The exponential proliferation of space debris in near-Earth orbits presents substantial risks to operational spacecraft and crew safety. Current space surveillance systems, primarily functioning at night, encounter significant limitations in detecting small, low-brightness debris against intense daylight backgrounds. The development of low-SNR dim target detection technologies under strong skyglow is essential for maintaining space asset security and sustainable orbital environments. Existing approaches are categorized into hardware optimization and algorithmic development. While spectral filtering is relatively mature, it remains sensitive to filter window configurations; polarimetric filtering faces limitations due to spatiotemporal variations in sky polarization; detector performance enhancement remains challenging; spatial-frequency filtering shows parameter sensitivity; visual saliency detection encounters difficulties with adaptive thresholding for varying target sizes; low-rank sparse decomposition methods face localization inaccuracies and computational constraints; deep learning approaches are limited by insufficient training data. This study introduces a velocity estimation and correlation decision-based detection framework specifically designed for low-signal-to-noise ratio (SNR) moving targets under strong skyglow. Numerical simulations and experimental validations demonstrate enhanced detection capabilities, define operational parameters, and verify engineering feasibility, providing a practical solution for dim target monitoring.

To address signal correlation degradation caused by inter-frame displacement in low-SNR moving target detection, this study presents a velocity-iteration-estimation aberration modulation correlation method (VAMCM). The method initially applies motion compensation to image frame sequences through affine transformation, utilizing the shift-and-add principle to restore temporal correlation between targets and aberration-modulated signals. Subsequently, a ??three-tier progressive velocity estimation architecture?? iteratively refines the velocity search range within constrained parameter spaces, optimizing motion parameters through hierarchical iterations. The implementation includes a 3σ statistical verification mechanism to validate target authenticity, optimizing computational efficiency and detection accuracy. Furthermore, periodic aberration perturbations characterized by Zernike polynomials are actively introduced to generate spatiotemporally coupled modulation signals, enhancing target discriminability. The method’s effectiveness is validated using a synthetic dataset incorporating motion trajectory simulation, Poisson noise modeling, and active aberration modulation, enabling comparative analysis with existing approaches. The validation process concludes with indoor and field experiments to verify practical efficacy.

The VAMCM demonstrates superior performance in low-SNR moving target detection through comprehensive simulations, indoor experiments, and field tests. Simulation results indicate that VAMCM achieves a detection probabilities exceeding 90% and a sensitivity over 95% with a false alarm rate below 10% and trajectory errors stabilized at 0.39 pixel/frame when SNR is not smaller than 2 (Fig. 10 and Fig. 11). Traditional tensor decomposition methods (e.g., TT, TR, and ASTTV-NTLA) fail completely at SNR of smaller than 4, with false alarm rates approaching 50% (Figs. 13?17). Indoor experiments show VAMCM achieving 45% detection probability and 59.4% sensitivity at SNR of 3.39, substantially outperforming alternative methods (all 0%). At SNR of 9.47, it attains 100% detection probability and sensitivity with trajectory errors converging to 1.003 pixel/frame, while other methods demonstrate limited effectiveness even at high SNR levels (e.g., 4DST-BTMD achieves 100% detection probability but only 50% sensitivity) (Table 3). Field tests under complex astronomical conditions (SNR of about 2.22) confirm VAMCM’s successful target detection, while alternative methods fail entirely (Fig. 22). Analysis reveals that VAMCM addresses conventional methods’ insufficient target-background separation capability in low-SNR scenarios through spatiotemporal matching filters constructed via velocity iteration estimation and aberration modulation. However, computational efficiency remains constrained by high time consumption, with velocity iteration comprising 95.67% of processing time (Fig. 12).

This research addresses the detection of low-SNR moving targets under strong skyglow interference. Through the integration of velocity iteration estimation with active wavefront modulation and exploitation of aberration response differences between targets and background noise, the study presents a local enhancement-velocity estimation-target verification detection method for low-SNR target identification and trajectory resolution. Simulation analysis demonstrates superior performance with detection probability and sensitivity exceeding 90% at SNR of 2, while maintaining false alarm rates below 10% and achieving trajectory mean absolute error within 1 pixel/frame. Indoor experimental validation shows comparable performance (equivalent to simulation results at SNR of 2) at approximately SNR of 4.3 under practical conditions. Field experiments confirm the method’s system integration capability and effective detection of spatial targets with SNR of about 2.2 under strong daylight interference. VAMCM exhibits three key advantages: reliable target discrimination capability, sub-pixel level trajectory accuracy, and enhanced low-SNR adaptability. The approach demonstrates significant improvements over conventional tensor decomposition-based methods in detection thresholds, expanding active aberration modulation techniques’ application scope. VAMCM offers an innovative solution for real-time detection and tracking of faint moving targets under intense daylight conditions. Future research will focus on computational efficiency optimization and application extension to diverse dynamic target scenarios, enhancing operational adaptability across various environments.