## 4 Experiment  
For this experiment, we selected an expansive outdoor area with open space, a uniform and stable geomagnetic magnetic field, and no interference sources. The equipment utilized in the field experiment is illustrated in Figure 16. The experimental setup comprised a three-axis fluxgate magnetometer, an electronically controlled small car, and a magnetic acquisition device. Within the electronically controlled car, a Rb-Fe-B magnet and a six-axis accelerometer were positioned. The car could move at a uniform velocity, with velocity adjustments possible. The orientation of the magnetic moment of the magnet and the fluxgate magnetometer is depicted in Figure 16 (3). (Assistance from a person was required when the speed was low.) The magnitude of the magnetic moment was 330 A \(\cdot\) m\({}^{2}\). The car moved at a lateral distance of \(Rm=2\) m from the three-axis fluxgate magnetometer, maintaining uniform speeds of 0.5 m/s, 1.5 m/s, and 2.7 m/s, respectively. Figure 17 displays the total field and the three components of the magnetic anomaly measurement signals obtained after calibration. Figure 16: Environment and equipment of experiment. Considering velocity error, we conducted experiments at each velocity five times. To gauge the velocity at each time interval, we employed an accelerometer and analyzed the data with a velocity error within a 10% range, as presented in Figure 18.  
By conducting comprehensive tests and precise accelerometer measurements, it was possible to gain deeper insights into speed variations in practical situations and to minimize the influence of random error. This data acquisition approach offers enhanced reliability and facilitates a more accurate analysis. The adaptive scale factor method ultimately aims to elevate the precision and dependability of detection for an unknown magnetic target motion state. Leveraging the experimental data, we performed orthogonal basis decomposition of the magnetic anomaly total field signals and determined the optimal scale factor \(\beta\). This process yielded energy measurements from orthogonal basis decomposition at various velocities, as depicted in Figure 19.  
Figure 17: The total field and the three components of the magnetic anomaly measurement signals obtained after calibration: raw data after sensor calibration. Figure 18: Sampling points of different velocities. In the comparative analysis, we used the OBF with a scale factor \(\beta=0.075\) for the speed of 1.5 m/s and compared it with the conventional OBF detection method. Figure 20 shows a comparison of the results. The following conclusions can be drawn from the data in Figure 20:  
1. In the experiment, the peak energy value that could be achieved with the traditional approach was 983 nT; the adaptive scale factor \(\beta\)-OBF detection obtained a peak energy value of 1157 nT. In comparison, the adaptive scale factor approach improved the peak value by 17.7%. 2. Using the OBF detection method with the conventional approach, the deviation t2 at the peak near the CPA was 0.187 s, while the deviation t1 of the scale factor \(\beta\)-OBF at the peak near the CPA was 0.086 s, which was an increase in accuracy 54.1%. The adaptive scale factor method proposed in this paper shows obvious advantages in improving magnetic anomaly signal detection. The experimental results show that the adaptive scale factor method significantly improves the value of peak energy compared to that of the conventional approach. In addition, the adaptive scale factor method achieves better performance in terms of the deviation at the peak value near the CPA. The effective  
Figure 19: Energy measurements from orthogonal basis decomposition at different velocities. Figure 20: Comparative analysis of T\(-\)OBF and \(\beta\)-OBF at 1.5 m/s: evaluating performance and results. ness and precision of the proposed method in practical applications were verified through experiments.