Relevant bibliographies by topics / PHD filtering

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'PHD filtering'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "PHD filtering"

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Yin, Jian Jun, and Jian Qiu Zhang. "Convolution PHD Filtering for Nonlinear Non-Gaussian Models." Advanced Materials Research 213 (February 2011): 344–48. http://dx.doi.org/10.4028/www.scientific.net/amr.213.344.

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A novel probability hypothesis density (PHD) filter, called the Gaussian mixture convolution PHD (GMCPHD) filter was proposed. The PHD within the filter is approximated by a Gaussian sum, as in the Gaussian mixture PHD (GMPHD) filter, but the model may be non-Gaussian and nonlinear. This is implemented by a bank of convolution filters with Gaussian approximations to the predicted and posterior densities. The analysis results show the lower complexity, more amenable for parallel implementation of the GMCPHD filter than the convolution PHD (CPHD) filter and the ability to deal with complex obser
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Li, Chenming, Wenguang Wang, Thia Kirubarajan, Jinping Sun, and Peng Lei. "PHD and CPHD Filtering With Unknown Detection Probability." IEEE Transactions on Signal Processing 66, no. 14 (2018): 3784–98. http://dx.doi.org/10.1109/tsp.2018.2835398.

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Addesso, Paolo, Roberto Conte, Maurizio Longo, Rocco Restaino, and Gemine Vivone. "MAP-MRF Cloud Detection Based on PHD Filtering." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5, no. 3 (2012): 919–29. http://dx.doi.org/10.1109/jstars.2012.2191144.

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Gao, Song, Chao Bo Chen, and Qian Gong. "Maneuvering Target Tracking Algorithm Based on Particle PHD Filtering." Advanced Materials Research 989-994 (July 2014): 2212–15. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2212.

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As for the problem of maneuvering target tracking in the clutter environment, this paper combines IMM with PHD and realizes it through approach of particle filter. This algorithm avoids the troublesome problem of data association, and takes advantage of probability hypothesis density (PHD) filter in tracking maneuvering targets and interacting multi-model (IMM) algorithm in the field of model switching effectively, in the clutter environment, the status of the targets can be estimated precisely and steadily. This paper compares the proposed filtering algorithm with the classical IMM algorithm
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Zhang, Guoliang, Chunling Yang, and Yan Zhang. "An Adaptive PHD Filter for Multitarget Tracking with Multispectral Data Fusion." Journal of Spectroscopy 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/179039.

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In order to improve the detection and tracking performance of multiple targets from IR multispectral image sequences, the approach based on spectral fusion algorithm and adaptive probability hypothesis density (PHD) filter is proposed. Firstly, the nonstationary adaptive suppression method is proposed to remove the background clutter. Based on the multispectral image sequence, the spectral fusion method is used to detect the abnormal targets. Spectral fusion produces the appropriate binary detection model and the computational probability of detection. Secondly, the particle filtering-based ad
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Lee, Chee Sing, Daniel E. Clark, and Joaquim Salvi. "SLAM With Dynamic Targets via Single-Cluster PHD Filtering." IEEE Journal of Selected Topics in Signal Processing 7, no. 3 (2013): 543–52. http://dx.doi.org/10.1109/jstsp.2013.2251606.

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Petetin, Yohan, Mark Morelande, and Francois Desbouvries. "Marginalized particle PHD filters for multiple object Bayesian filtering." IEEE Transactions on Aerospace and Electronic Systems 50, no. 2 (2014): 1182–96. http://dx.doi.org/10.1109/taes.2014.120805.

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Li, Tiancheng, Franz Hlawatsch, and Petar M. Djuric. "Cardinality-Consensus-Based PHD Filtering for Distributed Multitarget Tracking." IEEE Signal Processing Letters 26, no. 1 (2019): 49–53. http://dx.doi.org/10.1109/lsp.2018.2878064.

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Li, Tiancheng, Mahendra Mallick, and Quan Pan. "A Parallel Filtering-Communication-Based Cardinality Consensus Approach for Real-Time Distributed PHD Filtering." IEEE Sensors Journal 20, no. 22 (2020): 13824–32. http://dx.doi.org/10.1109/jsen.2020.3004068.

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Ren, Yayun, and Benlian Xu. "A Quantitative Analysis on Two RFS-Based Filtering Methods for Multicell Tracking." Mathematical Problems in Engineering 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/495765.

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Multiobject filters developed from the theory of random finite sets (RFS) have recently become well-known methods for solving multiobject tracking problem. In this paper, we present two RFS-based filtering methods, Gaussian mixture probability hypothesis density (GM-PHD) filter and multi-Bernoulli filter, to quantitatively analyze their performance on tracking multiple cells in a series of low-contrast image sequences. The GM-PHD filter, under linear Gaussian assumptions on the cell dynamics and birth process, applies the PHD recursion to propagate the posterior intensity in an analytic form,
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Dissertations / Theses on the topic "PHD filtering"

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Feng, Pengming. "Enhanced particle PHD filtering for multiple human tracking." Thesis, University of Newcastle upon Tyne, 2016. http://hdl.handle.net/10443/3481.

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Video-based single human tracking has found wide application but multiple human tracking is more challenging and enhanced processing techniques are required to estimate the positions and number of targets in each frame. In this thesis, the particle probability hypothesis density (PHD) lter is therefore the focus due to its ability to estimate both localization and cardinality information related to multiple human targets. To improve the tracking performance of the particle PHD lter, a number of enhancements are proposed. The Student's-t distribution is employed within the state and measurement
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Pasha, Syed Electrical Engineering &amp Telecommunications Faculty of Engineering UNSW. "Problems in nonlinear Bayesian filtering." Awarded by:University of New South Wales. Electrical Engineering & Telecommunications, 2009. http://handle.unsw.edu.au/1959.4/43792.

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This dissertation presents solutions to two open problems in estimation theory. The first is a tractable analytical solution for problems in multi-target filtering which are too complex to solve using traditional techniques. The second explores a new approach to the nonlinear filtering problem for a general class of models. The approach to the multi-target filtering problem which involves jointly estimating a random process of the number of targets and their state, developed using the probability hypothesis density (PHD) filter alleviates the intractability of the problem by avoiding explicit
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Delande, Emmanuel. "Filtrage PHD multicapteur avec application à la gestion de capteurs." Phd thesis, Ecole Centrale de Lille, 2012. http://tel.archives-ouvertes.fr/tel-00688304.

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Le filtrage multiobjet est une technique de résolution du problème de détection et/ou suivi dans un contexte multicible. Cette thèse s'intéresse au filtre PHD (Probability Hypothesis Density), une célèbre approximation du filtre RFS (Random Finite Set) adaptée au cas où les observations sont le fruit d'un seul capteur. La première partie propose une construction rigoureuse du filtre PHD multicapteur exact et son expression simplifiée, sans approximation, grâce à un partitionnement joint de l'espace d'état des cibles et des capteurs. Avec cette nouvelle méthode, la solution exacte du filtre PHD
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Qiu, Jianbin. "New approaches to robust filtering design for uncertain dynamical systems with time delay /." access full-text access abstract and table of contents, 2009. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?phd-meem-b30082626f.pdf.

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Thesis (Ph.D.)--City University of Hong Kong, 2009.<br>"Submitted to Department of Manufacturing Engineering and Engineering Management in partial fulfillment of the requirements for the degree of Doctor of Philosophy." Includes bibliographical references (leaves [169]-205)
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Fortin, Benoît. "Méthodes conjointes de détection et suivi basé-modèle de cibles distribuées par filtrage non-linéaire dans les données lidar à balayage." Phd thesis, Université du Littoral Côte d'Opale, 2013. http://tel.archives-ouvertes.fr/tel-01021085.

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Dans les systèmes de perception multicapteurs, un point central concerne le suivi d'objets multiples. Dans mes travaux de thèse, le capteur principal est un télémètre laser à balayage qui perçoit des cibles étendues. Le problème desuivi multi-objets se décompose généralement en plusieurs étapes (détection, association et suivi) réalisées de manière séquentielle ou conjointe. Mes travaux ont permis de proposer des alternatives à ces méthodes en adoptant une approche "track-before-detect" sur cibles distribuées qui permet d'éviter la succession des traitements en proposant un cadre global de rés
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Rachamalla, Garuda Sekhar Reddy. "Developing a framework for text filtering applications /." Available to subscribers only, 2005. http://proquest.umi.com/pqdweb?did=1083544061&sid=14&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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Aysal, Tuncer Can. "Filtering and estimation theory first-order, polynomial and decentralized signal processing /." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 327 p, 2007. http://proquest.umi.com/pqdweb?did=1257806501&sid=6&Fmt=2&clientId=8331&RQT=309&VName=PQD.

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Chen, Chong. "Acceleration of Computer Based Simulation, Image Processing, and Data Analysis Using Computer Clusters with Heterogeneous Accelerators." University of Dayton / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=dayton148036732102682.

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Pace, Michele. "Stochastic models and methods for multi-object tracking." Phd thesis, Université Sciences et Technologies - Bordeaux I, 2011. http://tel.archives-ouvertes.fr/tel-00651396.

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La poursuite multi-cibles a pour objet le suivi d'un ensemble de cibles mobiles à partir de données obtenues séquentiellement. Ce problème est particulièrement complexe du fait du nombre inconnu et variable de cibles, de la présence de bruit de mesure, de fausses alarmes, d'incertitude de détection et d'incertitude dans l'association de données. Les filtres PHD (Probability Hypothesis Density) constituent une nouvelle gamme de filtres adaptés à cette problématique. Ces techniques se distinguent des méthodes classiques (MHT, JPDAF, particulaire) par la modélisation de l'ensemble des cibles comm
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Brundin, Michelle, Peter Morris, Gustav Åhlman, and Emil Rosén. "Implementation av webbsida för rekommendationssystem med användaruppbyggd databas." Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-175489.

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The goal of this project was to create a web-based, crowd-sourced, correlational database, that easily allowed users to submit objects and receive correlated objects as results. The webservice was created in the web development languages of HTML, CSS, PHP and Javscript, with MySQL to handle the database. Simultaneous development was kept in check with the aid of the source code management system GIT. Upon completion, the service contained several HTML-views, the ability to add and rate objects, a per-object dedicated page with information parsed from Wikipedia.org, and a view with objects rank
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Book chapters on the topic "PHD filtering"

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Chen, Kai, Tongxin Li, Bin Ma, Peng Wang, XiaoFeng Wang, and Peiyuan Zong. "Filtering for Malice Through the Data Ocean: Large-Scale PHA Install Detection at the Communication Service Provider Level." In Research in Attacks, Intrusions, and Defenses. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66332-6_8.

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Torrealba S., Miguel, Mireya Morales P., José M. Campos, and Marina Meza S. "A Software Tool to support Risks Analysis about what Should or Should Not go to the Cloud." In Transportation Systems and Engineering. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8473-7.ch064.

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This chapter proposes a software prototype called 2thecloud, programmed in HTML and PHP under free software guidelines, whose main objective is to allow end users to be aware of imminent cloud dangers. To do this, the user must undergo a metacognitive process of basic risk analysis, which suggested result would be to what cloud the object should go. Through the use of this tool is expected that he / she can develop a risk analysis capability, so that in this way he/she is the one who makes the final decision about selecting between different cloud models (public, private, hybrid, and community) where the object will be placed. An important aspect is that the user has to understand that different cloud models provide different levels of security and this will allow him/her to question safety in other settings, given that it is a work that goes beyond simply encrypting and filtering information. It also presented 2thecloud functions and a description of each step that indicate how it operates. Finally, the authors propose four alternatives in risk analysis calculation, which are plausibly adapted to 2thecloud and if they are implemented they will provide different advantages.
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Torrealba S., Miguel, Mireya Morales P., José M. Campos, and Marina Meza S. "A Software Tool to support Risks Analysis about what Should or Should Not go to the Cloud." In Security Engineering for Cloud Computing. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2125-1.ch005.

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This chapter proposes a software prototype called 2thecloud, programmed in HTML and PHP under free software guidelines, whose main objective is to allow end users to be aware of imminent cloud dangers. To do this, the user must undergo a metacognitive process of basic risk analysis, which suggested result would be to what cloud the object should go. Through the use of this tool is expected that he / she can develop a risk analysis capability, so that in this way he/she is the one who makes the final decision about selecting between different cloud models (public, private, hybrid, and community) where the object will be placed. An important aspect is that the user has to understand that different cloud models provide different levels of security and this will allow him/her to question safety in other settings, given that it is a work that goes beyond simply encrypting and filtering information. It also presented 2thecloud functions and a description of each step that indicate how it operates. Finally, the authors propose four alternatives in risk analysis calculation, which are plausibly adapted to 2thecloud and if they are implemented they will provide different advantages.
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Conference papers on the topic "PHD filtering"

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Petetin, Yohan, and Francois Desbouvries. "Marginalized PHD Filters for multi-target filtering." In 2012 11th International Conference on Information Sciences, Signal Processing and their Applications (ISSPA). IEEE, 2012. http://dx.doi.org/10.1109/isspa.2012.6310587.

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Mahler, Ronald, and Adel El-Fallah. "PHD filtering in known target-dependent clutter." In SPIE Defense, Security, and Sensing, edited by Ivan Kadar. SPIE, 2010. http://dx.doi.org/10.1117/12.849465.

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Delande, Emmanuel, Jérémie Houssineau, and Daniel Clark. "PHD filtering with localised target number variance." In SPIE Defense, Security, and Sensing, edited by Ivan Kadar. SPIE, 2013. http://dx.doi.org/10.1117/12.2015786.

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Maggio, Emilio, Elisa Piccardo, Carlo Regazzoni, and Andrea Cavallaro. "Particle PHD Filtering for Multi-Target Visual Tracking." In 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.366104.

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Addesso, Paolo, Roberto Conte, Maurizio Longo, Rocco Restaino, and Gemine Vivone. "MAP-MRF cloud detection based on PHD filtering." In IGARSS 2011 - 2011 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2011. http://dx.doi.org/10.1109/igarss.2011.6049905.

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Liu, Yang, Qinghua Hu, Yuexian Zou, and Wenwu Wang. "Labelled Non-zero Particle Flow for SMC-PHD Filtering." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8683399.

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Suwannatat, Tassaphan, Krisana Chinnasarn, and Nakorn Indra-Payoong. "Multi-features particle PHD filtering for multiple humans tracking." In 2015 International Computer Science and Engineering Conference (ICSEC). IEEE, 2015. http://dx.doi.org/10.1109/icsec.2015.7401442.

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Pasha, A., B. Vo, H. d. Tuan, and W. k. Ma. "Closed Form PHD Filtering for Linear Jump Markov Models." In 2006 9th International Conference on Information Fusion. IEEE, 2006. http://dx.doi.org/10.1109/icif.2006.301593.

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Zhang, Heng, and Soon Yim Tan. "TOA Based Indoor Localization and Tracking via Single-Cluster PHD Filtering." In 2017 IEEE Global Communications Conference (GLOBECOM 2017). IEEE, 2017. http://dx.doi.org/10.1109/glocom.2017.8254560.

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Song, Yan, Jianwang Hu, and Bing Ji. "A Survey of PHD Filtering Method Based on Random Finite Set." In 2nd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2017). Atlantis Press, 2017. http://dx.doi.org/10.2991/icmeit-17.2017.36.

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