Weak Life Signal Detection Based on Wavelet Transform and Threshold De-noising Theory

Abstract

When natural disasters and man-made disasters occur, people hope to search and rescue as soon as possible to the survivors. The radar echo signal is very weak and hard to extract in the life signal detection. In order to solve this problem, a new method based on wavelet transform and threshold de-noising theory is proposed. Through the studies of wavelet threshold de-noising method, the use of it in weak life signal de-noising in strong noise background, and the verification of simulation by Matlab. The results show that the wavelet threshold de-noising theory and method can effectively eliminate the noise signal from the weak life signal, which is an effective de-noising and extracting method, which is suitable for weak life signal detection. If this method is applied to the signal analysis of detection instruments, it should play an important role in the detection field of weak life signal.

The Enhancement of Weak Bearing Fault Signatures by Stochastic Resonance with a Novel Potential Function

“…How to detect and extract weak signals from a strong noise background is thus an important field [1][2][3][4][5][6][7]. Traditional signal processing methods include empirical mode decomposition (EMD) [8], ensemble empirical mode decomposition (EEMD) [9,10], maximum correlated kurtosis deconvolution (MCKD) [11], wavelets [12][13][14], etc. Although the above methods can filter out the interference noise energy to obtain useful signals to some degree, they also inevitably weaken the energy of the useful signals, and the signal-to-noise (SNR), which is the evaluation index of the improvement about the quality of the output signal relative to the input signal, so the SNR obtained by those methods is low.…”