## 1 Introduction
[24] analyzed the mapping relationship between the physiological data (including ECG, respiration, etc.) and pilot control intentions to realize the research on the cognitive state changes of pilots. [PERSON] et al. [25; 26] established an intelligent human pilot model to describe the nonlinear PIO prediction process and identify pilot behavior based on the pilot's visual perception, intelligent decision-making, and force feedback from control receivers to address issues such as the unfavorable interaction of pilot-aircraft systems including pilot-induced oscillations (PIO). [PERSON] et al. [27] proposed a pattern confusion detection framework based on the pilot's intention for UI (user interface) verification. What is more, the automatic system control strategy is abstracted into a continuous intention state. This provides a theoretical basis for the research of pilots' decision-making and the control strategies of automation systems. The essence of intention identification is the problem of pattern recognition. The intention is mainly to use abstract objects (e.g., physiological signals) for the description of specific microscopic behaviors. From the perspective of engineering physiology, scholars have conducted extensive and in-depth research on the pilot's intentions. In these studies, data preprocessing, feature extraction, and pattern recognition methods are adopted to identify the pilot's intentions. They have also generated considerable research results. The importance of intention in the study of a pilot's micro behavior is proved by these research results from different levels. In other words, the intention has a significant impact on flight safety. Feature extraction is generally regarded as an important process in pattern recognition because a fine feature space could make the identification process concise and efficient and the accuracy of the identification results could be improved. What's more, due to the diversity of intentions in nonstationary systems under the interaction of human-aircraft-environment. However, in previous studies, feature extraction is mainly through Shannon entropy, statistical methods such as average error, variance, time domain, and frequency domain [17; 18; 19; 20]. These methods ignore the time-varying evolving nature of intentions. At the same time, these also limit the process of automatic training and the analysis of the identification model. In addition to feature extraction, the establishment of an identification model is also an important stage. Methods proposed by scholars for intention identification mainly include artificial neural networks, support vector machines, Gaussian distribution, and other methods [18; 19; 20; 21; 22; 23]. These methods lay the foundation for intention identification and have made great achievements. However, they still have certain problems. For example, under a large number of samples, most of these methods will have problems, such as slow training speed, uncertain model parameters, and difficulty in optimization. In this paper, outcomes of the above-mentioned feature extraction and identification methods are referenced and make up for their shortcomings. In this study, the airfield traffic pattern is taken as an example. The pilot's dynamic physiological data, such as electrocardiogram (ECG), respiration (RESP), and electrodermal activity (EDA), are collected by the wireless physiological acquisition system in simulated flight. Here, the extended symbol aggregation approximation method and the intelligent icon method are adopted to extract and analyze the complex physiological signal features. These features provide sufficient information for further identification. Then, based on the pilot intention characteristic variables, the crow search algorithm (CSA) is adopted to optimize the parameters of the extreme learning machine (ELM). The pilot's intention identification model based on CSA-ELM is constructed.