Target trajectory prediction based on optimized neural network

2017 ◽  
Author(s):  
Xiaoxiang Song ◽  
Yan Guo ◽  
Ning Li ◽  
Baoming Sun
Keyword(s):  
Neural Network ◽  
Trajectory Prediction ◽  
Target Trajectory

scholarly journals Air Combat Maneuver Trajectory Prediction Model of Target Based on Chaotic Theory and IGA-VNN

2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Zhi-fei Xi ◽  
An Xu ◽  
Ying-xin Kou ◽  
Zhan-wu Li ◽  
Ai-wu Yang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Time Series ◽  
Bp Neural Network ◽  
Functional Model ◽  
Mutation Operator ◽  
Improved Genetic Algorithm ◽  
Trajectory Prediction ◽  
Target Trajectory ◽  
Chaotic Theory

Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and threat assessment. Aiming at the problem of low prediction accuracy in traditional trajectory prediction methods, combined with the chaotic characteristics of the target maneuver trajectory time series, a target maneuver trajectory prediction model based on chaotic theory and improved genetic algorithm-Volterra neural network (IGA-VNN) model is proposed, mathematically deducing and analyzing the consistency between Volterra functional model and back propagation (BP) neural network in structure. Firstly, the C-C method is used to reconstruct the phase space of the target trajectory time series, and the maximum Lyapunov exponent of the time series of the target maneuver trajectory is calculated. It is proved that the time series of the target maneuver trajectory has chaotic characteristics, so the chaotic method can be used to predict the target trajectory time series. Then, the practicable Volterra functional model and BP neural network are combined together, learning the advantages of both and overcoming the difficulty in obtaining the high-order kernel function of the Volterra functional model. At the same time, an adaptive crossover mutation operator and a combination mutation operator based on the difference degree of gene segments are proposed to improve the traditional genetic algorithm; the improved genetic algorithm is used to optimize BP neural network, and the optimal initial weights and thresholds are obtained. Finally, the IGA-VNN model of chaotic time series is applied to the prediction of target maneuver trajectory time series, and the experimental results show that its estimated performance is obviously superior to other prediction algorithms.

scholarly journals A Ship Trajectory Prediction Framework Based on a Recurrent Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5133
Author(s):  
Yongfeng Suo ◽  
Wenke Chen ◽  
Christophe Claramunt ◽  
Shenhua Yang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Computational Time ◽  
Identification System ◽  
Trajectory Prediction ◽  
Ground Truth Data ◽  
Redundant Data ◽  
Computation Efficiency ◽  
Path Distance

Ship trajectory prediction is a key requisite for maritime navigation early warning and safety, but accuracy and computation efficiency are major issues still to be resolved. The research presented in this paper introduces a deep learning framework and a Gate Recurrent Unit (GRU) model to predict vessel trajectories. First, series of trajectories are extracted from Automatic Identification System (AIS) ship data (i.e., longitude, latitude, speed, and course). Secondly, main trajectories are derived by applying the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Next, a trajectory information correction algorithm is applied based on a symmetric segmented-path distance to eliminate the influence of a large number of redundant data and to optimize incoming trajectories. A recurrent neural network is applied to predict real-time ship trajectories and is successively trained. Ground truth data from AIS raw data in the port of Zhangzhou, China were used to train and verify the validity of the proposed model. Further comparison was made with the Long Short-Term Memory (LSTM) network. The experiments showed that the ship’s trajectory prediction method can improve computational time efficiency even though the prediction accuracy is similar to that of LSTM.

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