Communication of Target Trajectory and Wind Information to Improve Airborne Interval Management Spacing Performance

2015 ◽  
Author(s):  
Lesley A. Weitz ◽  
William Penhallegon ◽  
Brock Lascara ◽  
Hans Stassen ◽  
Rafe Katkin
Keyword(s):  
Target Trajectory

On the use of a minimal path approach for target trajectory analysis

2013 ◽  
Vol 46 (7) ◽  
pp. 2015-2027 ◽  
Author(s):  
B. Cancela ◽  
M. Ortega ◽  
M.G. Penedo ◽  
J. Novo ◽  
N. Barreira
Keyword(s):  
Trajectory Analysis ◽  
Minimal Path ◽  
Target Trajectory

Model of Moving Target Trajectory Detected Based on Airborne Laser Radar Imaging

2015 ◽  
Vol 52 (10) ◽  
pp. 101002
Author(s):  
寇添 Kou Tian ◽  
王海晏 Wang Haiyan ◽  
王芳 Wang Fang ◽  
吴学铭 Wu Xueming ◽  
王领 Wang Ling ◽  
...  
Keyword(s):  
Radar Imaging ◽  
Laser Radar ◽  
Moving Target ◽  
Target Trajectory ◽  
Airborne Laser

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.

Modeling typical driving patterns in target trajectory decisions for vehicles traversing roads with complex shapes

SIMULATION ◽  
2016 ◽  
Vol 92 (7) ◽  
pp. 669-686 ◽  
Author(s):  
Jin Xu ◽  
Jun Zhao ◽  
Kui Yang ◽  
Yiming Shao
Keyword(s):  
Target Trajectory ◽  
Driving Patterns ◽  
Complex Shapes

Predictive human pursuit and "orbital goal" of microstimulated smooth eye movements

1995 ◽  
Vol 74 (3) ◽  
pp. 1358-1361 ◽  
Author(s):  
P. van Gelder ◽  
S. Lebedev ◽  
W. H. Tsui
Keyword(s):  
Smooth Pursuit ◽  
Human Subjects ◽  
Initial Position ◽  
Common Point ◽  
Frontal Eye Field ◽  
Moving Target ◽  
The Gaze ◽  
Target Trajectory ◽  
Eye Field ◽  
Normal Human

1. Anticipatory saccades in smooth pursuit move the point of gaze from near the moving target to well ahead of it, interrupting accurate smooth pursuit. Their effects on the pursuit process were studied in 22 normal human subjects. We presented horizontal periodic target trajectories of 30 degrees amplitude and 30 degrees/s constant velocity or 0.4 Hz sinusoidal velocity in 40-s trials. Saccades and surrounding smooth eye movement (SEM) segments were marked and classified by computer. 2. Anticipatory saccades were often followed by slowed SEM that tended to intercept the target at the endpoint of its trajectory. This was seen in the distribution of projections of the initial 60 ms of postsaccadic SEM to the time of the trajectory endpoint. Magnitude of this SEM tended to follow a function of the time and location of the endpoint of the anticipatory saccade, decreasing as the anticipatory saccades landed closer to the trajectory endpoint. 3. The time and location of the target trajectory endpoint seemed to be the goal for this SEM. We believe this to demonstrate the predictive use of the period and amplitude of the trajectory in smooth pursuit, apart from the instantaneous velocity match of the target. 4. Gottlieb and coworkers in the frontal eye field and Ron and Robinson in the cerebellum produced SEMs in the monkey by microstimulation. At some sites in both structures, direction and velocity of the SEMs depended on the initial position of the eye in that the elicited SEMs appeared to be converging toward a common point, or "orbital goal", and the SEM velocity diminished as the gaze neared that goal.2+ Both our SEM after anticipatory saccades and microstimulated SEM in the monkey slowed as the initial position was brought closer to the inferred orbital goal. This similarity suggests that the goal-directed SEM sites in the monkey might be part of a mechanism for predictive pursuit.

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