## 1 Introduction  
Currently, the possession and level of development of missile weapons have become increasingly important for assessing a country's military and technological capabilities. Within the various components of a missile system, the air data system (ADS) is primarily responsible for measuring atmospheric parameters during missile flight and calculating the relevant flight parameters required for guidance and control [1, 2]. However, sensors of the ADS may experience failures due to harsh flight environments, such as unexpected impact, electromagnetic interference, and component anomalies, leading to the output of incorrect flight parameters. This may result in errors in subsequent control and guidance processes, potentially causing catastrophic accidents [3, 4]. Therefore, conducting fault diagnosis for ADSs is of significant importance, which requires the timely detection and identification of faults following their occurrence and plays a crucial role in ensuring missile reliability and mission completion. In the field of aircraft fault diagnosis, model-based approaches and data-based approaches are two main strategies presently used [5, 6]. Model-based diagnostic methods generate relevant residuals through parameter estimation [7], state estimation [8], equivalent space methods [9], etc. These residuals are then used for fault diagnosis via approaches such as the chi-squared test [10, 11, 12, 13] and optimal parity vector method [14, 15, 16]. However, it is challenging to establish the ADS model to some extent, and traditional residual generation methods based on atmospheric data calculation equations fail to comprehensively consider various environmental interference factors in practical situations. This often creates difficulties in distinguishing between normal and faulty conditions, resulting in decreased diagnostic accuracy. To represent fault features more clearly, [PERSON] et al. [17] introduced an augmented fault filter into a kinematic model that includes atmospheric parameters, enabling fault detection, isolation, and estimation. However, this requires setting fault thresholds separately for each sensor, which results in a lack of adaptability. Building upon this, a dual Kalman filter with fault/no-fault states is designed in reference [18], where the occurrence of faults is judged through the different conditional probabilities of two filters allocated by their respective innovation and covariance, and this method achieves good robustness. Furthermore, references [19; 20; 21; 22] have made improvements to model establishment and diagnostic methods in practical scenarios based on different strategies. Nevertheless, model-based approaches require complex designs to extract reliable fault characteristics, and often suffer from worse accuracy and real-time performance in the diagnostic process when relevant conditions are changeable, which restricts their application in the fault diagnosis of ADSs. With the advancement of artificial intelligence technology, data-based methods utilized for fault diagnosis have emerged, including support vector machines [23; 24] and neural networks [25; 26; 27]. These methods can extract fault features from large amounts of data without the need for precise models, and have achieved promising results in the field of fault diagnosis. [PERSON] et al. [28] constructed a single-dimensional fully convolutional neural network characterized by multi channels for fault detection. This technique extracts features at various scales, capturing both local and general information of the sensor signal sequence to achieve a comprehensive and accurate classification. [PERSON] et al. [29] developed a fault diagnosis model based on a bi-directional gated recurrent unit (GRU) and multi-layer perceptron (MLP). By combining the data attention mechanism and spatial attention mechanism, the model captures the semantic relationship between monitoring variables and faults, enabling fault detection and identification for autonomous underwater vehicles (AUVs).