Civil Aircraft Landing Attitude Ultra-Limit Warning System Based on mRMR-LSTM

To achieve the forward movement of the aircraft landing attitude ultra-limit, this paper builds a deep learning-based aircraft landing attitude warning system. The early warning system includes four modules: data pretreatment, feature dimensionality reduction, prediction, and judgment. Subsequently, through data pretreatment methods such as data cleaning, frequency normalization, data standardization, and feature classification, the experimental dataset is transformed into a form recognizable by machine learning algorithms and neural network models. The necessary feature parameters are extracted to form a deep learning training dataset. Then, the Max-Relevance and Min-Redundancy algorithm was applied to screen the QAR (Quick Access Recorder) parameters with the highest correlation with the predictor variables, and the LSTM network model was established to predict the pitch and roll angles of the aircraft landing, respectively. Evaluation metrics are used to determine the optimal model parameters. Finally, the confusion matrix is introduced to test the prediction effect of the model, and through the secondary indicators of the confusion matrix, the prediction accuracy of the established landing attitude warning system is 94.83% for the pitch angle and 91.18% for the roll angle. It also provides pilots with a 5 s time margin to avoid risks. The system can effectively issue early warnings for ultra-limit landing attitude events and, based on the prediction results, identify the types of risks.