In response to the need for quasi-real-time and high-confidence research and judgment of spacecraft abnormal proximity symptoms in space situation awareness, this paper proposes an intelligent detection method for orbital anomalies based on the high-dimesional representation of spacecraft behavior. Based on the target orbit element database to extract the spacecraft behavior time series characteristics and generate a high-dimensional representation matrix, a convolutional neural network structure integrating multi-dimensional characteristic classification and detection is designed, the orbital abnormal behavior characteristics is automatically learned, and it’s detected whether the spacecraft orbital is abnormal. The historical Two-Line Elements (TLE) data are used to generate a test set of abnormal orbital behavior of the spacecraft, and the Mahalanobis distance method and the intelligent detection method are used to jointly detect the test set. The test results show that the intelligent detection method provides a better orbital anomaly detection success rate on the self-built test set than the Mahalanobis distance method, which is increased from 85% to 98.5%. The intelligent detection method can be effectively used for detection of abnormal spacecraft orbital behavior.
We solve the problem of video object segmentation by investigating how to expand the role of convolution in convolutional neural networks. Based on the One-Shot Video Object Segmentation (OSVOS) which can successfully tackle the task of semi-supervised video object segmentation, we introduce U-shape architecture. We first build a Global Guidance Module (GGM) on the bottom-up path to provide location information of potentially significant objects for layers of different feature levels. Then we design a Multi-scale Convolution Module (MCM) to fully get feature information and a Feature Fusion Module (FFM) to make the coarse-level semantic information well fused with the finelevel features from the top-down pathway. GGM and FFM allow the high-level semantic features to be progressively refined, yielding detail enriched segmentation maps. The experimental results on DAVIS 2016 data set shows that our proposed approach can more accurately locate the segmentation objects with sharpened details and our model has improved on all indicators than OSVOS.
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