Wavelet-Based Adaptive Detection of Magnetic Anomaly Signal Contaminated by 1/f Noise

Abstract

Magnetic anomaly detection is a passive method for detection of a ferromagnetic target, and its performance is often limited by external noise with a power spectral density of 1/fa, (0<a<2). In consideration of this kind of noise is non-stationary, self-similarity and long-range correlation, an effective adaptive detection based on the wavelet transform is proposed in this paper. The discrete wavelet transform of the noisy signal is chosen as the inputs of the adaptive whitening filter, and then decomposed by the orthonormal basis functions (OBFs) and the energy signal was taken out for threshold detection. The simulation results show that the algorithm improves the effect for detecting weak magnetic anomaly signal contaminated by 1/fanoise.

A Multi-Task Learning for Submarine Cable Magnetic Anomaly Recognition

“…Under the condition of Gaussian white noise, the OBF detector has the best detection performance, but 2 of 17 in the presence of non-Gaussian white noise, the detection performance is greatly affected. In view of the dependence of OBF detector on environmental noise, researchers have successively proposed whitening filtering [7][8][9] and wavelet decomposition [10,11] methods to suppress the environmental noise, further improving the practicability of the OBF detection method. The dependence of OBF detector on Gaussian white noise greatly limits its performance, so other detection methods based on target characteristic signals have also been studied, such as stochastic resonance [12] and principal component analysis [13] methods.…”