TU-E-204B-07: Real-Time 3D Target Position Estimation Using a Single KV Imager Combined with an External Respiratory Monitor during Arc and Static Beam Delivery

2010 ◽  
Vol 37 (6Part13) ◽  
pp. 3402-3403
Author(s):  
B Cho ◽  
P Poulsen ◽  
D Ruan ◽  
A Sawant ◽  
P Keall
Keyword(s):  
Real Time ◽  
Target Position ◽  
Position Estimation ◽  
Beam Delivery

scholarly journals Analysis of the Position Estimation Error of a Local Positioning System utilizing Mobile Anchors

2018 ◽  
Author(s):  
Sang C. Lee ◽  
Syamsul Rizal ◽  
HeungJu Ahn
Keyword(s):  
Real Time ◽  
Estimation Error ◽  
Gaussian Model ◽  
Target Position ◽  
Position Estimation ◽  
Position Error ◽  
Positioning System ◽  
Target Node ◽  
Local Positioning System ◽  
Anchor Nodes

This paper proposes a real-time local positioning system (RT-LPs) utilizing a mobile platform equipped with three anchor nodes placed in a right-angle triangle formation for a real-time locating system (RTLS). After deriving an analytic formula to calculate the target position utilizing the measured distances among anchor nodes and the target node, we find that four parameters have an effect on the position error of the target node. The spacing between anchors is a design parameter that must be large enough to the reduce position error. However, the distance from the anchor node to the target node is an operation parameter that must be small enough to reduce the position error. Additionally, the measured ranges among the anchors and the target node have probabilistic distributions with a mean and variance, which are dominant parameters that have effects on the position error. A comparison study was conducted to determine the effects of the parameters of the target position in both a simulation and an experiment, showing rates of approximately 4% ~ 10%. These findings indicate that our simulation can work properly with the proposed method after assuming that the distance error is a Gaussian model.

scholarly journals Calibration Method of Array Errors for Wideband MIMO Imaging Radar Based on Multiple Prominent Targets

2021 ◽  
Vol 13 (15) ◽  
pp. 2997
Author(s):  
Zheng Zhao ◽  
Weiming Tian ◽  
Yunkai Deng ◽  
Cheng Hu ◽  
Tao Zeng
Keyword(s):  
Multiple Input Multiple Output ◽  
Estimation Method ◽  
Target Position ◽  
Position Estimation ◽  
Least Square ◽  
Antenna Structure ◽  
Imaging Radar ◽  
Position Errors ◽  
Calibration Methods ◽  
Array Errors

Wideband multiple-input-multiple-output (MIMO) imaging radar can achieve high-resolution imaging with a specific multi-antenna structure. However, its imaging performance is severely affected by the array errors, including the inter-channel errors and the position errors of all the transmitting and receiving elements (TEs/REs). Conventional calibration methods are suitable for the narrow-band signal model, and cannot separate the element position errors from the array errors. This paper proposes a method for estimating and compensating the array errors of wideband MIMO imaging radar based on multiple prominent targets. Firstly, a high-precision target position estimation method is proposed to acquire the prominent targets’ positions without other equipment. Secondly, the inter-channel amplitude and delay errors are estimated by solving an equation-constrained least square problem. After this, the element position errors are estimated with the genetic algorithm to eliminate the spatial-variant error phase. Finally, the feasibility and correctness of this method are validated with both simulated and experimental datasets.

Accurate and real-time human-joint-position estimation for a patient-transfer robot using a two-level convolutional neutral network

2021 ◽  
Vol 139 ◽  
pp. 103735
Author(s):  
Mengqian Chen ◽  
Jiang Wu ◽  
Shunda Li ◽  
Jinyue Liu ◽  
Hideo Yokota ◽  
...  
Keyword(s):  
Real Time ◽  
Position Estimation ◽  
Patient Transfer ◽  
Neutral Network ◽  
Joint Position ◽  
Human Joint

scholarly journals Real-Time Onboard 3D State Estimation of an Unmanned Aerial Vehicle in Multi-Environments Using Multi-Sensor Data Fusion

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 919 ◽  
Author(s):  
Hao Du ◽  
Wei Wang ◽  
Chaowen Xu ◽  
Ran Xiao ◽  
Changyin Sun
Keyword(s):  
Data Fusion ◽  
State Estimation ◽  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Position Estimation ◽  
Sensor Data ◽  
Measurement Unit ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Aerial Vehicle

The question of how to estimate the state of an unmanned aerial vehicle (UAV) in real time in multi-environments remains a challenge. Although the global navigation satellite system (GNSS) has been widely applied, drones cannot perform position estimation when a GNSS signal is not available or the GNSS is disturbed. In this paper, the problem of state estimation in multi-environments is solved by employing an Extended Kalman Filter (EKF) algorithm to fuse the data from multiple heterogeneous sensors (MHS), including an inertial measurement unit (IMU), a magnetometer, a barometer, a GNSS receiver, an optical flow sensor (OFS), Light Detection and Ranging (LiDAR), and an RGB-D camera. Finally, the robustness and effectiveness of the multi-sensor data fusion system based on the EKF algorithm are verified by field flights in unstructured, indoor, outdoor, and indoor and outdoor transition scenarios.

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