Sensitivity of Trajectory Prediction Accuracy to Aircraft Performance Uncertainty

2013 ◽  
Author(s):  
Enrique Casado ◽  
Miguel Vilaplana ◽  
Colin Goodchild
Keyword(s):  
Prediction Accuracy ◽  
Trajectory Prediction ◽  
Aircraft Performance ◽  
Performance Uncertainty

scholarly journals Moving Time UCAV Maneuver Decision Based on the Dynamic Relational Weight Algorithm and Trajectory Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Xie Lei ◽  
Ding Dali ◽  
Wei Zhenglei ◽  
Xi Zhifei ◽  
Tang Andi
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Prediction Accuracy ◽  
Control Variable ◽  
Trajectory Prediction ◽  
Constant Weight ◽  
Adaptive Boosting ◽  
Time Performance ◽  
Correlation Degree ◽  
Air Combat

To improve the accuracy and real-time performance of autonomous decision-making by the unmanned combat aerial vehicle (UCAV), a decision-making method combining the dynamic relational weight algorithm and moving time strategy is proposed, and trajectory prediction is added to maneuver decision-making. Considering the lack of continuity and diversity of air combat situation reflected by the constant weight in situation assessment, a dynamic relational weight algorithm is proposed to establish an air combat situation system and adjust the weight according to the current situation. Based on the dominance function, this method calculates the correlation degree of each subsituation and the total situation. According to the priority principle and information entropy theory, the hierarchical fitting function is proposed, the association expectation is calculated by using if-then rules, and the weight is dynamically adjusted. In trajectory prediction, the online sliding input module is introduced, and the long- and short-term memory (LSTM) network is used for real-time prediction. To further improve the prediction accuracy, the adaptive boosting (Ada) method is used to build the outer frame and compare with three traditional prediction networks. The results show that the prediction accuracy of Ada-LSTM is better. In the decision-making method, the moving time optimization strategy is adopted. To solve the problem of timeliness and optimization, each control variable is divided into 9 gradients, and there are 729 control schemes in the control sequence. Through contrast pursuit simulation experiments, it is verified that the maneuver decision method combining the dynamic relational weight algorithm and moving time strategy has a better accuracy and real-time performance. In the case of using prediction and not using prediction, the adaptive countermeasure simulation is carried out with the current more advanced Bayesian inference maneuvering decision-making scheme. The results show that the UCAV maneuvering decision-making ability combined with accurate prediction is better.

Adaptive Algorithm to Improve Trajectory Prediction Accuracy of Climbing Aircraft

2013 ◽  
Vol 36 (1) ◽  
pp. 15-24 ◽  
Author(s):  
David P. Thipphavong ◽  
Charles A. Schultz ◽  
Alan G. Lee ◽  
Steven H. Chan
Keyword(s):  
Adaptive Algorithm ◽  
Prediction Accuracy ◽  
Trajectory Prediction

scholarly journals An improved 4D trajectory modeling approach for traffic management

2021 ◽  
Author(s):  
Zhou Shen ◽  
Xiaomo Yu
Keyword(s):  
Traffic Flow ◽  
Traffic Safety ◽  
Data Model ◽  
Traffic Management ◽  
Air Transportation ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Trajectory Prediction ◽  
Air Traffic Flow ◽  
Aircraft Performance

Abstract Under the premise that the capability of existing air transportation system can no longer meet the demand of air traffic flow, 4D trajectory operation based on accuracy is the basis of future air traffic management (ATM) system to achieve the optimization of flight trajectory. This article investigates the establishment of a data model system based on aircraft performance and operation procedures, which can be applied to 4D trajectory prediction to greatly reduce or avoid the possibility of flight conflicts in the air, enhance air traffic safety and improve air traffic flow.

scholarly journals Vessel Trajectory Prediction Model Based on AIS Sensor Data and Adaptive Chaos Differential Evolution Support Vector Regression (ACDE-SVR)

2019 ◽  
Vol 9 (15) ◽  
pp. 2983 ◽  
Author(s):  
Jiao Liu ◽  
Guoyou Shi ◽  
Kaige Zhu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Differential Evolution ◽  
Support Vector Regression ◽  
Prediction Accuracy ◽  
Sensor Data ◽  
Support Vector ◽  
Identification System ◽  
Trajectory Prediction ◽  
Model Based

There are difficulties in obtaining accurate modeling of ship trajectories with traditional prediction methods. For example, neural networks are prone to falling into local optima and there are a small number of Automatic Identification System (AIS) information samples regarding target ships acquired in real time at sea. In order to improve the accuracy of ship trajectory predictions and solve these problems, a trajectory prediction model based on support vector regression (SVR) is proposed. Ship speed, course, time stamp, longitude and latitude from AIS data were selected as sample features and the wavelet threshold de-noising method was used to process the ship position data. The adaptive chaos differential evolution (ACDE) algorithm was used to optimize the internal model parameters to improve convergence speed and prediction accuracy. AIS sensor data corresponding to a certain section of the Tianjin Port ships were selected, on which SVR, Recurrent Neural Network (RNN) and Back Propagation (BP) neural network model trajectory prediction simulations were carried out. A comparison of the results shows that the trajectory prediction model based on ACDE-SVR has higher and more stable prediction accuracy, requires less time and is simple, feasible and efficient.

scholarly journals Novel method for hurricane trajectory prediction based on data mining

2013 ◽  
Vol 1 (5) ◽  
pp. 4681-4712
Author(s):  
X. Dong ◽  
D. C. Pi
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Prediction Accuracy ◽  
Trajectory Prediction ◽  
Motion Patterns ◽  
Novel Method ◽  
Motion Characteristics ◽  
Trajectory Database ◽  
Current Movement

Abstract. This paper describes a novel method for hurricane trajectory prediction based on data mining (HTPDM) according to the hurricane's motion characteristics. Firstly, all frequent trajectories in the historical hurricane trajectory database are mined by using association analysis technology and their corresponding association rules are generated as motion patterns. Then, the current hurricane trajectories are matched with the motion patterns for predicting. If no association rule is found for matching, a predicted result according to the hurricane current movement trend would be returned. All experiments are conducted with the Atlantic weather Hurricane/Tropical Data from 1900 to 2008. The experimental results show that if the matching failure part is contained, the prediction accuracy is 57.5%. Whereas, the valve would be to 65% provided all matches are successful.

Impact of Data-Link on Ground-Based Trajectory Prediction Accuracy for Continuous Descent Arrivals

2013 ◽  
Author(s):  
Jesper Bronsvoort ◽  
Greg McDonald ◽  
Jean Boucquey ◽  
Carlos Garcia-Avello ◽  
Juan A. Besada
Keyword(s):  
Prediction Accuracy ◽  
Trajectory Prediction ◽  
Data Link

scholarly journals Novel method for hurricane trajectory prediction based on data mining

2013 ◽  
Vol 13 (12) ◽  
pp. 3211-3220 ◽  
Author(s):  
X. Dong ◽  
D. C. Pi
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Prediction Accuracy ◽  
Trajectory Prediction ◽  
Motion Patterns ◽  
Novel Method ◽  
Motion Characteristics ◽  
Trajectory Database ◽  
Current Movement

Abstract. This paper describes a novel method for hurricane trajectory prediction based on data mining (HTPDM) according to the hurricane's motion characteristics. Firstly, all frequent trajectories in the historical hurricane trajectory database are mined by using association analysis technology and their corresponding association rules are generated as motion patterns. Then, the current hurricane trajectories are matched with the motion patterns for predicting. If no association rule is found for matching, a predicted result according to the hurricane current movement trend would be returned. All experiments are conducted with the Atlantic weather Hurricane/Tropical Data from 1900 to 2008. The experimental results show that if the matching failure part is contained, the prediction accuracy is 57.5%. Whereas, the valve would be to 65% provided all matches are successful.

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