{\mathrm{BW}}=f_{\mathrm{max}}-f_{\mathrm{min}}, \tag{5}\]  
where \(\Omega\) denotes the segmented area, \(x_{i}\) denotes a single datum inside \(\Omega\), \(\mu\) denotes the SLTVSF mean, and \(f_{\mathrm{max}}\) and \(f_{\mathrm{min}}\) denote the maximum and minimum frequencies of the region. The moving average is obtained by calculating the average of a datum and its 2D adjacent, which is the most basic statistic reflecting the trend of signal energy changes. The moving standard deviation is obtained by calculating the standard deviation of a datum and its 2D adjacent, which can reflect the randomness of the signals. The moving maximum (minimum) value is the maximum (minimum) value of the signal in the sliding window, which reflects the maximum (minimum) peak value of the signal amplitude in the frequency domain. By using the weighted sliding statistics method, a group of low-pass filtering is imposed  
where \(<\)x,y\(>\) denotes the inner product of two vectors, N denotes the signal that is detected from the near site, F denotes the signal of the near site, and \(\Omega\) denotes the area of a single signal of the near site. KL divergence, also known as relative entropy, is an indicator to measure the matching degree of two probability distributions. The greater the KL divergence is, the greater the distribution difference is and the lower the matching degree is. JS divergence overcomes the asymmetry of KL divergence, which is more accurate in similarity judgment. Cosine similarity is used to evaluate the similarity of two vectors by calculating the cosine value of their angle. When the calculated cosine angle is \(0^{\circ}\), the similarity of the two vectors is the highest. The Pearson correlation coefficient is the quantity of linear correlation of vectors. When the Pearson correlation coefficient is close to \(\pm 1\), the two vectors are mostly linearly dependent. Based on the above similarity criteria, we design a fusion decision logic as shown in Figure 4, which determines whether the signal is a near-site signal, far-site signal, or background signal. Firstly, we need to judge whether the detected signal is a single-frequency signal. If the SLTVSF BW is too large, it must be an external interference signal. Secondly, if the SLTVSF mean of the near site is far larger than that of the far site, it indicates that the signal energy fades rapidly with distance, which means it is more possible it is a near-site signal rather than a far-site environmental interference. Thirdly, if the SLTVSF mean difference between the near and far sites is not large, it is difficult to judge whether it is an external interference signal based on this basis. Because the energy difference of narrow-band external interference signals in the same frequency band may be small and the main difference lies in the energy distribution of the signal, it is necessary to utilize the above similarity criteria of SLTVSFs to make a decision.