Magnetic anomaly detection on wavelet packet and support vector machine

There is still a lot of noise signal redundancy in the empirical frequency range of the vehicle magnetic anomaly detection (MAD) signal, and the extremely weak magnetic anomaly signal is still submerged in noise. This paper proposes a wavelet packet denoising algorithm combined with the denoising algorithm of the upper limit frequency of the magnetic anomaly feature. And because it is difficult to describe the magnetic anomaly signal in the characteristic layer in a single dimension, a multi-feature extraction method is used to process the magnetic anomaly signal. For the denoised magnetic anomaly signals, the dimensionless constants and singular values are extracted to construct feature vectors, and the support vector machine is used to classify and recognize the magnetic anomalies. Finally, through experiments, for the weak magnetic anomaly signal with an amplitude of the only 0.04nT, an accuracy of 80% can be achieved, and a good recognition effect has been achieved.