Bayesian Neural Networks for Flight Trajectory Prediction and Safety Assessment Abstract   Safety, as the most important concern in civil aviation, needs to be maintained at an acceptable level at all times in the air transportation system. This paper aims to increase enroute flight safety through the development of deep learning models for trajectory prediction, where model prediction uncertainty is characterized following a Bayesian approach. The proposed methodology consists of four steps. In the first step, a large volume of raw messages in Flight Information Exchange Model (FIXM) format streamed from Federal Aviation Administration are processed with a distributed computing engine Apache Spark to extract trajectory information in an efficient manner. In the second step, two types of deep learning models are trained to predict flight trajectory from different perspectives. Specifically, deep feedforward neural networks (DNN) are trained to make a onestepahead prediction on the deviation along latitude and longitude between the actual flight trajectory and target flight trajectory. In parallel, deep Long ShortTerm Memory (LSTM) neural networks are trained to make longerterm predictions on the flight trajectory over several subsequent time instants. The DNN model is more accurate but has a singlestep prediction horizon, whereas the LSTM model is less accurate but longer prediction horizon. Therefore, in the third step, the two different types of deep learning models are blended together to create a multifidelity prediction. After quantifying the discrepancy between the two model predictions in the current time instant, the DNN prediction is used to correct the LSTM prediction of flight trajectory along subsequent time instants accordingly. The multifidelity approach is expanded to multiple flights, and is then used to assess safety based on horizontal and vertical separation distance between two flights. Computational results illustrate the promising performance of the blended model in predicting the flight trajectory and assessing enroute flight safety.

RealTime Anomaly Detection for Flight Testing Using AutoEncoder and LSTM Flight testing is crucial in validating the functionality and safety in new commercial aircraft design before mass production. The challenge is to support realtime analysis of highdimensional time series data generated from tens of thousands of sensors around the aircraft during test flights. We propose a novel 2stage approach, using a finetuned autoencoder to extract the generic underlying features of highdimensional data, followed by a stacked LSTM using the learned features to predict aircraft time series and to detect anomalies in realtime for flight testing. A novel Timestep(TS)buffer is introduced to avoid redundant calculations of LSTM gate operations to reduce system latency. Compared with a software implementation of the AutoEncoderLSTM on CPU and GPU, our FPGA design is respectively 36.3 and 23.9 times faster and consumes 247 and 499 times less energy.