Airport Perimeter Intrusion Alarm and Classification Algorithm Based on AE-LSTM Network Model

The airport perimeter intrusion alarm system is the first line of defense in the airport's flight zone. Traditional airport perimeter intrusion alarm system has higher false alarm rate and cannot classify different intrusion categories under the influence of severe weather. In order to solve the problem, a self-encoder long-short term memory network model is proposed in this paper. This model extracts the hidden encoder feature from input signals and constructs the feature vector matrix fused with timing information such that its complexity can be reduced. It is shown via the results of network model performance evaluation that the proposed network model has a low false alarm rate and a high accuracy of vibration states classification. Besides, this model has a low complexity that can guarantee a good practical application prospect.