Segmentation Processing Technology for Weak Signal Data in Lunar Laser Ranging

When performing Lunar Laser Ranging (LLR), the signal within the echo data is often submerged by noise. Sometimes, only a fraction of suspected signals can be detected, making signal extraction a challenging task, and even if the suspected signals are successfully extracted, it remains uncertain whether the extracted data accurately represent the real signal. This study proposes a segmented integration signal extraction method. By taking the intervals where the residual signals are present as a priori conditions, time intervals containing suspected signals are identified. The data within these intervals are then integrated and processed, and the characteristic parameters of the data distribution are compared to verify the authenticity of the extracted signals. When processing actual measurement data, a comparison is made with the classical Poisson filtering algorithm. The segmented integration signal extraction method is experimentally confirmed to show better performance when dealing with ranging data containing suspected signals. Based on the characteristic parameters of the data distribution, the existence of real signals can be effectively verified.