Space Debris Impact Acoustic Emission Signal Identification Method In Noise Environment

As technology advances, people’s attention on the aerospace industry is also getting higher and higher, and the space exploration is increasing, but these also bring a certain influence to the environment of space. Because human space activities will inevitably produce a very large number of "space junk", also known as space debris, that can extremely threat the spacecraft. The space station is a large spacecraft to provide working and living in the space environment for the astronauts. It takes a long time to run in space. In this context, we need to develop a system can perceive the space debris impact, namely ‘space debris in orbit perceptual system’. The system can sense, locate the impact of the spacecraft, and evaluate the impact of the situation. In the running process of the on orbit sensing system, a variety of unexpected and persistent interference signals can be collected, which leads to the occurrence of false. Therefore, in view of this situation, it is necessary to identify the non homologous signals, and identify the event of the space debris in orbit.In this paper, we first compare and analyze the waveform and frequency spectrum of the super high speed impact signal and jamming signal of debris cloud. By using the concept of mutual correlation, compare and analyze the peak value of the cross-correlation between the standard signal and the debris cloud super high speed impact signal and the interference signal. Intrinsic mode function of various signals of acoustic emission signal processing is the foundation of the next step, based on the empirical mode decomposition method of the signal, the obtained spectral cross-correlation peak were compared and analyzed. Because the HHT transform has very good effect on processing the non-stationary signal, based on the empirical mode decomposition, the intrinsic mode function of the signal is transformed to the Hilbert transform, and then the correlation processing is carried out. By comparing their spectral correlation, and proposing an improved algorithm. The following conclusions are obtained:(1) Debris cloud ultra high speed impact signal and the interference signal spectrum has overlapping parts, only from the signal waveform and spectrum of the signal source nature of the distinction is not high;(2) By means of the method of cross-correlation between the frequency of the standard super high speed impact acoustic emission signal and the sensing system, the super high speed impact event is calculated by calculating the cross-correlation function peak value;(3) The signal were empirical mode decomposition. One by one comparison of intrinsic mode functions with different frequency components of the peak of the cross-correlation function. Recognition of super high speed impact events by the number level of each component spectrum correlation;(4) By using the HHT transform and the HHT transform of the reconstructed signal, analysing the cross correlation performed on the selected intrinsic mode functions respectively, proposing 2 improved algorithms, then verifying the reconstruction of the signal HHT transform method can be better on identify the ultra-high speed impact event.