Results 11 to 20 of about 6,182,412 (149)
Time-Matching Random Finite Set-Based Filter for Radar Multi-Target Tracking
Sensors, 2018 The random finite set (RFS) approach provides an elegant Bayesian formulation of the multi-target tracking (MTT) problem without the requirement of explicit data association.
Defu Jiang +5 more
doaj +2 more sources
IET Signal Processing, 2022 This paper proposes a random finite set (RFS)‐based algorithm to deal with the tracking problem of multiple non‐rigid extended targets (MNRET) with irregular shapes in the presence of clutter, false alarms and missed detection.
Sunyong Wu +3 more
doaj +1 more source
Stochastic finite elements: Where is the physics? [PDF]
Theoretical and Applied Mechanics, 2011 The micromechanics based on the Hill-Mandel condition indicates that the majority of stochastic finite element methods hinge on random field (RF) models of material properties (such as Hooke’s law) having no physical content, or even at odds with ...
Ostoja-Starzewski Martin
doaj +1 more source
Label GM-PHD Filter Based on Threshold Separation Clustering
Sensors, 2021 Gaussian mixture probability hypothesis density (GM-PHD) filtering based on random finite set (RFS) is an effective method to deal with multi-target tracking (MTT).
Kuiwu Wang, Qin Zhang, Xiaolong Hu
doaj +1 more source
Arbitrary clutter extended target probability hypothesis density filter
IET Radar, Sonar & Navigation, 2021 Based on the random finite set (RFS) framework and the probability hypothesis density (PHD) filter, the extended target PHD (ET‐PHD) filter is proposed for multiple extended target tracking.
Xinglin Shen +4 more
doaj +1 more source
A Comparison of Error Bounds for a Nonlinear Tracking System with Detection Probability Pd < 1
Sensors, 2012 Error bounds for nonlinear filtering are very important for performance evaluation and sensor management. This paper presents a comparative study of three error bounds for tracking filtering, when the detection probability is less than unity.
Xiqin Wang +3 more
doaj +1 more source
Transformation Methods for Static Field Problems With Random Domains [PDF]
, 2011 The numerical solution of partial differential equations onto random domains can be done by using a mapping transforming this random domain into a deterministic domain. The issue is then to determine this one to one random mapping.
J. C. Mipo +5 more
core +1 more source
Explicit filtering equations for labelled random finite sets [PDF]
, 2015 We decompose a probability density function (PDF) of a labelled random finite set (RFS) into a probability mass function over a set of labels and a PDF on a vector-valued multitarget state given the labels. Using this decomposition, we write the Bayesian
M. Morelande (23306635) +1 more
core +2 more sources
Solution of Static Field Problems With Random Domains [PDF]
, 2010 A method to solve stochastic partial differential equations on random domains consists in using a one-to-one random mapping function which transforms the random domain into a deterministic domain.
Stephane Clenet +7 more
core +1 more source
Comparison of two approaches to compute magnetic field in problems with random domains [PDF]
, 2012 This paper is a postprint of a paper submitted to and accepted for publication in Science, Measurement & Technology, IET and is subject to Institution of Engineering and Technology Copyright.
MAC, Duy Hung +2 more
core +1 more source