The variational modal decomposition (VMD) method has a better modal decomposition effect in the denoising of ultra-low altitude magnetic anomaly signals, however, it needs to rely on the manual setting of the penalty factor and the modal decomposition parameters in practical detection, and the magnetic anomaly signals are weak and the environmental noise is complex. Aiming at the above problems, this paper proposes an improved particle swarm optimized variational modal decomposition (IPSO-VMD) combined wavelet threshold denoising method. Firstly, by introducing the adaptive inertia weights and learning factor strategy, and utilizing the arrangement entropy as the fitness function, the self-adaptation to the above parameters is realized. After that, the optimal parameter combination is used to decompose the signal, and wavelet threshold denoising is applied to the abnormal components. Finally, the signal is reconstructed and the denoised signal is obtained. The simulation experiment results show that the method improves the SNR by about 9.44 dB compared with other methods, and the correlation coefficient reaches about 0.74, obtaining a good denoising effect. The field experiments show that the magnetic anomaly location of the measured signal after denoising is obvious, which effectively reduces the interference of environmental noise on the signal and shows the potential of application in the exploration of ultralow altitude magnetic targets in the field.