## 1 Introduction
However, these methods rely on the support of sufficient data, and missile mission environments are complex, making it difficult to differentiate between faults and variations in flight states solely through data. Moreover, challenges still exist in diagnosing minor faults under the influence of noise and other interferences, which may result in worse performances in practical engineering applications. Due to the inherent difficulty of accurately diagnosing faults in sensors and the unique nature of utilizing redundant information [30; 31], models can be established based on the characteristics of multiple sensors. For example, various integrated navigation system models can be built with an inertial navigation system (INS) as the benchmark [32; 33; 34], combined with data-based strategies, such as neural networks, to achieve fault diagnosis. [PERSON] et al. [35] established a Kalman filter model for an electric multiple unit (EMU) braking system, which generated integrated filtering data with higher signal quality, and then the time domain parameters of the relay valve input and the output pressure signal are input into the SVM, achieving high diagnostic accuracy under small sample conditions. Nonetheless, it is still data-based in essence, and although it enhances the signal quality, it still faces difficulties in distinguishing between faults and variations in flight states through the measurement signals themselves. To overcome this challenge, one approach is to extract data from the model calculation process that can reflect fault conditions and input them into the neural network for fault diagnosis. This can effectively address the issues faced by data-based methods, because certain data calculated by the model under normal conditions exhibit sequences fluctuating around zero, while faults significantly disrupt this characteristic [36; 37]. Such data are ideal input features for the neural network, which is able to significantly improve diagnostic results. [PERSON] et al. [38] proposed a fault diagnosis algorithm based upon long short-term memory (LSTM) for the Strapdown Inertial Navigation System (SINS)/Ultra-Short Baseline (USBL)/Doppler Velocity Log (DVL)/Depth Gauge (DG) integrated navigation system. It extracts normalized residual sequences generated by the Kalman filter model, then inputs this sequence into the LSTM network to monitor the sequences, which outperforms the traditional chi-squared detection method in respect of speed and accuracy. Such a method still has potential for improvement, yet the thought of combining models with neural networks is worth further exploration. Building upon the aforementioned research, aiming at enhancing the diagnostic effectiveness of ADSs under changing conditions, this article proposes an innovative method based on the UKF and Inception V3. The UKF (unscented Kalman filter) is an improvement on the Kalman filter, and it has advantages in nonlinear systems due to its ability to approximate probability distributions. It is particularly suitable for complex and strongly nonlinear systems, like the ADS. Not only does the UKF model allow the estimation of the normal state, but also certain information obtained during its computation process can reflect fault characteristics effectively. The Inception model is an extension of the convolutional layer in terms of depth and width. It constructs a fundamental neural unit structure, which uses convolution or pooling operations with different scales of convolutional kernels and concatenates the features along the dimension, to build a network architecture that maintains network sparsity and high-performance computing, and Inception V3 improves upon the specific structure to further reduce parameter calculations. By combining UKF with Inception V3, the fault characteristics will be more prominent, and it compensates for the limitations of the methods of the model or data.