The increasing risk posed by space debris highlights the need for accurate localization techniques. Spaceborne single photon Lidar (SSPL) offers a promising solution, overcoming the limitations of traditional ground-based systems by providing expansive coverage and superior maneuverability without being hindered by weather, time, or geographic constraints. This study introduces a novel approach leveraging non-parametric Bayesian inference and the Dirichlet process mixture model (DPMM) to accurately determine the distance of space debris in low Earth orbit (LEO), where debris exhibits nonlinear, high dynamic motion characteristics. By integrating extended Kalman filtering (EKF) for range gating, our method captures the temporal distribution of reflected photons, employing Markov chain Monte Carlo (MCMC) for iterative solutions. Experimental outcomes demonstrate our method s superior accuracy over conventional statistical techniques, establishing a clear correlation between radial absolute velocity and ranging error, thus significantly enhancing monostatic space debris localization. © 2024 Optica Publishing Group (formerly OSA). All rights reserved.