Adaptive hybrid ranging and trajectory extraction for single-photon lidar space-debris detection.

With the exponential growth of on-orbit space debris, timely detection and monitoring are essential for space situational awareness. Space-borne single-photon Lidar (SSPL) offers day-and-night operability and high pointing agility. Debris observations are often characterized by extremely low signal-to-noise ratios (SNR) and signal loss, which hinder reliable target discrimination and the extraction of distance trajectories. We present a variational Bayesian adaptive Kalman filtering with the Spatio-Temporal Association (VBAKF-STA) algorithm for space-borne, thousand-kilometer-range observation, integrating a variational Bayesian adaptive Kalman filter (VBAKF) with a local-measurement module to identify signal photons and reconstruct distance trajectories. Simulation results demonstrate that the VBAKF-STA recovers complete and accurate distance trajectories when SNR < -18 dB with intermittent signal loss and outperforms conventional baselines in terms of accuracy and robustness. The results support an efficient and resilient space-borne data-processing scheme for long-range, active detection of non-cooperative space targets at the thousand-kilometer scale.