The Application Of Hilbert-Huang Transform In Transient Signal Processing

The underwater acoustic transient signals such as the splash sound of targets' water entry and underwater collision noise have been the focus in underwater acoustic field, and it's very important to detect and estimate the azimuth of these signals in underwater warfare field, especially when local interferences are strong. In addition, when the signal to noise ratio (SNR) becomes lower, the correlation peak derived by the traditional cross-correlation method is not very obvious and in correlation peak detection, it is prone to false alarms. In this dissertation, as a new method of time-frequency analysis, the Hilbert-Huang Transform (HHT) has been applied to the transient signals' detection, and the algorithm was studied that how to detect and estimate its azimuth in strong interferences. The cross-correlation azimuth estimation method was improved, and the target's azimuth can be estimated effectively in lower SNR by this improved method.This dissertation dissertated the algorithm of HHT and how to realize it in details. Simulation results approved that the empirical mode decomposition (EMD) can emphasize the local instantaneous characters of original signals and decompose the multi-component signals into mono-component signals. Aiming at different actual applications, the principle of intrinsic mode function's (IMF) selection was proposed. The cross-correlation functions were decomposed in this dissertation, and the reconstructed cross-correlation functions were derived by selecting the decomposed IMFs suitably. The correlation peak in reconstructed waveform is more obvious, which can improve the detecting ability. Based on this, the cross-correlation azimuth estimation method was improved, and the improved method can be applied in correlation peak detection in much lower SNR. The azimuth estimation results showed the validity of approved method's in the azimuth estimation.This dissertation studied the character of targets' water entry splash sound, and analyzed its waveform and source level of the impact sound, which provided a theoretical basis for the identification of water entry splash sound and the forecast of impact sound's source level. A vector sensor can measure the pressure and particle velocity synchronously. Based on this, this dissertation combines traditional vector signal processing theory and Hilbert-Huang transform.. Considering actually that it's very familiar to detect the weak instantaneous signals in strong interferences, mode acoustic intensity averager (MAIA) method was proposed in this dissertation. Combining MAIA and the waveform characters, the method can realize detection and azimuth estimation for the targets' water entry splash sound, which can be used for any targets' azimuth estimation dropped into water. It can also monitor instantaneous signals such as underwater collision noise and targets' start-up noise effectively in the strong interferences flatform. The lake experimentation and sea trial result showed this algorithm was feasible and reliable.