High Reliability Neural Networks Structure with Application to Spacecraft ASMS Tone Detection

In this study we will show that the research on Nversion high-reliability software structures can be extended to neural networks architecture. In addition, we will explore the possibility of applying this structure to a spacecraft tracking problem. One such system is the Automated Spacecraft Monitoring System (ASMS), a beacon-monitoring or detection system. Four neural networks, each trained for various operating environments, are implemented in an Nversion structure. The results of the networks are combined to form a composite outcome. The combined outcome is used as part of a hypothesis testing procedure to distinguish between the presence or absence of the beacon signal. The results show that any of a number of composite outcomes outperforms the use of any single neural network. Further, the simple average of network results provides the composite outcome with best performance.