## 4 Experimental Validation  
### Dataset  
To verify the applicability of the proposed algorithm, a missile system model is built on the Simulink simulation platform, with the BGM-109D cruise missile as the research object. As shown in Figure 7, the missile model consists of 6 modules, including the dynamics and kinematics of the missile, engine, rudder loop, control system, navigation system, and guidance system. The navigation system includes an INS/GPS integrated navigation system and air data system, with the UKF model embedded in the ADS section. Aiming at matching the actual flight scenarios of the missile, the environment model in the missile system adopts the 1976 International Standard Atmosphere model. The gravity model refers to the gravity calculation model in the World Geodetic System. The wind module utilizes the wind shear model and the Dryden wind turbulence model. The projectile parameters are set based on the Tomahawk cruise missile. To evaluate the algorithm's performance under different trajectories, the target waypoints are randomly set within the range specified in Table 2.  
The air data system mainly includes sensors such as the airspeed indicator, angle of attack sensor, and sideslip angle sensor. These sensors may encounter faults caused by factors such as airspeed probe leakage, sensor component aging, and electromagnetic interference [45, 46]. The fault manifestations on the sensor signals include a constant fault, abrupt fault, and gradual fault. Since diagnosing constant faults is relatively easy, this study mainly focuses on abrupt faults and gradual faults in the airspeed, angle of attack, and sideslip angle signals, and the fault manifestations are represented as the step fault and ramp fault, respectively. Denote the unit step function as \(u(t-t_{0})\) and the fault onset time as \(t_{0}\), then the faults can be described as follows:  
Step fault:  
\[f(t)=Au(t-t_{0}) \tag{24}\]  
where \(A\) is the amplitude of the step fault.  
Ramp fault:  
\[f(t)=R(t-t_{0})u(t-t_{0}) \tag{25}\]  
where \(R\) is the slope of the ramp fault.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **X (m)** & **Y (m)** & **Z (m)** \\ \hline Random Range & 8000–12,000 & –50–50 & 1000–2000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Random range of the target waypoints.  
Figure 7: The missile simulation model.  
In this fault setting scenario, without considering compound faults, there are 6 fault types as well as the normal state, which means a total of 7 scenarios. The flight time is set to 50 s, and the fault is randomly added in the range of 20-35 s. The simulation platform generates data points every 0.01 s, resulting in 5000 data points for each simulation. The fault types and settings are presented in Table 3.  
To enrich the dataset and enhance training effectiveness, sliding window is employed for data augmentation. The principle of sliding window is shown in Figure 8. Here, data are collected for 24 sample points (240 ms) following each fault occurrence, with a window length of 20 (200 ms) and a sliding step of 1. This increases the number of samples 5 times, obtaining 2500 samples for each fault scenario in the training dataset and 500 samples in the testing set.