Abstract:To address the issue of electromagnetic interference and noise in magnetic measurement sequences for indoor magnetic field positioning, an improved particle swarm optimization variational mode decomposition method combined with wavelet thresholding is proposed for magnetic field signal processing. Based on the study of magnetic measurement sequence signal characteristics, an improved PSO algorithm is utilized to optimize the number of modes and penalty factors in VMD. Correlation coefficient analysis is conducted on the decomposed modes, and an adaptive wavelet thresholding improvement is introduced based on normalized envelope entropy and wavelet decomposition level parameters to process signals with different frequency bands and noise levels, thereby achieving mode selection and adaptive noise reduction. Simulation results demonstrate that the proposed method outperforms PSO-VMD reconstruction and PSO-VMD with fixed wavelet thresholding in terms of SNR, similarity, MAE, and MSE. In real magnetic field sequence measurements, compared with the reference methods, the proposed method improves SNR by 6.7001 dB and 4.6568 dB, increases similarity by 1.306% and 0.5568%, and significantly reduces MAE and MSE. The experimental results in an indoor corridor environment further validated the effectiveness of the proposed method. This method can effectively enhance the quality of magnetic field signals and is suitable for the analysis and processing of indoor magnetic measurement sequences.