Singularity detection of noisy signals based on two wavelet denoising algorithms

As the inevitable noises exist in actual application, the common method (MTMM) gets unacceptable result of singularity detection. In order to get more accurate singularity detection, wavelet transform shrinkage and spatially selective noise filtration methods are respectively utilized to denoise the corrupted signals. Then, the wavelet transform are applied to the two independent preprocessing signals, following that the modulus maxima are extracted for them. Based on the differences of modulus maxima dominated by noise and true signal, modulus maxima lines are picked up for the two disrelated sources. Meanwhile a proper fused and weighted manner is adapted to obtain reliable modulus maxima lines, which are directly corresponding to numbers and positions of singular points. Finally, several simulation experiments validate that the proposed algorithm obtains acceptable result of singularity detections for noisy signal, and achieves better performance over other two methods in noisy condition.