Publication Date:
2024-04-11
Description:
This book includes papers from the section “Multisensor Information Fusion”, from Sensors between 2018 to 2019. It focuses on the latest research results of current multi-sensor fusion technologies and represents the latest research trends, including traditional information fusion technologies, estimation and filtering, and the latest research, artificial intelligence involving deep learning.
Keywords:
TA1-2040
;
T1-995
;
similarity measure
;
information filter
;
out-of-sequence
;
Hellinger distance
;
coefficient of determination maximization strategy
;
uncertainty measure
;
embedded systems
;
Internet of things (IoT)
;
random delays
;
adaptive distance function
;
random finite set
;
Dempster–Shafer evidence theory (DST)
;
safe trajectory
;
health reliability degree
;
dynamic optimization
;
state probability approximation
;
sensors bias
;
multi-environments
;
belief entropy
;
quaternion
;
closed world
;
Gaussian process regression
;
Gaussian mixture model (GMM)
;
intelligent transport system
;
multirotor UAV
;
multi-sensor system
;
attitude
;
time-domain data fusion
;
precision landing
;
Industry 4.0
;
magnetic angular rate and gravity (MARG) sensor
;
uncertainty
;
unscented information filter
;
data classification
;
high-definition map
;
global information
;
inconsistent data
;
extended belief entropy
;
sensor system
;
Steffensen’s iterative method
;
SLAM
;
the Range-Range-Range frame
;
evidential reasoning
;
belief functions
;
powered two wheels (PTW)
;
electronic nose
;
particle swarm optimization
;
grey group decision-making
;
user experience platform
;
complex surface measurement
;
DoS attack
;
extended Kalman filter
;
ICP
;
Gaussian density peak clustering
;
artificial marker
;
random parameter matrices
;
optimal estimate
;
local structure descriptor
;
object classification
;
domain adaption
;
networked systems
;
expectation maximization (EM) algorithm
;
attitude estimation
;
Gaussian process model
;
least-squares smoothing
;
target positioning
;
RFS
;
spectral clustering
;
maintenance decision
;
multi-target tracking
;
GMPHD
;
time-distributed ConvLSTM model
;
non-rigid feature matching
;
unknown inputs
;
cardiac PET
;
subspace alignment
;
gradient domain
;
multi-sensor measurement
;
data fusion
;
Bar-Shalom Campo
;
Kalman filter
;
signal feature extraction methods
;
sensor data fusion algorithm
;
distributed architecture
;
predictive modeling techniques
;
Gaussian mixture model
;
self-reporting
;
deep learning
;
mutual support degree
;
security zones
;
sensor array
;
soft sensor
;
aircraft pilot
;
projection
;
vehicle-to-everything
;
distributed intelligence system
;
square-root cubature Kalman filter
;
information fusion
;
evidence combination
;
LiDAR
;
feature representations
;
multi-sensor information fusion
;
linear constraints
;
galvanic skin response
;
decision-level sensor fusion
;
most suitable parameter form
;
Pignistic vector angle
;
SINS/DVL integrated navigation
;
fault diagnosis
;
facial expression
;
yaw estimation
;
dual gating
;
multi-sensor data fusion
;
multisensor system
;
A* search algorithm
;
data fusion architectures
;
drift compensation
;
augmented state Kalman filtering (ASKF)
;
manifold
;
nested iterative method
;
data preprocessing
;
interference suppression
;
conflicting evidence
;
sonar network
;
Gaussian process
;
health management decision
;
state estimation
;
eye-tracking
;
high-dimensional fusion data (HFD)
;
MEMS accelerometer and gyroscope
;
multitarget tracking
;
gaussian mixture probability hypothesis density
;
integer programming
;
image registration
;
Dempster–Shafer evidence theory
;
linear regression
;
data association
;
nonlinear system
;
covariance matrix
;
multi-source data fusion
;
fuzzy neural network
;
least-squares filtering
;
fire source localization
;
network flow theory
;
weight maps
;
camera
;
plane matching
;
calibration
;
unmanned aerial vehicle
;
fixed-point filter
;
workload
;
intelligent and connected vehicles
;
mimicry security switch strategy
;
alumina concentration
;
the Range-Point-Range frame
;
spatiotemporal feature learning
;
distributed fusion
;
user experience evaluation
;
image fusion
;
vehicular localization
;
sensor fusion
;
vibration
;
parameter learning
;
weighted fusion estimation
;
data registration
;
pose estimation
;
surface quality control
;
trajectory reconstruction
;
land vehicle
;
square root
;
Deng entropy
;
multi-focus
;
EEG
;
low-cost sensors
;
sensor fusing
;
sensor data fusion
;
packet dropouts
;
estimation
;
industrial cyber-physical system (ICPS)
;
multi-sensor time series
;
multi-sensor network
;
Human Activity Recognition (HAR)
;
transfer
;
multisensor data fusion
;
convergence condition
;
interaction tracker
;
acoustic emission
;
Covariance Projection method
;
mix-method approach
;
orthogonal redundant inertial measurement units
;
sematic segmentation
;
Surface measurement
;
conflict measurement
;
user experience measurement
;
observable degree analysis
;
open world
;
novel belief entropy
;
cutting forces
;
machine health monitoring
;
Bayesian reasoning method
;
orientation
;
surface modelling
;
hybrid adaptive filtering
;
supervoxel
;
RTS smoother
;
Dempster-Shafer evidence theory (DST)
;
fast guided filter.
;
multi-sensor joint calibration
;
principal component analysis
;
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
Language:
English
Format:
application/octet-stream
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