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GNSS time offset monitoring based on the single difference among systems
期刊论文
IET RADAR SONAR AND NAVIGATION, 2020, 卷号: 14, 期号: 2, 页码: 299-302
作者:
Tu, Rui
;
Zhang, Pengfei
;
Zhang, Rui
;
Fan, Lihong
;
Liu, Jinhai
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  |  
浏览/下载:14/0
  |  
提交时间:2020/11/09
delays
satellite navigation
radio receivers
Global Positioning System
GNSS time
single difference
time offset monitoring
multiple Global Navigation Satellite Systems
compatibility
interoperability
conventional signal space approaches
single-point positioning
SPP
precise point positioning
single-difference point positioning
SDPP
receiver clock parameter
common model errors
estimated time
smooth estimation
time offset estimation
different stations
system biases
Numerical Simulation of Bias Effect on Dose Rate Effect of SiGe Heterojunction Bipolar Transistor
期刊论文
Yuanzineng Kexue Jishu/Atomic Energy Science and Technology, 2017, 卷号: 51, 页码: 549-554
作者:
Ma, Ting
;
Zhang, Jin-Xin
;
He, Chao-Hui
;
Tang, Du
;
Li, Pei
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  |  
浏览/下载:7/0
  |  
提交时间:2019/11/26
Bias effects
Different biases
Dose-rate effects
Reverse bias
SiGe heterojunction bipolar transistor
TCAD software
Three-dimensional numerical simulations
Transient current
Intelligent MRTD testing for thermal imaging system using ANN (EI CONFERENCE)
会议论文
ICO20: Remote Sensing and Infrared Devices and Systems, August 21, 2005 - August 26, 2005, Changchun, China
Sun J.
;
Ma D.
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浏览/下载:21/0
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提交时间:2013/03/25
The Minimum Resolvable Temperature Difference (MRTD) is the most widely accepted figure for describing the performance of a thermal imaging system. Many models have been proposed to predict it. The MRTD testing is a psychophysical task
for which biases are unavoidable. It requires laboratory conditions such as normal air condition and a constant temperature. It also needs expensive measuring equipments and takes a considerable period of time. Especially when measuring imagers of the same type
the test is time consuming. So an automated and intelligent measurement method should be discussed. This paper adopts the concept of automated MRTD testing using boundary contour system and fuzzy ARTMAP
but uses different methods. It describes an Automated MRTD Testing procedure basing on Back-Propagation Network. Firstly
we use frame grabber to capture the 4-bar target image data. Then according to image gray scale
we segment the image to get 4-bar place and extract feature vector representing the image characteristic and human detection ability. These feature sets
along with known target visibility
are used to train the ANN (Artificial Neural Networks). Actually it is a nonlinear classification (of input dimensions) of the image series using ANN. Our task is to justify if image is resolvable or uncertainty. Then the trained ANN will emulate observer performance in determining MRTD. This method can reduce the uncertainties between observers and long time dependent factors by standardization. This paper will introduce the feature extraction algorithm
demonstrate the feasibility of the whole process and give the accuracy of MRTD measurement.
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