Satellite Anomaly Detection Method Based on Parameter Adaptive Optimization Clustering

Real-time monitoring and anomaly detection of satellites in orbit are conducive to ensuring the safe and stable operation of satellites. In order to solve the problems of poor fineness, low efficiency and limited grid points when selecting the optimal clustering hyper-parameters through grid search in the process of detecting satellite anomalies using cluster analysis, the selection of clustering hyper-parameters is transformed into a single-objective optimization problem. And based on the heuristic search ability of intelligent optimization algorithm, a hyper-parameter adaptive optimization clustering algorithm UMOEAsII_BIRCH is proposed. To verify the effectiveness of adaptive search, tests are conducted on a satellite telemetry data set and a public data set. Using grid search as the benchmark, the clustering algorithms based on partition, density and hierarchy are selected respectively to compare the F1-score of anomaly detection and the algorithm execution time in adaptive search and grid search. The experimental results show that the proposed adaptive search overcomes the contradiction between fineness and efficiency in grid search, and is not limited by grid points. Besides, adaptive search outperforms grid search in the F1-score of anomaly detection, and has a significant advantage in execution efficiency.