Relevant bibliographies by topics / Filtre PHD

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Filtre PHD'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Filtre PHD"

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Durão Ferreira, Lúcia De Fátima, and Paula Moreira Baltar Bellemain. "Aire et de périmètre dans les manuels scolaires brésiliens à la transition entre l’école élémentaire et le collègeArea and perimeter in Brazilian textbooks at the transition from elementary school to college." Educação Matemática Pesquisa : Revista do Programa de Estudos Pós-Graduados em Educação Matemática 22, no. 4 (2020): 332–42. http://dx.doi.org/10.23925/1983-3156.2020v22i4p332-342.

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RésuméCe travail, qui s’inscrit dans une recherche doctorale sur à la transition de la 5ème année (élèves de 10-11 ans) à la 6ème année (élèves 11-12 ans) de l’enseignement obligatoire brésilien, concerne l’analyse de manuels scolaires de 5ème et 6ème, à l'aide du filtre des grandeurs, de la notion de reprise et des niveaux de codétermination. La prise en charge des reprises proposée dans les manuels scolaires analysés ne paraît pas a priori suffisante pour assurer les conditions favorables à la transition entre les sous-niveaux et l’apprentissage des nouveaux objets étudiés en 6ème.Mots clés: Transition, Enseignement Primaire, Région; Périmètre, ManuelAbstractThis work, which is part of a PhD research about the transition period between Grade 6 (students of age 10-11) and Grade 7 (students of age 11-12) in the Brazilian educational system, concerns of an analysis of Grade 5 and Grade 6 textbooks, with the help from magnitude's filters, the idea of reprise and the levels of co-determination. The occasions responsible for reprise contained in the textbooks analyzed do not seem enough to assure the favorable conditions for the transition between the sub-levels and the learning of new objects presented in Grade 7.Keywords: Transition, Elementary education, Area; Perimeter, Textbook.
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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 observation model, small observation noise and non-Gaussian noise of the proposed filter over the existing Gaussian mixture particle PHD (GMPPHD) filter. The multi-target tracking simulation results verify the effectiveness of the proposed method.
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Cong-An, Xu, Xu Congqi, Dong Yunlong, Xiong Wei, Chai Yong, and Li Tianmei. "A Novel Sequential Monte Carlo-Probability Hypothesis Density Filter for Particle Impoverishment Problem." Journal of Computational and Theoretical Nanoscience 13, no. 10 (2016): 6872–77. http://dx.doi.org/10.1166/jctn.2016.5640.

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As a typical implementation of the probability hypothesis density (PHD) filter, sequential Monte Carlo PHD (SMC-PHD) is widely employed in highly nonlinear systems. However, diversity loss of particles introduced by the resampling step, which can be called particle impoverishment problem, may lead to performance degradation and restrain the use of SMC-PHD filter in practical applications. In this paper, a novel SMC-PHD filter based on particle compensation is proposed to solve the problem. Firstly, based on an analysis of the particle impoverishment problem, a new particle compensatory method is developed to improve the particle diversity. Then, all the particles are integrated into the SMC-PHD filter framework. Compared with the SMC-PHD filter, simulation results demonstrate that the proposed particle compensatory SMC-PHD filter is capable of overcoming the particle impoverishment problem, which indicate good application prospects.
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Gao, Yiyue, Defu Jiang, and Ming Liu. "Particle-gating SMC-PHD filter." Signal Processing 130 (January 2017): 64–73. http://dx.doi.org/10.1016/j.sigpro.2016.06.017.

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Markovic, Ivan, Josip Cesic, and Ivan Petrovic. "Von Mises Mixture PHD Filter." IEEE Signal Processing Letters 22, no. 12 (2015): 2229–33. http://dx.doi.org/10.1109/lsp.2015.2472962.

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Tian, Shu Rong, Xiao Shu Sun, and Xi Jing Sun. "Multi-Sensor Interactive Multi-Model PHD Filter for Maneuvering Multi-Target Tracking." Applied Mechanics and Materials 336-338 (July 2013): 200–203. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.200.

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In maneuvering multiple targets tracking problem, Probability Hypothesis Density(PHD) filter can be used to estimate the multi-target state and the number at each time step, but single model method may not provide accurate estimates. In this paper, an interactive multiple model PHD filter is proposed, and then multiple sensor interactive multiple model PHD filter is proposed to improve the tracking of multiple maneuvering targets. PHD particle filter implementation is used to perform the proposed method consisting of multiple maneuvering targets.
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Zhang, Huanqing, Hongwei Ge, and Jinlong Yang. "Improved Gaussian Mixture Probability Hypothesis Density for Tracking Closely Spaced Targets." International Journal of Electronics and Telecommunications 63, no. 3 (2017): 247–54. http://dx.doi.org/10.1515/eletel-2017-0033.

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AbstractProbability hypothesis density (PHD) filter is a suboptimal Bayesian multi-target filter based on random finite set. The Gaussian mixture PHD filter is an analytic solution to the PHD filter for linear Gaussian multi-target models. However, when targets move near each other, the GM-PHD filter cannot correctly estimate the number of targets and their states. To solve the problem, a novel reweighting scheme for closely spaced targets is proposed under the framework of the GM-PHD filter, which can be able to correctly redistribute the weights of closely spaced targets, and effectively improve the multiple target state estimation precision. Simulation results demonstrate that the proposed algorithm can accurately estimate the number of targets and their states, and effectively improve the performance of multi-target tracking algorithm.
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Fuse, T., D. Hiramatsu, and W. Nakanishi. "MULTI-TARGET DETECTION FROM FULL-WAVEFORM AIRBORNE LASER SCANNER USING PHD FILTER." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 16, 2016): 647–52. http://dx.doi.org/10.5194/isprsarchives-xli-b5-647-2016.

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We propose a new technique to detect multiple targets from full-waveform airborne laser scanner. We introduce probability hypothesis density (PHD) filter, a type of Bayesian filtering, by which we can estimate the number of targets and their positions simultaneously. PHD filter overcomes some limitations of conventional Gaussian decomposition method; PHD filter doesn’t require a priori knowledge on the number of targets, assumption of parametric form of the intensity distribution. In addition, it can take a similarity between successive irradiations into account by modelling relative positions of the same targets spatially. Firstly we explain PHD filter and particle filter implementation to it. Secondly we formulate the multi-target detection problem on PHD filter by modelling components and parameters within it. At last we conducted the experiment on real data of forest and vegetation, and confirmed its ability and accuracy.
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Fuse, T., D. Hiramatsu, and W. Nakanishi. "MULTI-TARGET DETECTION FROM FULL-WAVEFORM AIRBORNE LASER SCANNER USING PHD FILTER." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 16, 2016): 647–52. http://dx.doi.org/10.5194/isprs-archives-xli-b5-647-2016.

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We propose a new technique to detect multiple targets from full-waveform airborne laser scanner. We introduce probability hypothesis density (PHD) filter, a type of Bayesian filtering, by which we can estimate the number of targets and their positions simultaneously. PHD filter overcomes some limitations of conventional Gaussian decomposition method; PHD filter doesn’t require a priori knowledge on the number of targets, assumption of parametric form of the intensity distribution. In addition, it can take a similarity between successive irradiations into account by modelling relative positions of the same targets spatially. Firstly we explain PHD filter and particle filter implementation to it. Secondly we formulate the multi-target detection problem on PHD filter by modelling components and parameters within it. At last we conducted the experiment on real data of forest and vegetation, and confirmed its ability and accuracy.
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Gong, Yang, and Chen Cui. "A Robust SMC-PHD Filter for Multi-Target Tracking with Unknown Heavy-Tailed Measurement Noise." Sensors 21, no. 11 (2021): 3611. http://dx.doi.org/10.3390/s21113611.

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In multi-target tracking, the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter is a practical algorithm. Influenced by outliers under unknown heavy-tailed measurement noise, the SMC-PHD filter suffers severe performance degradation. In this paper, a robust SMC-PHD (RSMC-PHD) filter is proposed. In the proposed filter, Student-t distribution is introduced to describe the unknown heavy-tailed measurement noise where the degrees of freedom (DOF) and the scale matrix of the Student-t distribution are respectively modeled as a Gamma distribution and an inverse Wishart distribution. Furthermore, the variational Bayesian (VB) technique is employed to infer the unknown DOF and scale matrix parameters while the recursion estimation framework of the RSMC-PHD filter is derived. In addition, considering that the introduced Student- t distribution might lead to an overestimation of the target number, a strategy is applied to modify the updated weight of each particle. Simulation results demonstrate that the proposed filter is effective with unknown heavy-tailed measurement noise.
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Dissertations / Theses on the topic "Filtre PHD"

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Gomes, borges Marcos Eduardo. "Détermination et implémentation temps-réel de stratégies de gestion de capteurs pour le pistage multi-cibles." Thesis, Ecole centrale de Lille, 2018. http://www.theses.fr/2018ECLI0019/document.

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Les systèmes de surveillance modernes doivent coordonner leurs stratégies d’observation pour améliorer l’information obtenue lors de leurs futures mesures afin d’estimer avec précision les états des objets d’intérêt (emplacement, vitesse, apparence, etc.). Par conséquent, la gestion adaptative des capteurs consiste à déterminer les stratégies de mesure des capteurs exploitant les informations a priori afin de déterminer les actions de détection actuelles. L’une des applications la plus connue de la gestion des capteurs est le suivi multi-objet, qui fait référence au problème de l’estimation conjointe du nombre d’objets et de leurs états ou trajectoires à partir de mesures bruyantes. Cette thèse porte sur les stratégies de gestion des capteurs en temps réel afin de résoudre le problème du suivi multi-objet dans le cadre de l’approche RFS labélisée. La première contribution est la formulation théorique rigoureuse du filtre mono-capteur LPHD avec son implémentation Gaussienne. La seconde contribution est l’extension du filtre LPHD pour le cas multi-capteurs. La troisième contribution est le développement de la méthode de gestion de capteurs basée sur la minimisation du risque Bayes et formulée dans les cadres POMDP et LRFS. En outre, des analyses et des simulations des approches de gestion de capteurs existantes pour le suivi multi-objets sont fournies<br>Modern surveillance systems must coordinate their observation strategies to enhance the information obtained by their future measurements in order to accurately estimate the states of objects of interest (location, velocity, appearance, etc). Therefore, adaptive sensor management consists of determining sensor measurement strategies that exploit a priori information in order to determine current sensing actions. One of the most challenging applications of sensor management is the multi-object tracking, which refers to the problem of jointly estimating the number of objects and their states or trajectories from noisy sensor measurements. This thesis focuses on real-time sensor management strategies formulated in the POMDP framework to address the multi-object tracking problem within the LRFS approach. The first key contribution is the rigorous theoretical formulation of the mono-sensor LPHD filter with its Gaussian-mixture implementation. The second contribution is the extension of the mono-sensor LPHD filter for superpositional sensors, resulting in the theoretical formulation of the multi-sensor LPHD filter. The third contribution is the development of the Expected Risk Reduction (ERR) sensor management method based on the minimization of the Bayes risk and formulated in the POMDP and LRFS framework. Additionally, analyses and simulations of the existing sensor management approaches for multi-object tracking, such as Task-based, Information-theoretic, and Risk-based sensor management, are provided
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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 comme un ensemble fini aléatoire et par l'utilisation des moments de sa densité de probabilité. Dans la première partie, on s'intéresse principalement à la problématique de l'application des filtres PHD pour le filtrage multi-cibles maritime et aérien dans des scénarios réalistes et à l'étude des propriétés numériques de ces algorithmes. Dans la seconde partie, nous nous intéressons à l'étude théorique des processus de branchement liés aux équations du filtrage multi-cibles avec l'analyse des propriétés de stabilité et le comportement en temps long des semi-groupes d'intensités de branchements spatiaux. Ensuite, nous analysons les propriétés de stabilité exponentielle d'une classe d'équations à valeurs mesures que l'on rencontre dans le filtrage non-linéaire multi-cibles. Cette analyse s'applique notamment aux méthodes de type Monte Carlo séquentielles et aux algorithmes particulaires dans le cadre des filtres de Bernoulli et des filtres PHD.
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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 multicapteur peut être propagée dans des scénarios de surveillance simples. La deuxième partie aborde le problème de gestion des capteurs dans le cadre du PHD. A chaque itération, le BET (Balanced Explorer and Tracker) construit une prédiction du PHD multicapteur a posteriori grâce au PIMS (Predicted Ideal Measurement Set) et définit un contrôle multicapteur en respectant quelques critères opérationnels simples adaptés aux missions de surveillance.
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Meißner, Daniel Alexander [Verfasser]. "Intersection-based road user tracking using a classifying multiple-model PHD filter / Daniel Alexander Meißner." Ulm : Universität Ulm. Fakultät für Ingenieurwissenschaften und Informatik, 2016. http://d-nb.info/1082294187/34.

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Thompson, Thaddeus. "Rheological Study of Linear and Nonlinear Viscoelastic Behavior for Silica-Reinforced Polybutadiene and Polystyrene." University of Akron / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=akron1134566032.

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Káčeríková, Martina. "Vývoj organických UV filtrů na bázi přírodních extraktů." Master's thesis, Vysoké učení technické v Brně. Fakulta chemická, 2020. http://www.nusl.cz/ntk/nusl-414167.

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This diploma thesis is focused on the development of organic UV filters. Organic UV filters were extracted from natural resources and encapsulated into nanomaterial-like delivery systems such as liposomes and nanofibres. SPF of particular extracts and carriers with encapsulated extracts were measured. All of the prepared extracts as well as carriers were characterised for their content of natural substances like phenolic compounds and their antioxidant acitvity, stability, cytotoxicity, micriobial acitivity and their safety were studied too. All of the prepared materials were evaluated as suitable for use in comestic industry. However, in a future, it would be appropriate to add to the study other experimental methods to increase the active substances and at the same time increase the SPF protection factor.
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Edman, Viktor. "Tracking Groups of People in Video Surveillance." Thesis, Linköpings universitet, Reglerteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-93996.

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In this master thesis, the problem of tracking groups using an image sequence dataset is examined. Target tracking can be defined as the problem of estimating a target's state given prior knowledge about its motion and some sensor measurements related to the target's state. A popular method for target tracking is e.g. the Kalman filter. However, the Kalman filter is insufficient when there are multiple targets in the scene. Consequently, alternative multitarget tracking methods must be applied along with methods for estimating the number of targets in the scene. Multitarget tracking can however be difficult when there are many unresolved targets, e.g. associating observations with targets in dense crowds. A viable simplification is group target tracking, keeping track of groups rather than individual targets. Furthermore, group target tracking is preferred when the user wants to know the motion and extension of a group in e.g. evacuation scenarios. To solve the problem of group target tracking in video surveillance, a combination of GM-PHD filtering and mean shift clustering is proposed. The GM-PHD filter is an approximation of Bayes multitarget filter. Pedestrian detections converted into flat world coordinates from the image dataset are used as input to the filter. The output of the GM-PHD filter consists of Gaussian mixture components with corresponding mean state vectors. The components are divided into groups by using mean shift clustering. An estimate of the number of members and group shape is presented for each group. The method is evaluated using both single camera measurements and two cameras partly surveilling the same area. The results are promising and present a nice visual representation of the groups' characteristics. However, using two cameras gives no improvement in performance, probably due to differences in detections between the two cameras, e.g. a single pedestrian can be observed being at two positions several meters apart making it difficult to determine if it is a single pedestrian or multiple pedestrians.
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Lundquist, Christian. "Sensor Fusion for Automotive Applications." Doctoral thesis, Linköpings universitet, Reglerteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-71594.

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Mapping stationary objects and tracking moving targets are essential for many autonomous functions in vehicles. In order to compute the map and track estimates, sensor measurements from radar, laser and camera are used together with the standard proprioceptive sensors present in a car. By fusing information from different types of sensors, the accuracy and robustness of the estimates can be increased. Different types of maps are discussed and compared in the thesis. In particular, road maps make use of the fact that roads are highly structured, which allows relatively simple and powerful models to be employed. It is shown how the information of the lane markings, obtained by a front looking camera, can be fused with inertial measurement of the vehicle motion and radar measurements of vehicles ahead to compute a more accurate and robust road geometry estimate. Further, it is shown how radar measurements of stationary targets can be used to estimate the road edges, modeled as polynomials and tracked as extended targets. Recent advances in the field of multiple target tracking lead to the use of finite set statistics (FISST) in a set theoretic approach, where the targets and the measurements are treated as random finite sets (RFS). The first order moment of a RFS is called probability hypothesis density (PHD), and it is propagated in time with a PHD filter. In this thesis, the PHD filter is applied to radar data for constructing a parsimonious representation of the map of the stationary objects around the vehicle. Two original contributions, which exploit the inherent structure in the map, are proposed. A data clustering algorithm is suggested to structure the description of the prior and considerably improving the update in the PHD filter. Improvements in the merging step further simplify the map representation. When it comes to tracking moving targets, the focus of this thesis is on extended targets, i.e., targets which potentially may give rise to more than one measurement per time step. An implementation of the PHD filter, which was proposed to handle data obtained from extended targets, is presented. An approximation is proposed in order to limit the number of hypotheses. Further, a framework to track the size and shape of a target is introduced. The method is based on measurement generating points on the surface of the target, which are modeled by an RFS. Finally, an efficient and novel Bayesian method is proposed for approximating the tire radii of a vehicle based on particle filters and the marginalization concept. This is done under the assumption that a change in the tire radius is caused by a change in tire pressure, thus obtaining an indirect tire pressure monitoring system. The approaches presented in this thesis have all been evaluated on real data from both freeways and rural roads in Sweden.<br>SEFS -- IVSS<br>VR - ETT
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Magraoui, Mohamed. "Validation de techniques de commande d'un filtre actif parallèle /." Thèse, Montréal : École de technologie supérieure, 2007. http://proquest.umi.com/pqdweb?did=1459914731&sid=1&Fmt=2&clientId=46962&RQT=309&VName=PQD.

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Thèse (M. Ing.)--École de technologie supérieure, Montréal, 2007.<br>"Mémoire présenté à l'École de technologie supérieure comme exigence partielle à l'obtention de la maîtrise en génie électrique." CaQMUQET Bibliogr. : f. [150]-155. Également disponible en version électronique. CaQMUQET
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Zhang, Xiuyin. "Novel RF resonators and bandpass filters for wireless communications : theory, design and application /." access full-text access abstract and table of contents, 2009. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?phd-ee-b23750832f.pdf.

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Thesis (Ph.D.)--City University of Hong Kong, 2009.<br>"Submitted to the Department of Electronic Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy." Includes bibliographical references (leaves 141-158)
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Books on the topic "Filtre PHD"

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PHP-Nuke garage. Prentice Hall PTR, 2005.

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Adobe Dreamweaver CS5 with PHP. AdobePress, 2010.

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PHP, MYSQL, and Apache. 3rd ed. Sams, 2007.

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Gilmore, William. PHP and MySQL web development. Sams, 2000.

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Welling, Luke. PHP and MySQL Web development. Sams, 2001.

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Integrating PHP with Windows. Published with the authorization of Microsoft Corp. by O'Reilly Media, 2011.

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Powers, David. Foundation PHP for Dreamweaver 8. Friends of Ed, 2006.

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Valade, Janet. PHP & MySQL Everyday Apps For Dummies. John Wiley & Sons, Ltd., 2005.

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Defrance, Jean-Marie. PHP/MySQL avec Dreamweaver 8. Eyrolles, 2006.

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Laura, Thomson, ed. PHP and MySQL Web development. 3rd ed. Sams, 2004.

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Book chapters on the topic "Filtre PHD"

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Zhao, Lingling, Xiaohong Su, and Peijun Ma. "Multitarget PHD Particle Filter Tracker Based on Single-Target PHD." In 2011 International Conference in Electrics, Communication and Automatic Control Proceedings. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8849-2_221.

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Li, Lei, Jinqiu Sun, Yu Zhu, and Haisen Li. "Dim Target Tracking Base on GM-PHD Filter." In Intelligent Science and Intelligent Data Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31919-8_37.

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Fang, Meng, Wenguang Wang, Dong Cao, and Yan Zuo. "An Improved PHD Filter Based on Dynamic Programming." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0896-3_28.

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Gal, Oren, and Eran Zeitouni. "Tracking Objects Using PHD Filter for USV Autonomous Capabilities." In Robotic Sailing 2012. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33084-1_1.

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Ma, Weizhang, Bo Ma, and Xueliang Zhan. "Kalman Particle PHD Filter for Multi-target Visual Tracking." In Intelligent Science and Intelligent Data Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31919-8_44.

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Leung, Yee, Tianjun Wu, and Jianghong Ma. "A PHD-Filter-Based Multitarget Tracking Algorithm for Sensor Networks." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39649-6_7.

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Liu, Yang, Wenwu Wang, Jonathon Chambers, Volkan Kilic, and Adrian Hilton. "Particle Flow SMC-PHD Filter for Audio-Visual Multi-speaker Tracking." In Latent Variable Analysis and Signal Separation. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53547-0_33.

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Liu, Weifeng, and Chenglin Wen. "A Linear Multisensor PHD Filter Using the Measurement Dimension Extension Approach." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21524-7_60.

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Ni, Sheng, Rentong Guo, Xiaofei Liao, and Hai Jin. "Parallel Bloom Filter on Xeon Phi Many-Core Processors." In Algorithms and Architectures for Parallel Processing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27122-4_27.

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Jing, Zhongliang, Han Pan, Yuankai Li, and Peng Dong. "Bearing-Only Multiple Target Tracking with the Sequential PHD Filter for Multi-Sensor Fusion." In Information Fusion and Data Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90716-1_6.

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Conference papers on the topic "Filtre PHD"

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Del Coco, Marco, and Andrea Cavallaro. "Parallel particle-PHD filter." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6854872.

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Ishibashi, Masanori, Yumi Iwashita, and Ryo Kurazume. "Noise-estimate Particle PHD filter." In 2014 World Automation Congress (WAC). IEEE, 2014. http://dx.doi.org/10.1109/wac.2014.6936154.

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Gao, Yiyue, Defu Jiang, Ming Liu, and Wei Fu. "Likelihood-gating SMC-PHD filter." In 2017 IEEE 17th International Conference on Communication Technology (ICCT). IEEE, 2017. http://dx.doi.org/10.1109/icct.2017.8359916.

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Junjie Wang, Lingling Zhao, Xiaohong Su, Rui Sun, and Jiquan Ma. "Cluster-based efficient particle PHD filter." In 2015 International Conference on Control, Automation and Information Sciences (ICCAIS). IEEE, 2015. http://dx.doi.org/10.1109/iccais.2015.7338665.

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Clark, D. E., and J. Bell. "Data Association for the PHD Filter." In 2005 International Conference on Intelligent Sensors, Sensor Networks and Information Processing. IEEE, 2005. http://dx.doi.org/10.1109/issnip.2005.1595582.

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Abdella, Hashim Kemal, David M. Lane, and Francesco Maurelli. "Sonar based mapping using PHD filter." In OCEANS 2014. IEEE, 2014. http://dx.doi.org/10.1109/oceans.2014.7003083.

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Houssineau, Jeremie, and Dann Laneuville. "PHD filter with diffuse spatial prior on the birth process with applications to GM-PHD filter." In 2010 13th International Conference on Information Fusion (FUSION 2010). IEEE, 2010. http://dx.doi.org/10.1109/icif.2010.5711951.

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Maher, Ronald. "A survey of PHD filter and CPHD filter implementations." In Defense and Security Symposium, edited by Ivan Kadar. SPIE, 2007. http://dx.doi.org/10.1117/12.721125.

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Shen, Xinglin, Zhiyong Song, Hongqi Fan, and Qiang Fu. "PHD filter for single extended target tracking." In 2016 CIE International Conference on Radar (RADAR). IEEE, 2016. http://dx.doi.org/10.1109/radar.2016.8059266.

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Ristic, B., D. Clark, and Ba-Ngu Vo. "Improved SMC implementation of the PHD filter." In 2010 13th International Conference on Information Fusion (FUSION 2010). IEEE, 2010. http://dx.doi.org/10.1109/icif.2010.5711922.

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Reports on the topic "Filtre PHD"

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Chambers, J. A., J. E. Garnier, and T. J. McMahon. Development and Testing of PRD-66 Hot Gas Filters. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/419377.

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