Abstract

The proliferation of space debris in Low Earth Orbit (LEO) poses a growing threat to operational satellites, requiring robust surveillance and tracking systems. Radar is a primary technology for monitoring these objects; however, standard tracking algorithms often degrade when measurements are sparse, corrupted by non-Gaussian noise, or available only as short tracklets. Traditional methods, such as the Unscented Kalman Filter (UKF), rely on explicit physical and statistical models that may struggle to converge under highly nonlinear dynamics, uncertain initialization, and non-ideal sensor perturbations. In this study, we propose a hybrid deep learning framework for learned one-step tracklet filtering of radar measurements. The architecture consists of a stateful Gated Recurrent Unit (GRU) layer followed by one-dimensional Convolutional Neural Network (1D-CNN) layers, complemented by variable-specific preprocessing strategies, including residual learning for range and relative pivoting for azimuth, to handle the scale disparities and heterogeneous behavior of radar observables. This design combines the ability of GRUs to model temporal dependencies with the effectiveness of CNNs in extracting local features for signal denoising. The method is validated on synthetically generated LEO trajectories with realistic orbital perturbations and tunable radar noise profiles, including Gaussian noise, impulsive spikes, transient degradation, and state-dependent perturbations. Compared with EKF- and UKF-based analytical baselines, the proposed model achieves lower filtering error and improved robustness under severe non-Gaussian disturbances. Stress testing further shows that the network can reject non-physical sensor anomalies without requiring long initialization warm-up phases, making it suitable for sparse short-tracklet processing in synthetic SST benchmark scenarios.

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