Probability Heatmap and Subpixel Regression Network for High-Precision Star Sensor Centroid Estimation

The attitude determination accuracy of star sensors depends heavily on the precision of star centroid estimation. However, complex degradation scenarios—including low signal-to-noise ratio (SNR), intense stray light, and isolated hot pixels induced by single-event effects (SEEs)—drastically degrade the localization performance of traditional centroiding methods, while existing deep learning approaches struggle to balance accuracy, robustness, and computational efficiency. This paper proposes a dual-branch network for star sensor centroid estimation that integrates energy-distribution-aware probability heatmaps with geometric quantization-compensated subpixel regression. A full-resolution architecture preserves the complete energy distribution of star diffraction spots; an improved focal loss mitigates the extreme class imbalance between star targets and background; and a physical centroid loss constrains the reconstructed centroid coordinates. Experiments are conducted on 300,000 simulated star maps covering ideal, typical, and extreme conditions, with all metrics reported on a reserved independent test set of 45,000 maps. The network achieves a 100% star detection rate and an overall average error of 0.0274 pixels. Under extreme conditions, the measurement standard deviation reaches 0.0397 pixels, about 1/12 that of Gaussian surface fitting, while the per-frame inference latency averages 1.07 ms with 99.989% of frames completing within 2 ms on the tested NVIDIA RTX 4090 platform, meeting the real-time constraint imposed by the constant sampling period of star sensors. The proposed method performs centroid estimation on 28 × 28-pixel star windows after coarse star extraction, providing high-precision and robust star sensor centroid estimation and supporting accurate spacecraft attitude determination.