Passivity-Based Visual Feedback Pose Regulation Integrating a Target Motion Model in Three Dimensions

2013 ◽  
Vol 6 (5) ◽  
pp. 322-330 ◽  
Author(s):  
Tatsuya IBUKI ◽  
Takeshi HATANAKA ◽  
Masayuki FUJITA
Keyword(s):  
Visual Feedback ◽  
Target Motion ◽  
Three Dimensions ◽  
Motion Model

Research of Target Motion Model in Polar Coordinates Based on Dynamic Systems

2012 ◽  
Vol 496 ◽  
pp. 343-346
Author(s):  
Sheng Jie Zhao ◽  
Chuan Wang
Keyword(s):  
Monte Carlo ◽  
Dynamic Systems ◽  
Scientific Data ◽  
Target Motion ◽  
Polar Coordinates ◽  
Motion Model ◽  
Novel Target ◽  
Nonlinear Observation ◽  
Cv Model ◽  
Motion Mode

Abstract:Aiming at the nonlinear observation problem of target motion model in Cartesian coordinates, a novel target motion model in polar coordinates is proposed based on dynamic systems with analysis of scientific data materials. This paper has done about the target motion model in polar coordinates corresponding to CV model and CA respectively and simulated with Monte Carlo. The experimental results indicate that the target motion model is all agreed with targets motion in fact and can be used to describe the targets motion. Simulations show that state estimation of the KF based on the target motion mode with analysis of dynamic systems is efficient.

A new smoothing approach with diverse fixed-lags based on target motion model

2006 ◽  
Vol 3 (4) ◽  
pp. 425-430
Author(s):  
Chen Li ◽  
Chong-Zhao Han ◽  
Hong-Yan Zhu
Keyword(s):  
Target Motion ◽  
Motion Model

Effect of changing feedback delay on spontaneous oscillations in smooth pursuit eye movements of monkeys

1992 ◽  
Vol 67 (3) ◽  
pp. 625-638 ◽  
Author(s):  
D. Goldreich ◽  
R. J. Krauzlis ◽  
S. G. Lisberger
Keyword(s):  
Eye Movements ◽  
Visual Feedback ◽  
Target Motion ◽  
Image Motion ◽  
Target Velocity ◽  
Feedback Delay ◽  
Total Delay ◽  
Internal Feedback ◽  
Pursuit Eye Movements ◽  
Eye Velocity

1. Our goal was to discriminate between two classes of models for pursuit eye movements. The monkey's pursuit system and both classes of model exhibit oscillations around target velocity during tracking of ramp target motion. However, the mechanisms that determine the frequency of oscillations differ in the two classes of model. In "internal feedback" models, oscillations are controlled by internal feedback loops, and the frequency of oscillation does not depend strongly on the delay in visual feedback. In "image motion" models, oscillations are controlled by visual feedback, and the frequency of oscillation does depend on the delay in visual feedback. 2. We measured the frequency of oscillation during pursuit of ramp target motion as a function of the total delay for visual feedback. For the shortest feedback delays of approximately 70 ms, the frequency of oscillation was between 6 and 7 Hz. Increases in feedback delay caused decreases in the frequency of oscillation. The effect of increasing feedback delay was similar, whether the increases were produced naturally by dimming and decreasing the size of the tracking target or artificially with the computer. We conclude that the oscillations in eye velocity during pursuit of ramp target motion are controlled by visual inputs, as suggested by the image motion class of models. 3. Previous experiments had suggested that the visuomotor pathways for pursuit are unable to respond well to frequencies as high as the 6-7 Hz at which eye velocity oscillates in monkeys. We therefore tested the response to target vibration at an amplitude of +/- 8 degrees/s and frequencies as high as 15 Hz. For target vibration at 6 Hz, the gain of pursuit, defined as the amplitude of eye velocity divided by the amplitude of target velocity, was as high as 0.65. We conclude that the visuomotor pathways for pursuit are capable of processing image motion at high temporal frequencies. 4. The gain of pursuit was much larger when the target vibrated around a constant speed of 15 degrees/s than when it vibrated around a stationary position. This suggests that the pursuit pathways contain a switch that must be closed to allow the visuomotor pathways for pursuit to operate at their full gain. The switch apparently remains open for target vibration around a stationary position. 5. The responses to target vibration revealed a frequency at which eye velocity lagged target velocity by 180 degrees and at which one monkey showed a local peak in the gain of pursuit.(ABSTRACT TRUNCATED AT 400 WORDS)

scholarly journals Strobing of Radar Marks for Trajectory Filtration in a Body-Fixed Frame

2019 ◽  
Vol 22 (5) ◽  
pp. 71-79
Author(s):  
Konstantin K. Vasiliev ◽  
Alexey V. Mattis ◽  
Oleg V. Saverkin
Keyword(s):  
Target Tracking ◽  
High Speed ◽  
The Body ◽  
Target Motion ◽  
Motion Model ◽  
New Approach ◽  
Fixed Frame ◽  
Speed Range ◽  
Radar Information ◽  
Analytical Expressions

Introduction. Modern air targets, particularly drones, are becoming less noticeable, while their manoeuvrability continues to improve. Trajectory processing algorithms have also been improved in order to provide for effective tracking of highly manoeuvring targets. The accuracy of filtering trajectory parameters is largely determined by the reliability of radar information. This has also required an enhanced role for strobe algorithms and the need to increase the effectiveness of strobe radar marks.Aim. To develop and investigate the efficiency of a trajectory strobe algorithm based on the target motion model in a high-speed coordinate system associated with the direction of the target motion and involving the formation of a strobe in the form of a truncated elliptical sector.Materials and methods. The study considered the target motion model in the body-fixed frame. This model was taken as the basis for new trajectory filtering algorithms based on Kalman filtering. Existing methods for strobing radar marks of the target were considered and a new approach based on filtering in the body-fixed frame proposed. The new algorithm assumes the formation of a strobe in the form of a truncated elliptical sector. This form corresponds to the most probable location of the marks of the tracked target. The effectiveness of the proposed solutions is confirmed by the results of mathematical modelling carried out using MATLAB.Results. The study produced analytical expressions for the motion model, recurrent filtering and strobe algorithm in the body-fixed frame. A comparative analysis of tracking effectiveness with the same volumes of the elliptical and proposed strobes was performed. It was established that the algorithm with strobe formation in the shape of a truncated elliptical sector provides for longer target tracking up to the time of the first loss of the mark for speed and highly manoeuvring targets, when compared to the elliptical strobe algorithm. In addition, the average duration of sector strobe tracking does not in practice depend on the initial speed of the target and provides greater accuracy for small measurement error values (less than 50 m) of the coordinates than in comparison with the elliptical one. Conclusion. The described results were achieved by the ability of the strobe in the body-fixed frame to adapt to the direction of motion and target manoeuvring, allowing high-quality target tracking within a larger speed range. Such strobe formation will also reduce the likelihood of skip-ping radar marks from the tracked target and will reduce the number of false marks belonging to other trajectories inside the strobe.

scholarly journals Gaussian process-based visual pursuit control with unknown target motion learning in three dimensions

2021 ◽  
Vol 14 (1) ◽  
pp. 116-127
Author(s):  
Marco Omainska ◽  
Junya Yamauchi ◽  
Thomas Beckers ◽  
Takeshi Hatanaka ◽  
Sandra Hirche ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Target Motion ◽  
Three Dimensions ◽  
Visual Pursuit ◽  
Motion Learning ◽  
Pursuit Control
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