The application of empirical mode decomposition in target-starting sound detection

When interfered suddenly, the regular signal would appear weak mutation, which is typical transient signal. Itpsilas difficult to detect the start and terminative time by conventional time-frequency analysis methods. Empirical mode decomposition (EMD) is a nonlinear analysis method, which can emphasize signalspsila instantaneous characteristics and can decompose multi-component signals into different intrinsic mode functions. Simulation results show its validity in mutation time detection. When engine is started in some time, we are interested in its starting time and azimuth. The target-starting is a transient signal. Traditional azimuth estimation methods canpsilat detect and estimate its azimuth only using a vector sensor, especially when the SNR (signal noise ratio) is low. Combining EMD with traditional vector signal processing theory, mode acoustic intensity averager can realize the transient signalspsila detection and azimuth estimation. The experiment results show that this method can decompose the target-starting sound and platform interference into different IMFs, so as to detect the starting time of the target-starting sound and estimate azimuth effectively.