TY - JOUR
T1 - Cislunar Tracklet Association Using CNN-Based Constrained Admissible Region
AU - Li, Jiayi
AU - Cai, Han
AU - Jiang, Yihang
AU - Sun, Xiucong
AU - Gui, Haichao
N1 - Publisher Copyright:
© 1965-2011 IEEE.
PY - 2026
Y1 - 2026
N2 - This article proposes an approach that integrates a convolutional neural network (CNN) with the constrained admissible region (CAR) method to improve tracklet association in cislunar space. The CNN is designed to enhance the CAR by reducing it to a more compact subregion, which properly bounds the probable orbital zone for a given optical tracklet. This region facilitates the classification of the tracklet into several possible orbital families, which is then utilized as prior information for sequential tracklet association. The derived CNN-CAR is integrated into the initial value problem (IVP) optimization framework, enabling intelligent tracklet association guided by orbital classification. Simulation results demonstrated that the proposed CNN-CAR-IVP method exhibits remarkable association performance, with both true positive and true negative rates exceeding 95%. These findings underscore the potential of the proposed framework to enhance space surveillance, collision risk assessment, and long-term catalog maintenance in the cislunar space environment.
AB - This article proposes an approach that integrates a convolutional neural network (CNN) with the constrained admissible region (CAR) method to improve tracklet association in cislunar space. The CNN is designed to enhance the CAR by reducing it to a more compact subregion, which properly bounds the probable orbital zone for a given optical tracklet. This region facilitates the classification of the tracklet into several possible orbital families, which is then utilized as prior information for sequential tracklet association. The derived CNN-CAR is integrated into the initial value problem (IVP) optimization framework, enabling intelligent tracklet association guided by orbital classification. Simulation results demonstrated that the proposed CNN-CAR-IVP method exhibits remarkable association performance, with both true positive and true negative rates exceeding 95%. These findings underscore the potential of the proposed framework to enhance space surveillance, collision risk assessment, and long-term catalog maintenance in the cislunar space environment.
KW - Circular restricted three-body problem (CR3BP)
KW - cislunar space
KW - convolutional neural network (CNN)
KW - tracklet association
UR - https://www.scopus.com/pages/publications/105032789945
U2 - 10.1109/TAES.2026.3671773
DO - 10.1109/TAES.2026.3671773
M3 - Article
AN - SCOPUS:105032789945
SN - 0018-9251
VL - 62
SP - 8777
EP - 8790
JO - IEEE Transactions on Aerospace and Electronic Systems
JF - IEEE Transactions on Aerospace and Electronic Systems
ER -