Estimation of low-altitude moving target trajectory using single acoustic array

2016 ◽  
Vol 139 (4) ◽  
pp. 1848-1858 ◽  
Author(s):  
Jianfei Tong ◽  
Wei Xie ◽  
Yu-Hen Hu ◽  
Ming Bao ◽  
Xiaodong Li ◽  
...  
Keyword(s):  
Moving Target ◽  
Target Trajectory ◽  
Acoustic Array ◽  
Low Altitude

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

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.

Design of Detecting System for Moving Target Based on Twice Mixing

2012 ◽  
Vol 562-564 ◽  
pp. 1016-1019
Author(s):  
Kang Yi Wang
Keyword(s):  
Basic Principle ◽  
Frequency Modulation ◽  
Doppler Effect ◽  
Moving Target ◽  
Microwave Technology ◽  
Velocity Information ◽  
Working Principle ◽  
The Doppler Effect ◽  
Low Altitude ◽  
Modern War

The low altitude breakthrough has become an important battle mode of modern war, which brings the huge difficulty for low altitude defense. The microwave technology is an effective method to detect the moving target in the low altitude. Taking the frequency modulation of microwave as basic principle, and combined the Doppler Effect which can measure the velocity information of target, a microwave detecting system based on twice mixing is established. Its composition and working principle are analyzed, and the circuits are designed. The laboratory experiment indicates that it is completely feasible to apply the system to the detection of the moving target.

scholarly journals Automatic Manipulator Tracking Control Based on Moving Target Trajectory Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Haifeng Luo
Keyword(s):  
Target Tracking ◽  
Tracking Control ◽  
Moving Target ◽  
Trajectory Prediction ◽  
Target Trajectory ◽  
Proposed Model ◽  
Core Issue ◽  
Testing Algorithm ◽  
The Given ◽  
Automatic Manipulator

The core issue of automatic manipulator tracking control is how to ensure the given moving target follows the expected trajectory and adapts to various uncertain factors. However, the existing moving target trajectory prediction methods rely on highly complex and accurate models, lacking the ability to generalize different automatic manipulator tracking scenarios. Therefore, this study tries to find a way to realize automatic manipulator tracking control based on moving target trajectory prediction. In particular, a moving target trajectory prediction model was established, and its parameters were optimized. Next, a tracking-training-testing algorithm was proposed for manipulator’s automatic moving target tracking, and the operating flows were detailed for training module, target detection module, and target tracking module. The proposed model and algorithm were proved effective through experiments.

scholarly journals Estimation of Moving Target Trajectory using Optimal Smoothing Filter based on Beamforming Data

2015 ◽  
Vol 43 (12) ◽  
pp. 1062-1070
Author(s):  
Junho Jeong ◽  
Gyeonghun Kim ◽  
Yeong-Ju Go ◽  
Jaehyung Lee ◽  
Seungkeun Kim ◽  
...  
Keyword(s):  
Moving Target ◽  
Target Trajectory ◽  
Smoothing Filter ◽  
Optimal Smoothing

scholarly journals Data Mining Algorithm for Moving Object Trajectory

2022 ◽  
Vol 16 ◽  
pp. 330-337
Author(s):  
Yudong Guo ◽  
Jinping Zuo
Keyword(s):  
Data Mining ◽  
Hausdorff Distance ◽  
Detection Efficiency ◽  
Moving Target ◽  
Data Mining Algorithm ◽  
Trajectory Data ◽  
Target Trajectory ◽  
Mining Algorithm ◽  
Trajectory Classification ◽  
Matching Structure

Aiming at the poor effect and long recognition time of data mining algorithm for moving target trajectory recognition, a data mining algorithm based on improved Hausdorff distance is proposed. The position and angle of abnormal trajectory data are detected by calculating the distance between trajectory classification and sub trajectory line segments, and the trajectory unit is established by using the improved Hausdorff distance algorithm to optimize the similarity matching structure. Experimental results show that the algorithm has low error pruning rate in identifying moving target trajectory, improves the detection efficiency of moving target trajectory recognition data, and ensures the quality of moving target trajectory recognition data mining

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