Relevant bibliographies by topics / Probability Hypothesis Density filter

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  1. Journal articles
  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'Probability Hypothesis Density filter'

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Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Probability Hypothesis Density filter"

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Sithiravel, Rajiv, Xin Chen, Ratnasingham Tharmarasa, Bhashyam Balaji, and Thiagalingam Kirubarajan. "The Spline Probability Hypothesis Density Filter." IEEE Transactions on Signal Processing 61, no. 24 (December 2013): 6188–203. http://dx.doi.org/10.1109/tsp.2013.2284139.

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Vo, B. N., and W. K. Ma. "The Gaussian Mixture Probability Hypothesis Density Filter." IEEE Transactions on Signal Processing 54, no. 11 (November 2006): 4091–104. http://dx.doi.org/10.1109/tsp.2006.881190.

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Wu, Wei Hua, Jing Jiang, Chong Yang Liu, and Xiong Hua Fan. "Fast Gaussian Mixture Probability Hypothesis Density Filter." Applied Mechanics and Materials 568-570 (June 2014): 550–56. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.550.

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Although the Gaussian mixture probability hypothesis density (GMPHD) filter is a multi-target tracker that can alleviate the computational intractability of the optimal multi-target Bayes filter and its computational complex is lower than that of sequential Monte Carlo probability hypothesis density (SMCPHD), its computational burden can be reduced further. In the standard GMPHD filter, each observation should be matched with each component when the PHD is updated. In practice, time cost of evaluating many unlikely measurements-to-components parings is wasteful, because their contribution is very limited. As a result, a substantial reduction in complexity could be obtained by directly setting relative value associated with these parings. A fast GMPHD algorithm is proposed in the paper based on gating strategy. Simulation results show that the fast GMPHD can save computational time by 60%~70% without any degradation in performance compared with standard GMPHD.
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Tang, Xu, Xin Chen, Michael McDonald, Ronald Mahler, Ratnasingham Tharmarasa, and Thiagalingam Kirubarajan. "A Multiple-Detection Probability Hypothesis Density Filter." IEEE Transactions on Signal Processing 63, no. 8 (April 2015): 2007–19. http://dx.doi.org/10.1109/tsp.2015.2407322.

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Sithiravel, Rajiv, Michael McDonald, Bhashyam Balaji, and Thiagalingam Kirubarajan. "Multiple model spline probability hypothesis density filter." IEEE Transactions on Aerospace and Electronic Systems 52, no. 3 (June 2016): 1210–26. http://dx.doi.org/10.1109/taes.2016.140750.

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Liu, Jiangyi, Chunping Wang, Wei Wang, and Zheng Li. "Particle Probability Hypothesis Density Filter Based on Pairwise Markov Chains." Algorithms 12, no. 2 (January 31, 2019): 31. http://dx.doi.org/10.3390/a12020031.

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Most multi-target tracking filters assume that one target and its observation follow a Hidden Markov Chain (HMC) model, but the implicit independence assumption of the HMC model is invalid in many practical applications, and a Pairwise Markov Chain (PMC) model is more universally suitable than the traditional HMC model. A set of weighted particles is used to approximate the probability hypothesis density of multi-targets in the framework of the PMC model, and a particle probability hypothesis density filter based on the PMC model (PF-PMC-PHD) is proposed for the nonlinear multi-target tracking system. Simulation results show the effectiveness of the PF-PMC-PHD filter and that the tracking performance of the PF-PMC-PHD filter is superior to the particle PHD filter based on the HMC model in a scenario where we kept the local physical properties of nonlinear and Gaussian HMC models while relaxing their independence assumption.
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Zhang, Feihu, and Alois Knoll. "Vehicle Detection Based on Probability Hypothesis Density Filter." Sensors 16, no. 4 (April 9, 2016): 510. http://dx.doi.org/10.3390/s16040510.

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Yang, Feng, Xi Shi, Keli Liu, Yan Liang, and Hao Chen. "Global track extraction for probability hypothesis density filter." Journal of Systems Engineering and Electronics 27, no. 6 (December 20, 2016): 1151–57. http://dx.doi.org/10.21629/jsee.2016.06.03.

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DU Hang-yuan, 杜航原, 郝燕玲 HAO Yan-ling, 赵玉新 ZHAO Yu-xin, and 杨永鹏 YANG Yong-peng. "Implementation of SLAM by probability hypothesis density filter." Optics and Precision Engineering 19, no. 12 (2011): 3064–73. http://dx.doi.org/10.3788/ope.20111912.3064.

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Whiteley, Nick, Sumeetpal Singh, and Simon Godsill. "Auxiliary Particle Implementation of Probability Hypothesis Density Filter." IEEE Transactions on Aerospace and Electronic Systems 46, no. 3 (July 2010): 1437–54. http://dx.doi.org/10.1109/taes.2010.5545199.

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Dissertations / Theses on the topic "Probability Hypothesis Density filter"

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Clark, Daniel Edward. "Multiple target tracking with the probability hypothesis density filter." Thesis, Heriot-Watt University, 2006. http://hdl.handle.net/10399/161.

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Swain, Anthony Jack. "Group and extended target tracking with the Probability Hypothesis Density filter." Thesis, Heriot-Watt University, 2013. http://hdl.handle.net/10399/2839.

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Multiple target tracking concerns the estimation of an unknown and time-varying number of objects (targets) as they dynamically evolve over time from a sequence of measurements obtained from sensors at discrete time intervals. In the Bayesian filtering framework the estimation problem incorporates natural phenomena such as false measurements and target birth/death. Though theoretically optimal, the generally intractable Bayesian filter requires suitable approximations. This thesis is particularly motivated by a first-order moment approximation known as the Probability Hypothesis Density (PHD) filter. The emphasis in this thesis is on the further development of the PHD filter for handling more advanced target tracking problems, principally involving multiple group and extended targets. A group target is regarded as a collection of targets that share a common motion or characteristic, while an extended target is regarded as a target that potentially generates multiple measurements. The main contributions are the derivations of the PHD filter for multiple group and extended target tracking problems and their subsequent closed-form solutions. The proposed algorithms are applied in simulated scenarios and their estimate results demonstrate that accurate tracking performance is attainable for certain group/extended target tracking problems. The performance is further analysed with the use of suitable metrics.
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Lee, Chee Sing. "Simultaneous localization and mapping using single cluster probability hypothesis density filters." Doctoral thesis, Universitat de Girona, 2015. http://hdl.handle.net/10803/323637.

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The majority of research in feature-based SLAM builds on the legacy of foundational work using the EKF, a single-object estimation technique. Because feature-based SLAM is an inherently multi-object problem, this has led to a number of suboptimalities in popular solutions. We develop an algorithm using the SC-PHD filter, a multi-object estimator modeled on cluster processes. This algorithm hosts capabilities not typically seen with feature-base SLAM solutions such as principled handling of clutter measurements and missed detections, and navigation with a mixture of stationary and moving landmarks. We present experiments with the SC-PHD SLAM algorithm on both synthetic and real datasets using an autonomous underwater vehicle. We compare our method to the RB-PHD SLAM, showing that it requires fewer approximations in its derivation and thus achieves superior performance.
En aquesta tesis es desenvolupa aquest algoritme a partir d’un filtre PHD amb un únic grup (SC-PHD), una tècnica d’estimació multi-objecte basat en processos d’agrupació. Aquest algoritme té unes capacitats que normalment no es veuen en els algoritmes de SLAM basats en característiques, ja que és capaç de tractar falses característiques, així com característiques no detectades pels sensors del vehicle, a més de navegar en un entorn amb la presència de característiques estàtiques i característiques en moviment de forma simultània. Es presenten els resultats experimentals de l’algoritme SC-PHD en entorns reals i simulats utilitzant un vehicle autònom submarí. Els resultats són comparats amb l’algoritme de SLAM Rao-Blackwellized PHD (RB-PHD), demostrant que es requereixen menys aproximacions en la seva derivació i en conseqüència s’obté un rendiment superior.
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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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Li, Tiancheng. "Efficient particle implementation of Bayesian and probability hypothesis density filtering." Thesis, London South Bank University, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.631738.

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Using a set of samples that are associated with weights (namely the particle method) to represent the distribution of interest for filtering under very general hypotheses (often referred to as the sequential Monte Carlo, SMC approaches or particle filters) has gained high attention in the last two decades. However, the particle method suffers from problems such as sample depletion, huge computational time and challenges raised in multi-target tracking (MTT). Aiming to address these problems and challenges, this thesis investigates efficient particle filtering from two perspectives that deal with different number of objects of interest: single-object and multi-object. On one side, novel resampling schemes, fast implementations of particle filters are developed under the Bayesian framework. On the other side, improved particle implementations of the probability hypothesis density (PHD) filter, namely the particle PHD filters are presented to deal with MTT. Resampling is a critical step in the implementation of particle filters that is of practical and theoretical significance. Firstly, various resampling methods and new developments are compared and classified into different categories, providing a comprehensive overview of resampling methods. General discussions about statistical effects of resampling are given with emphasis on robustness and identical distribution testing. New deterministic, adaptive and fast resampling schemes are put forward separately. Further, to increase the computing speed of the particle filter, a fast likelihood computing method based on numerical fitting is proposed, where the likelihood of particles is numerically fitted by the likelihood probability density function (Li-PDF) instead of directly computing it based on measurements. This is the first attempt that applies numerical fitting to enable real-time particle filtering.
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Petetin, Yohan. "Algorithmes de restauration bayésienne mono- et multi-objets dans des modèles markoviens." Phd thesis, Institut National des Télécommunications, 2013. http://tel.archives-ouvertes.fr/tel-00939083.

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Cette thèse est consacrée au problème d'estimation bayésienne pour le filtrage statistique, dont l'objectif est d'estimer récursivement des états inconnus à partir d'un historique d'observations, dans un modèle stochastique donné. Les modèles stochastiques considérés incluent principalement deux grandes classes de modèles : les modèles de Markov cachés et les modèles de Markov à sauts conditionnellement markoviens. Ici, le problème est abordé sous sa forme générale dans la mesure où nous considérons le problème du filtrage mono- et multi objet(s), ce dernier étant abordé sous l'angle de la théorie des ensembles statistiques finis et du filtre " Probability Hypothesis Density ". Tout d'abord, nous nous intéressons à l'importante classe d'approximations que constituent les algorithmes de Monte Carlo séquentiel, qui incluent les algorithmes d'échantillonnage d'importance séquentiel et de filtrage particulaire auxiliaire. Les boucles de propagation mises en jeux dans ces algorithmes sont étudiées et des algorithmes alternatifs sont proposés. Les algorithmes de filtrage particulaire dits " localement optimaux ", c'est à dire les algorithmes d'échantillonnage d'importance avec densité d'importance conditionnelle optimale et de filtrage particulaire auxiliaire pleinement adapté sont comparés statistiquement, en fonction des paramètres du modèle donné. Ensuite, les méthodes de réduction de variance basées sur le théorème de Rao-Blackwell sont exploitées dans le contexte du filtrage mono- et multi-objet(s) Ces méthodes, utilisées principalement en filtrage mono-objet lorsque la dimension du vecteur d'état à estimer est grande, sont dans un premier temps étendues pour les approximations Monte Carlo du filtre Probability Hypothesis Density. D'autre part, des méthodes de réduction de variance alternatives sont proposées : bien que toujours basées sur le théorème de Rao-Blackwell, elles ne se focalisent plus sur le caractère spatial du problème mais plutôt sur son caractère temporel. Enfin, nous abordons l'extension des modèles probabilistes classiquement utilisés. Nous rappelons tout d'abord les modèles de Markov couple et triplet dont l'intérêt est illustré à travers plusieurs exemples pratiques. Ensuite, nous traitons le problème de filtrage multi-objets, dans le contexte des ensembles statistiques finis, pour ces modèles. De plus, les propriétés statistiques plus générales des modèles triplet sont exploitées afin d'obtenir de nouvelles approximations de l'estimateur bayésien optimal (au sens de l'erreur quadratique moyenne) dans les modèles à sauts classiquement utilisés; ces approximations peuvent produire des estimateurs de performances comparables à celles des approximations particulaires, mais ont l'avantage d'être moins coûteuses sur le plan calculatoire
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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 data association. Moreover, the notion of linear jump Markov systems is generalized to the multiple target case to accommodate births, deaths and switching dynamics to derive a closed form solution to the PHD recursion for this so-called linear Gaussian jump Markov multi-target model. The proposed solution is general enough to accommodate a broad class of practical problems which are deemed intractable using traditional techniques. Based on this closed form solution, an efficient method is developed for tracking multiple maneuvering targets that switch between multiple models without the need for gating, track initiation and termination, or clustering for extracting state estimates. The approach to the nonlinear filtering problem explores the framework of the virtual linear fractional transformation (LFT) model which localizes the nonlinearity to the feedback with a simple and sparse structure. The LFT is an exact representation for any differentiable nonlinear mapping and therefore amenable to a general class of problems. An alternative analytical approximation method is presented which avoids linearization of the state space model. The uncorrelated structure of the feedback connection gives of the state space model. The uncorrelated structure of the feedback connection gives better second-order moment approximation of the nonlinearly mapped variables. By arranging the unscented transform in the feedback, the prediction and estimation steps are derived in closed form. The proposed filters for the discrete-time model and continuous-time dynamics with sampled-data measurements respectively are shown to be robust under highly nonlinear and uncertain conditions where standard analytical approximation based filters diverge. Moreover, the LFT based filters are efficient for online implementation. In addition, the LFT framework is applied to extend the closed form solution of the PHD recursion to the nonlinear jump Markov multi-target model.
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Jerrelind, Jakob. "Tracking of Pedestrians Using Multi-Target Tracking Methods with a Group Representation." Thesis, Linköpings universitet, Reglerteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-172579.

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Multi-target tracking (MTT) methods estimate the trajectory of targets from noisy measurement; therefore, they can be used to handle the pedestrian-vehicle interaction for a moving vehicle. MTT has an important part in assisting the Automated Driving System and the Advanced Driving Assistance System to avoid pedestrian-vehicle collisions. ADAS and ADS rely on correct estimates of the pedestrians' position and velocity, to avoid collisions or unnecessary emergency breaking of the vehicle. Therefore, to help the risk evaluation in these systems, the MTT needs to provide accurate and robust information of the trajectories (in terms of position and velocity) of the pedestrians in different environments. Several factors can make this problem difficult to handle for instance in crowded environments the pedestrians can suffer from occlusion or missed detection. Classical MTT methods, such as the global nearest neighbour filter, can in crowded environments fail to provide robust and accurate estimates. Therefore, more sophisticated MTT methods should be used to increase the accuracy and robustness and, in general, to improve the tracking of targets close to each other. The aim of this master's thesis is to improve the situational awareness with respect to pedestrians and pedestrian-vehicle interactions. In particular, the task is to investigate if the GM-PHD and the GM-CPHD filter improve pedestrian tracking in urban environments, compared to other methods presented in the literature.  The proposed task can be divided into three parts that deal with different issues. The first part regards the significance of different clustering methods and how the pedestrians are grouped together. The implemented algorithms are the distance partitioning algorithm and the Gaussian mean shift clustering algorithm. The second part regards how modifications of the measurement noise levels and the survival of targets based on the target location, with respect to the vehicle's position, can improve the tracking performance and remove unwanted estimates. Finally, the last part regards the impact the filter estimates have on the tracking performance and how important accurate detections of the pedestrians are to improve the overall tracking. From the result the distance partitioning algorithm is the favourable algorithm, since it does not split larger groups. It is also seen that the proposed filters provide correct estimates of pedestrians in events of occlusion or missed detections but suffer from false estimates close to the ego vehicle due to uncertain detections. For the comparison, regarding the improvements, a classic standard MTT filter applying the global nearest neighbour method for the data association is used as the baseline. To conclude; the GM-CPHD filter proved to be the best out of the two proposed filters in this thesis work and performed better also compared to other methods known in the literature. In particular, its estimates survived for a longer period of time in presence of missed detection or occlusion. The conclusion of this thesis work is that the GM-CPHD filter improves the tracking performance and the situational awareness of the pedestrians.
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Daniyan, Abdullahi. "Advanced signal processing techniques for multi-target tracking." Thesis, Loughborough University, 2018. https://dspace.lboro.ac.uk/2134/35277.

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The multi-target tracking problem essentially involves the recursive joint estimation of the state of unknown and time-varying number of targets present in a tracking scene, given a series of observations. This problem becomes more challenging because the sequence of observations is noisy and can become corrupted due to miss-detections and false alarms/clutter. Additionally, the detected observations are indistinguishable from clutter. Furthermore, whether the target(s) of interest are point or extended (in terms of spatial extent) poses even more technical challenges. An approach known as random finite sets provides an elegant and rigorous framework for the handling of the multi-target tracking problem. With a random finite sets formulation, both the multi-target states and multi-target observations are modelled as finite set valued random variables, that is, random variables which are random in both the number of elements and the values of the elements themselves. Furthermore, compared to other approaches, the random finite sets approach possesses a desirable characteristic of being free of explicit data association prior to tracking. In addition, a framework is available for dealing with random finite sets and is known as finite sets statistics. In this thesis, advanced signal processing techniques are employed to provide enhancements to and develop new random finite sets based multi-target tracking algorithms for the tracking of both point and extended targets with the aim to improve tracking performance in cluttered environments. To this end, firstly, a new and efficient Kalman-gain aided sequential Monte Carlo probability hypothesis density (KG-SMC-PHD) filter and a cardinalised particle probability hypothesis density (KG-SMC-CPHD) filter are proposed. These filters employ the Kalman- gain approach during weight update to correct predicted particle states by minimising the mean square error between the estimated measurement and the actual measurement received at a given time in order to arrive at a more accurate posterior. This technique identifies and selects those particles belonging to a particular target from a given PHD for state correction during weight computation. The proposed SMC-CPHD filter provides a better estimate of the number of targets. Besides the improved tracking accuracy, fewer particles are required in the proposed approach. Simulation results confirm the improved tracking performance when evaluated with different measures. Secondly, the KG-SMC-(C)PHD filters are particle filter (PF) based and as with PFs, they require a process known as resampling to avoid the problem of degeneracy. This thesis proposes a new resampling scheme to address a problem with the systematic resampling method which causes a high tendency of resampling very low weight particles especially when a large number of resampled particles are required; which in turn affect state estimation. Thirdly, the KG-SMC-(C)PHD filters proposed in this thesis perform filtering and not tracking , that is, they provide only point estimates of target states but do not provide connected estimates of target trajectories from one time step to the next. A new post processing step using game theory as a solution to this filtering - tracking problem is proposed. This approach was named the GTDA method. This method was employed in the KG-SMC-(C)PHD filter as a post processing technique and was evaluated using both simulated and real data obtained using the NI-USRP software defined radio platform in a passive bi-static radar system. Lastly, a new technique for the joint tracking and labelling of multiple extended targets is proposed. To achieve multiple extended target tracking using this technique, models for the target measurement rate, kinematic component and target extension are defined and jointly propagated in time under the generalised labelled multi-Bernoulli (GLMB) filter framework. The GLMB filter is a random finite sets-based filter. In particular, a Poisson mixture variational Bayesian (PMVB) model is developed to simultaneously estimate the measurement rate of multiple extended targets and extended target extension was modelled using B-splines. The proposed method was evaluated with various performance metrics in order to demonstrate its effectiveness in tracking multiple extended targets.
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Vestin, Albin, and Gustav Strandberg. "Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms." Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.

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Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms.
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Book chapters on the topic "Probability Hypothesis Density filter"

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Wang, Ding, Xu Tang, and Qun Wan. "The Recursive Spectral Bisection Probability Hypothesis Density Filter." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 47–56. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36402-1_5.

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Zhang, Pu, Hongwei Li, and Yuan Huang. "Quadrature Kalman Probability Hypothesis Density Filter for Multi-Target Tracking." In Informatics in Control, Automation and Robotics, 757–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25899-2_102.

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Wu, Tianjun, and Jianghong Ma. "Unscented Particle Implementation of Probability Hypothesis Density Filter for Multisensor Multitarget Tracking." In Recent Advances in Computer Science and Information Engineering, 321–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25792-6_48.

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Atitey, Komlan, and Yan Cang. "A Novel Prediction Algorithm in Gaussian-Mixture Probability Hypothesis Density Filter for Target Tracking." In Lecture Notes in Computer Science, 373–93. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21978-3_33.

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Zhu, Jihong, Benlian Xu, Fei Wang, and Qiquan Wang. "A New Method Based on Ant Colony Optimization for the Probability Hypothesis Density Filter." In Lecture Notes in Computer Science, 537–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21524-7_66.

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Rezatofighi, Seyed Hamid, Stephen Gould, Ba-Ngu Vo, Katarina Mele, William E. Hughes, and Richard Hartley. "A Multiple Model Probability Hypothesis Density Tracker for Time-Lapse Cell Microscopy Sequences." In Lecture Notes in Computer Science, 110–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38868-2_10.

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Chen, Jiandan, Iyeyinka Damilola Olayanju, Olabode Paul Ojelabi, and Wlodek Kulesza. "RFID Multi-target Tracking Using the Probability Hypothesis Density Algorithm for a Health Care Application." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 95–105. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32304-1_9.

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Huang, Fangming, Kun Wang, Jian Xu, and Zhiliang Huang. "The Analytic Implementation of the Multisensor Probability Hypothesis Density Filter." In Multisensor Data Fusion, 235–52. CRC Press, 2017. http://dx.doi.org/10.1201/b18851-15.

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Waqar, Amina. "Mobile/Wireless Robot Navigation." In Mobile Ad Hoc Robots and Wireless Robotic Systems, 80–88. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2658-4.ch004.

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Sensor-based localization has been found to be one of the most preliminary issues in the world of Mobile/Wireless Robotics. One can easily track a mobile robot using a Kalman Filter, which uses a Phase Locked Loop for tracing via averaging the values. Tracking has now become very easy, but one wants to proceed to navigation. The reason behind this is that tracking does not help one determine where one is going. One would like to use a more precise “Navigation” like Monte Carlo Localization. It is a more efficient and precise way than a feedback loop because the feedback loops are more sensitive to noise, making one modify the external loop filter according to the variation in the processing. In this case, the robot updates its belief in the form of a probability density function (pdf). The supposition is considered to be one meter square. This probability density function expands over the entire supposition. A door in a wall can be identified as peak/rise in the probability function or the belief of the robot. The mobile updates a window of 1 meter square (area depends on the sensors) as its belief. One starts with a uniform probability density function, and then the sensors update it. The authors use Monte Carlo Localization for updating the belief, which is an efficient method and requires less space. It is an efficient method because it can be applied to continuous data input, unlike the feedback loop. It requires less space. The robot does not need to store the map and, hence, can delete the previous belief without any hesitation.
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Gu, Irene Yu-Hua, and Vasile Gui. "Joint Space-Time-Range Mean ShiftBased Image and Video Segmentation." In Advances in Image and Video Segmentation, 113–39. IGI Global, 2006. http://dx.doi.org/10.4018/978-1-59140-753-9.ch006.

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This chapter addresses image and video segmentation by using mean shift-based filtering and segmentation. Mean shift is an effective and elegant method to directly seek the local modes (or, local maxima) of the probability density function without the requirement of actually estimating it. Mean shift is proportional to the normalized density gradient estimate, and is pointing to the local stationary point (or, local mode) of the density estimate at which it converges. A mean shift filter can be related to a domain filter, a range filter or a bilateral filter depending on the variable setting in the kernel, and also has its own strength due to its flexibility and statistical basis. In this chapter a variety of mean shift filtering approaches are described for image/video segmentation and nonlinear edge-preserving image smoothing. A joint space-time-range domain mean shift-based video segmentation approach is presented. Segmentation of moving/static objects/background is obtained through inter-frame mode-matching in consecutive frames and motion vector mode estimation. Newly appearing objects/regions in the current frame due to new foreground objects or uncovered background regions are segmented by intra-frame mode estimation. Examples of image/video segmentation are included to demonstrate the effectiveness and robustness of these methods. Pseudo codes of the algorithms are also included.
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Conference papers on the topic "Probability Hypothesis Density filter"

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Garcia-Fernandez, Angel F., and Lennart Svensson. "Trajectory probability hypothesis density filter." In 2018 21st International Conference on Information Fusion (FUSION 2018). IEEE, 2018. http://dx.doi.org/10.23919/icif.2018.8455270.

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Panta, Kusha, Ba-Ngu Vo, Sumeetpal Singh, and Arnaud Doucet. "Probability hypothesis density filter versus multiple hypothesis tracking." In Defense and Security, edited by Ivan Kadar. SPIE, 2004. http://dx.doi.org/10.1117/12.543357.

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Sithiravel, Rajiv, Ratnasingham Tharmarasa, Mike McDonald, Michel Pelletier, and Thiagalingam Kirubarajan. "The spline probability hypothesis density filter." In SPIE Defense, Security, and Sensing. SPIE, 2012. http://dx.doi.org/10.1117/12.921022.

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Wu, M., D. Zheng, J. Yuan, S. Zhang, A. Chen, and B. Cheng. "Probability hypothesis density filter with low detection probability." In IET International Radar Conference (IET IRC 2020). Institution of Engineering and Technology, 2021. http://dx.doi.org/10.1049/icp.2021.0664.

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Georgescu, Ramona, and Peter Willett. "Multiple model cardinalized probability hypothesis density filter." In SPIE Optical Engineering + Applications, edited by Oliver E. Drummond. SPIE, 2011. http://dx.doi.org/10.1117/12.890953.

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Georgescu, Ramona, and Peter Willett. "Classification aided cardinalized probability hypothesis density filter." In SPIE Defense, Security, and Sensing. SPIE, 2012. http://dx.doi.org/10.1117/12.917729.

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Wang, Yang, Zhongliang Jing, and Shiqiang Hu. "Data Association for Cardinalized Probability Hypothesis Density Filter." In 2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC). IEEE, 2009. http://dx.doi.org/10.1109/icicic.2009.153.

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Erdinc, O., P. Willett, and Y. Bar-Shalom. "Probability hypothesis density filter for multitarget multisensor tracking." In 2005 7th International Conference on Information Fusion. IEEE, 2005. http://dx.doi.org/10.1109/icif.2005.1591848.

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Jian Jun Yin, Jian Qiu Zhang, and Ze Sen Zhuang. "The Gaussian sum convolution probability hypothesis density filter." In 2008 9th International Conference on Signal Processing (ICSP 2008). IEEE, 2008. http://dx.doi.org/10.1109/icosp.2008.4697125.

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Wang, Mingjie, Hongbing Ji, Xiaolong Hu, and Yongquan Zhang. "Gaussian mixture particle flow probability hypothesis density filter." In 2017 20th International Conference on Information Fusion (Fusion). IEEE, 2017. http://dx.doi.org/10.23919/icif.2017.8009679.

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