## 1 Introduction  
In recent years, obstacles between communication transmitters and receivers have posed significant challenges to their high-quality communication, which even results in the disconnecting of communication link. Unmanned Aerial Vehicles (UAVs) are widely utilized to alleviate the above problem due to the characteristics of flexible deployment, all-weather application, and low cost. The term UAV mainly refers to radio remote control technology and automatic program control technology that enables unmanned aircraft. Due to the limited technology and economic requirements, UAVs were initially only used in military applications. Recently, the manufacturing costs have gradually decreased, and UAVs have begun to enter people's daily lives. UAV sensor networks are becoming increasingly important because of their low cost, comprehensive coverage, flexibility, and availability of small-scale sensors. However, in addition to these effective applications that greatly benefit daily life, some safety hazards and social problems also need to be solved. For example, many civil UAV flights are \"black flights\", which means they are flow without an airworthiness certificate from the civil aviation administration or a license issued by the relevant authorities. This makes the necessary communication subject to human-induced interference, resulting in harmful incidents such as interference with normal aerospace communication by non-cooperative UAVs. Therefore, avoiding such interference through reasonable and effective methods is a major topic that needs to be studied. The first step in this topic is to monitor as accurately as possible whether there is non-cooperative UAV interference and whether the interference affects the normal flight of cooperative UAV. In terms of communication, monitoring channel quality and non-cooperative UAV can be achieved by analyzing and comparing the received signals of receivers for evaluation. Channel quality evaluation is a key technology for UAV wireless communication systems, and the signal-to-noise ratio (SNR) is a significant metric to assess the channel quality of UAV communication [1; 2; 3; 4; 5; 6]. SNR evaluation plays an important role in many wireless communication systems, such as signal detection, power control, adaptive modulation and demodulation, etc. Obtaining SNR evaluation results accurately in real-time can improve system performance to some extent. In addition, SNR evaluation technology has also been widely used in fields such as electrostatics and the Internet. In the transmission of modulated signals, SNR generally refers to the ratio of the average power of the carrier signal at the output end of the channel, that is to say, the average power of the noise in the channel at the input end of the receiver. Because of its direct correspondence with the communication bit error rate, SNR becomes an effective evaluation standard for the reliability of the channel state and communication quality; it is necessary prior information for many signal processing algorithms or technologies. The real-time performance and accuracy of SNR evaluation directly affect the performance of communication systems [7; 8; 9; 10; 11]. Generally, the currently available SNR evaluation algorithms can be divided into two categories, one is the data-aid SNR evaluation algorithm that requires some prior information as an auxiliary condition (e.g., auxiliary data, modulation method, etc. ), and this type of algorithm will increase the overhead of the communication system to varying degrees; the other is the non-data-aid SNR evaluation algorithm that does not require the knowledge of auxiliary information. The non-data-aid evaluation algorithm ensures the stability of the evaluation results, and this type of algorithm generally employs some observation data [12]. Specifically, the traditional SNR evaluation methods contain the maximum likelihood ratio-based SNR evaluation algorithm [13; 14], Second-order and Four-order Moments (M2M4) algorithm [15], etc.