基于成本敏感CNN-BiLSTM网络的目标可见性预测方法OA

To address the low accuracy and insufficient autonomy of the traditional SGP4 model in predicting target vis-ibility under non-perfect ephemeris conditions,a cost-sensitive hybrid network-based method was proposed for predict-ing the visibility of high-dynamic targets in specific regions.This method employed an early fusion of multi-source het-erogeneous data features and constructed a CNN-BiLSTM architecture to comprehensively extract spatial geometric lo-cal features and temporal short-term dependency features.A cost-sensitive learning mechanism was introduced to ad-dress the extreme imbalance in target visibility,enabling accurate prediction of visibility windows in specific regions.The results show that with an extreme imbalance ratio of 1:357,the proposed method achieves a precision of 94.56%and a recall of 97.78%,outperforming existing methods,and provides technical support for space mission planning and colli-sion avoidance warning.