Speckle suppression for UAV tracking using single-photon LiDAR with vibrating MMF and ST-DKDE

Area of Science:

• Aerospace Engineering
• Optical Sensing Technologies
• Signal Processing

Background:

• Low-altitude, slow-speed (LSS) UAVs present airspace security challenges, requiring high-precision, long-range detection.
• Single-photon LiDAR systems offer enhanced sensitivity but suffer from speckle noise due to multimode fiber (MMF), degrading tracking performance.
• Existing methods struggle with non-uniform echo distributions and model mismatch caused by speckle noise.

Purpose of the Study:

• To develop a robust solution for high-precision tracking of LSS UAVs using single-photon LiDAR.
• To mitigate speckle noise in LiDAR systems caused by MMF.
• To enhance the stability and accuracy of aerial target tracking in complex airspace environments.

Main Methods:

• Implemented a transmitter-side fiber vibration strategy to physically suppress speckle fluctuations.
• Developed a spatio-temporal joint dynamic kernel density estimation (ST-DKDE) algorithm for robust photon-intensity recovery.
• Utilized an improved MeanShift-Kalman algorithm for high-precision aerial target tracking.

Main Results:

• Monte Carlo simulations demonstrated over a 90% reduction in average speckle contrast (from 1.1999 to 0.1039).
• Field experiments showed up to an 83% reduction in speckle contrast and an average tracking accuracy of 0.89 cm under overcast conditions.
• Under clear conditions, the method achieved up to 82.6% reduction in mean tracking error and 88.0% reduction in RMSE compared to traditional methods.

Conclusions:

• The proposed fiber vibration and ST-DKDE algorithm effectively suppress speckle noise in single-photon LiDAR systems.
• This approach significantly enhances inter-frame matching and tracking stability for dynamic LSS UAV monitoring.
• The findings provide a robust solution for high-precision, long-range LiDAR-based aerial surveillance in complex airspace.