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Aircraft Spatial Signal Testing Method Based on Big Data Driven Self-Learning

The aircraft spatial signal map is used to discern whether the aircraft signal meets the system operating requirements. The traditional acquisition of aircraft spatial signal direction map relies on test flights, and the data obtained can only plot two-dimensional directional map of aircraft signals. Given that such approach will incur high cost, long time and incomplete data, this paper proposes a novel technical route to acquire aircraft spatial signals by utilizing big data driven self-learning technology. Using neural networks and fuzzy algorithm for data processing, the data-based method is able to improve the measurement accuracy of the aircraft spatial signal, save test flight cost, promote overhaul of the aircraft communication system and maintain aircraft flight safety.