## 3 Results  
To verify the effectiveness of the proposed seamless IM-NN fusion structure, a self-constructed unmanned vehicle platform is applied, which is equipped with a CMP10A 10-axis attitude sensor. Figure 4 shows the experimental setup and hardware structure. The 10-axis attitude measurement device incorporates a three-axis geomagnetic sensor and IMU. The attitude sensor consists of an accelerometer, a gyroscope, a magnetometer, and a barometer. It has a resolution of 0.0005 (g/LSB) for the acceleration, 0.061 (\({}^{\circ}\)/s) for the gyroscope, and 0.0667 (mGauss/LSB) for the magnetometer. A computer receives and displays experimental data and runs the program to achieve the fusion process of the heading angle. The reference heading angle of the test system comes from a high-precision compact optical fiber integrated navigation system (model: SPAN-KVH1750), which has a heading accuracy of 0.035\({}^{\circ}\) and a sampling frequency of 100 Hz (root mean square, RMS). Experiments were conducted to confirm the algorithm's superiority, and six representative methods were proposed for comparison.  
Figure 3: (**a**) Block diagram of integrated INS/MNS navigation based on IM-NN training mode. (**b**) Block diagram of integrated INS/MNS navigation based on IM-NN prediction model.  
* Method 1: Reference system.
* Method 2: Pure INS algorithm.
* Method 3: EKF INS/MNS algorithm.
* Method 4: CKF INS/MNS algorithm.
* Method 5: Adaptive CKF INS/MNS algorithm.
* Method 6: Adaptive CKF-LSTM INS/MNS algorithm.  
The test results are analyzed later in this section.  
* Verification of the Adaptive CKF Algorithm  
The test was conducted on 6 April 2023, at 0940 h at 3 College Road, Taiyuan, China (112.45\({}^{\circ}\) E, 38.02\({}^{\circ}\) N). The CMP10A 10-axis attitude sensor was mounted on the UV in conjunction with the reference system. The experiment began by validating the EKF fusion performance of the pitch and roll information from the IMU (comprising a three-axis gyroscope and three-axis accelerometer). Subsequently, the EKF algorithm fusion process was performed for the collected three-axis gyroscope and three-axis accelerometer data. The comparison effect is shown in Figure 5a,b. The precision and stability of pitch and roll output have been substantially enhanced.  
Figure 4: Experimental equipment.  
Figure 5: _Cont._  
During normal geomagnetic conditions, the tri-axial magnetometer data obtained by the device were fused with the experimental attitude output results in the initial stage using three distinct fusion algorithms for comparison. The heading fusion strategy's performance was assessed by plotting the heading angle curves of each of the five methods for contrast, as depicted in Figure 6a,b.  
Figure 5: (a) Pitch angle in INS. (b) Roll angle in INS.  
Figure 6: _Cont_.  
The two groups of predicted data periods were used as the interruption period for the MNS. Method 4 processed data during the MNS normal period, while Method 5 processed forecasted data during the MNS interruption period. Figure 8a,b display the heading angle curves and heading angle error curves of the four ways to assess the performance of the seamless fusion strategy during geomagnetic disruption.  
Figure 8: \(Cont.\)  
Figure 7: (a) LSTM-based training and validation analysis of pre-group data. (b) LSTM-based training and validation analysis of post-group data.  
Throughout the interruption, the error progressively grew as only INS was available for the heading analysis, and the LSTM prediction model seamlessly filled the gap caused by the interruption. Table 2 shows the comparative analysis of the statistical characteristic values of Method 6 and pure INS. The results demonstrated that the seamless IM-NN fusion structure effectively suppressed the INS error and improved the heading accuracy in the presence of MNS interruption, outperforming the other four methods.