Results 221 to 230 of about 87,335 (264)

Finite Random Sets

1997
In this and the four chapters which follow, we develop the random set approach to data fusion summarized in Section 2.5 of Chapter 2. This chapter sets the stage by developing the mathematical cornerstone of this approach: the concept of a finite random set.
I. R. Goodman, Ronald P. S. Mahler, Hung T. Nguyen   +2 more
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Labeled Random Finite Sets With Moment Approximation

IEEE Transactions on Signal Processing, 2017
The probability hypothesis density (PHD) filter was proposed as a practical approximation of the multitarget Bayes filter. The cardinalized PHD (CPHD) filter improves on the PHD filter by propagating cardinality distribution. However, both the PHD and CPHD filters have limitations in dealing with missed detections, extracting target state in their ...
Zhejun Lu, Weidong Hu, Thia Kirubarajan
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A labeled random finite set spawning model

2017 International Conference on Control, Automation and Information Sciences (ICCAIS), 2017
Previous labeled random finite set filter developments use a target motion model that only accounts for survival and birth. While such a model provides the means for a multi-target tracking filter such as the Generalized Labeled Multi-Bernoulli filter to capture target births and deaths in a wide variety of applications, it lacks the capability to ...
Daniel S. Bryant, Ba-Tuong Vo, Ba-Ngu Vo, Brandon A. Jones   +3 more
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Why Random Finite Sets?

2011
We begin the justification for the use of RFSs by re-evaluating the basic issues of feature representation, and considering the fundamental mathematical relationship between environmental feature representations, and robot motion. We further the justification for the use of RFSs in FBRM and SLAM by considering an issue of fundamental mathematical ...
Mullane, J., Vo, Ba-Ngu, Adams, M., Vo, B.   +3 more
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A Random-Finite-Set Approach to Bayesian SLAM

IEEE Transactions on Robotics, 2011
This paper proposes an integrated Bayesian frame work for feature-based simultaneous localization and map building (SLAM) in the general case of uncertain feature number and data association. By modeling the measurements and feature map as random finite sets (RFSs), a formulation of the feature-based SLAM problem is presented that jointly estimates the
John Mullane, Ba-Ngu Vo, Martin David Adams, Ba-Tuong Vo   +3 more
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The Greatest of a Finite Set of Random Variables

Operations Research, 1961
The variables ξ1, …, ξn have a joint normal distribution. We are concerned with the calculation or approximation of max(ξ1, …, ξn). Current analyses and tables handle the case in which the ξı are independently distributed with common expected values and common variances.
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Estimation with Random Finite Sets

2011
The previous chapter provided the motivation to adopt an RFS representation for the map in both FBRM and SLAM problems. The main advantage of the RFS formulation is that the dimensions of the measurement likelihood and the predicted FBRM or SLAM state do not have to be compatible in the application of Bayes theorem, for optimal state estimation.
Mullane, J., Vo, Ba-Ngu, Adams, M., Vo, B.   +3 more
openaire   +2 more sources

Mobile Robotics in a Random Finite Set Framework

2011
This paper describes the Random Finite Set approach to Bayesian mobile robotics, which is based on a natural multi-object filtering framework, making it well suited to both single and swarm-based mobile robotic applications. By modeling the measurements and feature map as random finite sets (RFSs), joint estimates the number and location of the objects
John Mullane, Ba-Ngu Vo, Martin Adams, Ba-Tuong Vo   +3 more
openaire   +3 more sources

Global robot localization with random finite set statistics

2010 13th International Conference on Information Fusion, 2010
We re-examine the problem of global localization of a robot using a rigorous Bayesian framework based on the idea of random finite sets. Random sets allow us to naturally develop a complete model of the underlying problem accounting for the statistics of missed detections and of spurious/erroneously detected (potentially unmodeled) features along with ...
Adrian N. Bishop, Patric Jensfelt
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Explicit filtering equations for labelled random finite sets

2015 International Conference on Control, Automation and Information Sciences (ICCAIS), 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 filtering recursion for labelled RFSs in an explicit form. The resulting formulas are of conceptual
Ángel F. García-Fernández, Mark R. Morelande   +1 more
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