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.