## 5 Conclusions  
This paper proposes a fault diagnosis method for a missile air data system based on the UKF and the Inception V3 network, which extracts innovation and residual sequences from the UKF model of the ADS as the input of the Inception V3 network, and comparative experiments demonstrate the advantages of the proposed method. Based on the analysis and experiments, the following conclusions can be drawn:  
1. Based on the working mechanism of the ADS and the missile kinematic equations, the UKF model of the ADS is established to provide a more accurate description of the system. This avoids the problems of distinguishing faults from changes in flight states that are commonly encountered in purely data-driven methods.
2. Instead of using hypothesis testing methods, such as chi-squared tests in the diagnostic process, the proposed method extracts innovation and residual sequences from the UKF model to amplify fault features and employs them as inputs to the neural network, which better reflects fault conditions compared to traditional methods based on filtered data or innovation data.  
Figure 14: Error bar plot according to 5-fold cross validation results.  
3. The Inception V3 network is designed, which extracts features in parallel at multiple scales while ensuring high computational efficiency by leveraging the principles of sparse matrices. Additionally, the network incorporates the lightweight ECA attention mechanism to further enhance the diagnostic performance.  
The proposed method, through the integration of model and data, effectively addresses the limitations of model-based approaches relying on hypothesis testing or subjective settings, as well as the performance degradation of data-driven methods under variable environmental conditions, which provides a new train of thought for the fault diagnosis of missile air data systems.  
This study still has some limitations, which are reflected in the relatively ideal setting of fault modes, and it may deviate from actual conditions and fail to consider all possible scenarios. Additionally, there is room for improvement in the establishment of the UKF model and Inception V3 network. In further research, we will delve into more complex scenarios and faults for missile air data systems. This may involve considering the mechanism and modeling of the pitot tube, angle of attack, and sideslip angle sensors, for example, as well as constructing a computational model for the air data computer. Meanwhile, this study focused on cruise missiles as the experimental subject, and future considerations include validating the proposed methods for other types of missiles. Moreover, by incorporating methods such as adaptive strategies and optimization algorithms, we hope to further optimize the UKF model and Inception V3 network to enhance accuracy and reliability.  
Conceptualization, Z.W., Y.C. and B.J.; methodology, Z.W.; validation, Z.W., Y.C. and K.G.; formal analysis, Z.W.; investigation, Z.W. and Y.C.; data curation, Z.W. and H.H.; writing--original draft preparation, Z.W. and Y.C.; writing--review and editing, Z.W. and B.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (No. 2023 YFB3307102) and National Natural Science Foundation Integration Project (No. U22B6001).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. (privacy restrictions of the employed model).  
The authors declare no conflicts of interest.