Matches in SemOpenAlex for { <https://semopenalex.org/work/W4226156928> ?p ?o ?g. }
Showing items 1 to 66 of
66
with 100 items per page.
- W4226156928 abstract "<p>Tracking multiple objects is a challenging problem for an automated system, with applications in many domains. Typically the system must be able to represent the posterior distribution of the state of the targets, using a recursive algorithm that takes information from noisy measurements. However, in many important cases the number of targets is also unknown, and has also to be estimated from data. The Probability Hypothesis Density (PHD) filter is an effective approach for this problem. The method uses a first-order moment approximation to develop a recursive algorithm for the optimal Bayesian filter. The PHD recursion can implemented in closed form in some restricted cases, and more generally using Sequential Monte Carlo (SMC) methods. The assumptions made in the PHD filter are appealing for computational reasons in real-time tracking implementations. These are only justifiable when the signal to noise ratio (SNR) of a single target is high enough that remediates the loss of information from the approximation. Although the original derivation of the PHD filter is based on functional expansions of belief-mass functions, it can also be developed by exploiting elementary constructions of Poisson processes. This thesis presents novel strategies for improving the Sequential Monte Carlo implementation of PHD filter using the point process approach. Firstly, we propose a post-processing state estimation step for the PHD filter, using Markov Chain Monte Carlo methods for mixture models. Secondly, we develop recursive Bayesian smoothing algorithms using the approximations of the filter backwards in time. The purpose of both strategies is to overcome the problems arising from the PHD filter assumptions. As a motivating example, we analyze the performance of the methods for the difficult problem of person tracking in crowded environments</p>" @default.
- W4226156928 created "2022-05-05" @default.
- W4226156928 creator A5071837889 @default.
- W4226156928 date "2021-11-10" @default.
- W4226156928 modified "2023-10-12" @default.
- W4226156928 title "State Estimation and Smoothing for the Probability Hypothesis Density Filter" @default.
- W4226156928 doi "https://doi.org/10.26686/wgtn.16984759.v1" @default.
- W4226156928 hasPublicationYear "2021" @default.
- W4226156928 type Work @default.
- W4226156928 citedByCount "0" @default.
- W4226156928 crossrefType "dissertation" @default.
- W4226156928 hasAuthorship W4226156928A5071837889 @default.
- W4226156928 hasBestOaLocation W42261569281 @default.
- W4226156928 hasConcept C105795698 @default.
- W4226156928 hasConcept C106131492 @default.
- W4226156928 hasConcept C107673813 @default.
- W4226156928 hasConcept C111350023 @default.
- W4226156928 hasConcept C11413529 @default.
- W4226156928 hasConcept C126255220 @default.
- W4226156928 hasConcept C154945302 @default.
- W4226156928 hasConcept C168773036 @default.
- W4226156928 hasConcept C19499675 @default.
- W4226156928 hasConcept C22597639 @default.
- W4226156928 hasConcept C31972630 @default.
- W4226156928 hasConcept C33923547 @default.
- W4226156928 hasConcept C3770464 @default.
- W4226156928 hasConcept C40343088 @default.
- W4226156928 hasConcept C41008148 @default.
- W4226156928 hasConcept C52421305 @default.
- W4226156928 hasConcept C70518719 @default.
- W4226156928 hasConcept C76826599 @default.
- W4226156928 hasConceptScore W4226156928C105795698 @default.
- W4226156928 hasConceptScore W4226156928C106131492 @default.
- W4226156928 hasConceptScore W4226156928C107673813 @default.
- W4226156928 hasConceptScore W4226156928C111350023 @default.
- W4226156928 hasConceptScore W4226156928C11413529 @default.
- W4226156928 hasConceptScore W4226156928C126255220 @default.
- W4226156928 hasConceptScore W4226156928C154945302 @default.
- W4226156928 hasConceptScore W4226156928C168773036 @default.
- W4226156928 hasConceptScore W4226156928C19499675 @default.
- W4226156928 hasConceptScore W4226156928C22597639 @default.
- W4226156928 hasConceptScore W4226156928C31972630 @default.
- W4226156928 hasConceptScore W4226156928C33923547 @default.
- W4226156928 hasConceptScore W4226156928C3770464 @default.
- W4226156928 hasConceptScore W4226156928C40343088 @default.
- W4226156928 hasConceptScore W4226156928C41008148 @default.
- W4226156928 hasConceptScore W4226156928C52421305 @default.
- W4226156928 hasConceptScore W4226156928C70518719 @default.
- W4226156928 hasConceptScore W4226156928C76826599 @default.
- W4226156928 hasLocation W42261569281 @default.
- W4226156928 hasLocation W42261569282 @default.
- W4226156928 hasOpenAccess W4226156928 @default.
- W4226156928 hasPrimaryLocation W42261569281 @default.
- W4226156928 hasRelatedWork W1569560753 @default.
- W4226156928 hasRelatedWork W1704271451 @default.
- W4226156928 hasRelatedWork W2027362388 @default.
- W4226156928 hasRelatedWork W2080514815 @default.
- W4226156928 hasRelatedWork W2097490190 @default.
- W4226156928 hasRelatedWork W2110766888 @default.
- W4226156928 hasRelatedWork W2803154851 @default.
- W4226156928 hasRelatedWork W3197391826 @default.
- W4226156928 hasRelatedWork W4226055627 @default.
- W4226156928 hasRelatedWork W4226156928 @default.
- W4226156928 isParatext "false" @default.
- W4226156928 isRetracted "false" @default.
- W4226156928 workType "dissertation" @default.