LSTM-based Flight Trajectory Prediction

Abstract

Safety ranks the first in Air Traffic Management (ATM). Accurate trajectory prediction can help ATM to forecast potential dangers and effectively provide instructions for safely traveling. Most trajectory prediction algorithms work for land traffic, which rely on points of interest (POIs) and are only suitable for stationary road condition. Compared with land traffic prediction, flight trajectory prediction is very difficult because way-points are sparse and the flight envelopes are heavily affected by external factors. In this paper, we propose a flight trajectory prediction model based on a Long Short-Term Memory (LSTM) network. The four interacting layers of a repeating module in an LSTM enables it to connect the long-term dependencies to present predicting task. Applying sliding windows in LSTM maintains the continuity and avoids compromising the dynamic dependencies of adjacent states in the long-term sequences, which helps to improve accuracy of trajectory prediction. Taking time dimension into consideration, both 3-D (time stamp, latitude and longitude) and 4-D (time stamp, latitude, longitude and altitude) trajectories are predicted to prove the efficiency of our approach. The dataset we use was collected by ADS-B ground stations. We evaluate our model by widely used measurements, such as the mean absolute error (MAE), the mean relative error (MRE), the root mean square error (RMSE) and the dynamic warping time (DWT) methods. As Markov Model is the most popular in time series processing, comparisons among Markov Model (MM), weighted Markov Model (wMM) and our model are presented. Our model outperforms the existing models (MM and wMM) and provides a strong basis for abnormal detection and decision-making.

A Hybrid CNN-LSTM Model for Aircraft 4D Trajectory Prediction

“…Currently, the dimension of the tensor becomes (None, 6, 32). Then after activated by Relu, passing a max pooling layer (maxpooling1D) with a window size of 2, the tensor dimension becomes (None, 3,32). Then after the same round of processing as above, the tensor shape becomes (None, 1, 32).…”

Approach and Landing Aircraft On-Board Parameters Estimation with LSTM Networks

“…Peyada et al presented a new filtering technique based upon a neural network and Gauss-Newton method to estimate aircraft parameters [17], [18]. A recent type of neural network called Long-Short Term Memory has proven to be very efficient on time series data to predict trajectories [19] or hard landings [20]. To the best of the authors' knowledge, LSTM neural networks have not already been used to estimate aircraft on-board parameters such as fuel flow rate or flap and landing gear settings.…”

Real-Time Prediction of Large-Scale Ship Model Vertical Acceleration Based on Recurrent Neural Network

“…The effective combination of machine learning and artificial neural network (ANN) has been the focus of ship motion prediction research. The long short-term memory (LSTM) model is a recurrent neural network (RNN) used in machine learning, which has great potential in time series data prediction [3,4]. Karim et al [5] applied the LSTM model to several complex multivariate time series classification tasks, such as activity recognition and action recognition.…”