## 1 Introduction  
Electromagnetic compatibility (EMC) testing works to ascertain the emissions and susceptibility of equipment, which play a crucial role in the communication, radar, and aerospace fields and the automotive industry. At present, EMC measurements can be implemented in open-area sites [1], screened rooms [2], and anechoic rooms [3]. Among them, the screened room surrounded by all-metal materials can be isolated from external electromagnetic interference to a high degree. Anechoic rooms can improve the measurement performance by lining the walls and ceiling with absorbing materials. Suffering from immovability, neither of them can be applied for in situ measurement or engine running status measurement. Open-area testing takes EMC measurements on a flat metalized ground area, and the observed signal is the superposition of the directly arriving signals and their reflection from the ground. Compared with the screened room and anechoic room testing, open-area testing is more flexible, which is suitable for in situ measurement or engine running status measurement, but it suffers from non-negligible external electromagnetic interference.  
From the aspect of signal analysis, external electromagnetic interference can be divided into two kinds, frequency-domain separable interference [4] and frequency-domain nonseparable interference [5]. For the former one, the interference of different equipment has different frequency regions and can be recognized. For the latter one, the interference of different equipment occupies the same frequency region, which makes it difficult to separate. Since most of the man-made interference is with a single-frequency or narrow-frequency occupancy, the frequency-domain separable interference plays a dominant role in open-area EMC testing.  
After separating different signals in the frequency domain, segmentation can be applied for further processing, which can generally be divided into two classes, semantic segmentation [6] and instance segmentation [7]. Semantic segmentation labels all image pixels with a set of object categories, and targets in the same category can not be distinguished. Recently, segmentation methods based on deep learning have achieved great progress and have been applied in different fields successfully.  
In this paper, an environmental interference suppression method named the EMC environmental interference suppression algorithm (\(E^{2}ISA\)) is proposed. Firstly, a hybrid segmentation algorithm that combines U-net with a classical connected component analysis algorithm is proposed to separate different signals in the time-frequency domain from the background. Then, a group of feature similarity decision criteria, named SLTVSFs, are designed to reflect the changes in the statistics of each signal with time. Finally, the KL divergence, JS divergence, cosine similarity, and Pearson correlation coefficient are calculated for near-far site recognition.  
The organization of this paper is as follows: In Section 2, the system configuration of our open-area EMC testing method is introduced briefly. In Section 3, the EMC environmental interference suppression algorithm is proposed and analyzed in detail. In Section 4, a group of experiments with real data are carried out to verify the performance of the proposed algorithms. A summary is given in Section 5.