scholarly journals Method for considering angle error in the position estimation of a moving target using ultrasonic array

2021 ◽  
Author(s):  
Masamichi Hattori ◽  
Asuka Tsujii ◽  
Takashi Kasashima ◽  
Hiroyuki Hatano ◽  
Takaya Yamazato
Keyword(s):  
Position Estimation ◽  
Moving Target ◽  
Angle Error ◽  
Ultrasonic Array

Tracking Compensation of a Moving Target for a Biped Robot Based on Vision Sensor

2010 ◽  
Vol 44-47 ◽  
pp. 788-793
Author(s):  
Yun De Shen ◽  
Dong Soo Cho ◽  
Chang Doo Kee ◽  
Zhen Zhe Li
Keyword(s):  
Rotation Angle ◽  
Biped Robot ◽  
Position Estimation ◽  
Moving Target ◽  
Vision Sensor ◽  
Matching Algorithm ◽  
Image Frame ◽  
Affine Transform ◽  
Search Area ◽  
Starting Point

In this paper, the visual tracking algorithm for a moving target is proposed for the biped robot of which camera movement is irregular. Hexagonal Matching Algorithm is used to measure the changes of size, location, and rotation angle for a moving object from its image frame. For enhancing the efficiency of the tracking, we can adaptively adjust the starting point and the size of search area from the image information obtained. Finally, by using Affine Transform and Kalman Filter, the position estimation of the moving target is refined against the swing of the camera. Experiments with 20-DOF biped robot using mono vision sensor are implemented to prove the reliability of the proposed method.

Moving Target Indication And Position Estimation With Autodyne Detection

1989 ◽  
Author(s):  
Marek Elbaum ◽  
Betsy Kingsbury ◽  
Jerzy Nowakowski ◽  
Ted Shultz ◽  
Victor Simuoli
Keyword(s):  
Position Estimation ◽  
Moving Target

Distributed Particle Filter With Online Model Learning for Localization Using Time-Difference-of-Arrival (TDOA) Measurements

2020 ◽  
Author(s):  
Chandler J. Panetta ◽  
Osama N. Ennasr ◽  
Xiaobo Tan
Keyword(s):  
Particle Filter ◽  
Nonlinear Filtering ◽  
Probability Distributions ◽  
Position Estimation ◽  
Propagation Model ◽  
Time Difference ◽  
Moving Target ◽  
Model Learning ◽  
Movement Model ◽  
Time Difference Of Arrival

Abstract The problem of localizing a moving target arises in various forms in wireless sensor networks. Deploying multiple sensing receivers and using the time-difference-of-arrival (TDOA) of the target’s emitted signal is widely considered an effective localization technique. Traditionally, TDOA-based algorithms adopt a centralized approach where all measurements are sent to a predefined reference node for position estimation. More recently, distributed TDOA-based localization algorithms have been shown to improve the robustness of these estimates. For target models governed by highly stochastic processes, the method of nonlinear filtering and state estimation must be carefully considered. In this work, a distributed TDOA-based particle filter algorithm is proposed for localizing a moving target modeled by a discrete-time correlated random walk (DCRW). We present a method for using data collected by the particle filter to estimate the unknown probability distributions of the target’s movement model, and then apply the distribution estimates to recursively update the particle filter’s propagation model. The performance of the distributed approach is evaluated through numerical simulation, and we show the benefit of using a particle filter with online model learning by comparing it with the non-adaptive approach.

scholarly journals Monocular Visual Position and Attitude Estimation Method of a Drogue Based on Coaxial Constraints

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5673
Author(s):  
Kedong Zhao ◽  
Yongrong Sun ◽  
Yi Zhang ◽  
Hua Li
Keyword(s):  
Spatial Structure ◽  
Estimation Method ◽  
Attitude Estimation ◽  
Position Estimation ◽  
Normal Vector ◽  
Angle Error ◽  
Fusion Algorithm ◽  
Aerial Refueling ◽  
The Matrix ◽  
The Mean

In aerial refueling, there exists deformation of the circular feature on the drogue’s stabilizing umbrella to a certain extent, which causes the problem of duality of position estimation by a single circular feature. In this paper, a monocular visual position and attitude estimation method of a drogue is proposed based on the coaxial constraints. Firstly, a procedure for scene recovery from one single circle is introduced. The coaxial constraints of the drogue are proposed and proved to be useful for the duality’s elimination by analyzing the matrix of the spatial structure. Furthermore, we came up with our method, which is composed of fitting the parameters of the spatial circles by restoring the 3D points on it, using the two-level coaxial constraints to eliminate the duality, and optimizing the normal vector of the plane where the inner circle is located. Finally, the effectiveness and robustness of the method proposed in this paper are verified, and the influence of the coaxial circle’s spatial structure on the method is explored through simulations of and experiments on a drogue model. Under the interference of a large amount of noise, the duality elimination success rate of our method can also be maintained at a level that is more than 10% higher than others. In addition, the accuracy of the normal vector obtained by the fusion algorithm is improved, and the mean angle error is reduced by more than 26.7%.

LSD Based Pose and Position Estimation for UAV Landing

2014 ◽  
Vol 631-632 ◽  
pp. 602-605 ◽  
Author(s):  
Xin Ying Liu ◽  
Ping Ping Liu
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Line Segment ◽  
Position Estimation ◽  
Position Error ◽  
The State ◽  
Experimental Results ◽  
Angle Error ◽  
Precise Estimation ◽  
Line Segment Detection ◽  
Aerial Vehicle

Currently, there has been growing interest in unmanned aerial vehicle (UAV) during the landing. With the widespread use of the UAVs, a more precise estimation on pose and position in the process of landing is required to support the higher-level applications. In this paper, the estimation of pose and position based on the line segment detection (LSD) is proposed. By applying a vision camera, a landmark is detected using the effective LSD algorithm. Then a line-based vision model is built to calculate the pose and position of the UAV. Experimental results show that the state solutions of the proposed method are effective with different shape of landmarks, and the accuracy is minute-level in pose angle error and centimeter-level in position error.

Where's That Wascally Wilber? The Challenges of Hitting a Moving Target

PsycCRITIQUES ◽  
2007 ◽  
Vol 52 (13) ◽  
Author(s):  
Douglas A. MacDonald
Keyword(s):  
Moving Target
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