Performance Analysis of Location Estimation Algorithm Using an Intelligent Coordination Scheme in RTLS

2006 ◽  
pp. 829-835 ◽  
Author(s):  
Seung-Hee Jeong ◽  
Hyun-Jae Lee ◽  
Joon-Sung Lee ◽  
Chang-Heon Oh
Keyword(s):  
Performance Analysis ◽  
Estimation Algorithm ◽  
Location Estimation ◽  
Coordination Scheme

Performance Analysis on an Object Location Estimation Algorithm Using a Single Receiver

2015 ◽  
Vol 42 (2) ◽  
pp. 264-271
Author(s):  
Enkhzaya Myagmar ◽  
Soonryang Kwon ◽  
Dong Myung Lee
Keyword(s):  
Performance Analysis ◽  
Estimation Algorithm ◽  
Location Estimation ◽  
Object Location ◽  
Single Receiver

WALE: a weighted adaptive location estimation algorithm

2018 ◽  
Vol 10 (7) ◽  
pp. 2621-2632 ◽  
Author(s):  
Darshak Sundar ◽  
Siddharth Sendil ◽  
Vasanth Subramanian ◽  
Vidhya Balasubramanian
Keyword(s):  
Estimation Algorithm ◽  
Location Estimation

scholarly journals Control system design of duct cleaning robot capable of overcoming L and T-shaped ducts

2020 ◽  
Vol 9 (2) ◽  
pp. 123
Author(s):  
Myeong In Seo ◽  
Woo Jin Jang ◽  
Junhwan Ha ◽  
Kyongtae Park ◽  
Dong Hwan Kim
Keyword(s):  
Control Method ◽  
Estimation Algorithm ◽  
Location Estimation ◽  
Positional Error ◽  
Cleaning Robot ◽  
Communication Module ◽  
Duct Cleaning Robot ◽  
Robot Position ◽  
Uwb Communication ◽  
Duct Cleaning

This study introduces the control method of duct cleaning robot that enables real-time position tracking and self-driving over L-shaped and T-shaped duct sections. The developed robot has three legs and is designed to flexibly respond to duct sizes. The position of the robot inside the duct is identified using the UWB communication module and the location estimation algorithm. Although UWB communication has relatively large distance error within the metal, the positional error was reduced by introducing appropriate filters to estimate the robot position accurately. TCP/IP communication allows commands to be sent between the PC and the robot and to receive live images of the camera attached to the robot. Using Haar-like and classifiers, the robot can recognize the type of duct that is difficult to overcome, such as L-shaped and T-shaped duct, and it moves successfully inside the duct according to the corresponding moving algorithms.

scholarly journals Spatio-Temporal Synchronization of Cross Section Based Sensors for High Precision Microscopic Traffic Data Reconstruction

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3193
Author(s):  
Adrian Fazekas ◽  
Markus Oeser
Keyword(s):  
Data Acquisition ◽  
Cross Section ◽  
Intelligent Transportation Systems ◽  
Estimation Algorithm ◽  
Initial Step ◽  
Original Data ◽  
Transportation Systems ◽  
Location Estimation ◽  
Traffic Data ◽  
Multiple Sensors

The next generation of Intelligent Transportation Systems (ITS) will strongly rely on a high level of detail and coverage in traffic data acquisition. Beyond aggregated traffic parameters like the flux, mean speed, and density used in macroscopic traffic analysis, a continuous location estimation of individual vehicles on a microscopic scale will be required. On the infrastructure side, several sensor techniques exist today that are able to record the data of individual vehicles at a cross-section, such as static radar detectors, laser scanners, or computer vision systems. In order to record the position data of individual vehicles over longer sections, the use of multiple sensors along the road with suitable synchronization and data fusion methods could be adopted. This paper presents appropriate methods considering realistic scale and accuracy conditions of the original data acquisition. Datasets consisting of a timestamp and a speed for each individual vehicle are used as input data. As a first step, a closed formulation for a sensor offset estimation algorithm with simultaneous vehicle registration is presented. Based on this initial step, the datasets are fused to reconstruct microscopic traffic data using quintic Beziér curves. With the derived trajectories, the dependency of the results on the accuracy of the individual sensors is thoroughly investigated. This method enhances the usability of common cross-section-based sensors by enabling the deriving of non-linear vehicle trajectories without the necessity of precise prior synchronization.

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