Fast surface signal extraction method for photon point clouds with strong background noise without prior altitude...

Area of Science:

• Photonics and Remote Sensing
• Signal Processing
• Spacecraft Technology

Background:

• Extracting target echo photon signals from noisy point clouds is difficult without geographic data.
• High background noise rates (BNR) challenge single-photon lidar performance, especially from spacecraft.

Purpose of the Study:

• To develop a fast and accurate surface detection method for single-photon lidar data.
• To improve the extraction of surface photon signals in environments with high background noise and varying terrain.
• To enable real-time processing of massive photon point clouds for applications like deep space navigation.

Main Methods:

• A novel DDA-KF algorithm combining an improved density-dimension algorithm (DDA) and Kalman filtering (KF).
• Application to photon signals with high background noise rates (BNR) from spacecraft platforms.
• Evaluation of adaptability to strong background noise and terrain slope variations.

Main Results:

• The DDA-KF algorithm demonstrated good adaptability to strong background noise and terrain variations.
• The method achieved real-time processing capabilities for massive photon point clouds.
• Effective extraction of surface photon signals was achieved without prior altitude information.

Conclusions:

• The DDA-KF algorithm provides a practical solution for single-photon lidar in challenging environments.
• This method enhances performance in deep space navigation and other applications with high background noise.
• The research facilitates improved signal extraction for large-scale photon point cloud detection.