Relevant bibliographies by topics / PHD filter

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Academic literature on the topic 'PHD filter'

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

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Journal articles on the topic "PHD filter"

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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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Xu, Weijun. "Adaptive probability hypothesis density filter for multi-target tracking with unknown measurement noise statistics." Measurement and Control 54, no. 3-4 (2021): 279–91. http://dx.doi.org/10.1177/0020294021992800.

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Under the Gaussian noise assumption, the probability hypothesis density (PHD) filter represents a promising tool for tracking a group of moving targets with a time-varying number. However, inaccurate prior statistics of the random noise will degrade the performance of the PHD filter in many practical applications. This paper presents an adaptive Gaussian mixture PHD (AGM-PHD) filter for the multi-target tracking (MTT) problem in the scenario where both the mean and covariance of measurement noise sequences are unknown. The conventional PHD filters are extended to jointly estimate both the multi-target state and the aforementioned measurement noise statistics. In particular, the Normal-inverse-Wishart and Gaussian distributions are first integrated to represent the joint posterior intensity by transforming the measurement model into a new formulation. Then, the updating rule for the hyperparameters of the model is derived in closed form based on variational Bayesian (VB) approximation and Bayesian conjugate prior heuristics. Finally, the dynamic system state and the noise statistics are updated sequentially in an iterative manner. Simulations results with both constant velocity and constant turn model demonstrate that the AGM-PHD filter achieves comparable performance as the ideal PHD filter with true measurement noise statistics.
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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 (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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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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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, while the multi-Bernoulli filter estimates the multitarget posterior density through propagating the parameters of a multi-Bernoulli RFS that approximates the posterior density of multitarget RFS. Numerous performance comparisons between the two RFS-based methods are carried out on two real cell images sequences and demonstrate that both yield satisfactory results that are in good agreement with manual tracking method.
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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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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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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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Dissertations / Theses on the topic "PHD filter"

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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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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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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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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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Granström, Karl. "Extended target tracking using PHD filters." Doctoral thesis, Linköpings universitet, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-82348.

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The world in which we live is becoming more and more automated, exemplified by the numerous robots, or autonomous vehicles, that operate in air, on land, or in water. These robots perform a wide array of different tasks, ranging from the dangerous, such as underground mining, to the boring, such as vacuum cleaning. In common for all different robots is that they must possess a certain degree of awareness, both of themselves and of the world in which they operate. This thesis considers aspects of two research problems associated with this, more specifically the Simultaneous Localization and Mapping (SLAM) problem and the Multiple Target Tracking (MTT) problem. The SLAM problem consists of having the robot create a map of an environment and simultaneously localize itself in the same map. One way to reduce the effect of small errors that inevitably accumulate over time, and could significantly distort the SLAM result, is to detect loop closure. In this thesis loop closure detection is considered for robots equipped with laser range sensors. Machine learning is used to construct a loop closure detection classifier, and experiments show that the classifier compares well to related work. The resulting SLAM map should only contain stationary objects, however the world also contains moving objects, and to function well a robot should be able to handle both types of objects. The MTT problem consists of having the robot keep track of where the moving objects, called targets, are located, and how these targets are moving. This function has a wide range of applications, including tracking of pedestrians, bicycles and cars in urban environments. Solving the MTT problem can be decomposed into two parts: one part is finding out the number of targets, the other part is finding out what the states of the individual targets are. In this thesis the emphasis is on tracking of so called extended targets. An extended target is a target that can generate any number of measurements, as opposed to a point target that generates at most one measurement. More than one measurement per target raise interesting possibilities to estimate the size and the shape of the target. One way to model the number of targets and the target states is to use random finite sets, which leads to the Probability Hypothesis Density (PHD) filters. Two implementations of an extended target PHD filter are given, one using Gaussian mixtures and one using Gaussian inverse Wishart (GIW) mixtures. Two models for the size and shape of an extended target measured with laser range sensors are suggested. A framework for estimation of the number of measurements generated by the targets is presented, and reduction of GIW mixtures is addressed. Prediction, spawning and combination of extended targets modeled using GIW distributions is also presented. The extended target tracking functions are evaluated in simulations and in experiments with laser range data.<br>Den värld i vilken vi lever har med tiden blivit allt mer automatiserad. Ett av många tecken på detta är det stora antal robotar, eller autonoma farkoster, som verkar bland annat i luften, på land, eller i vatten. De här robotarna kan utföra ett brett spektrum av olika uppgifter, allt ifrån direkt farliga, som underjordisk gruvdrift och sanering av havererade kärnreaktorer, till alldagliga och tråkiga, som dammsugning och gräsklippning. På samma sätt som en människa behöver använda sina sinnen och sitt medvetande för att hantera vardagen, måste alla typer av robotar ha en viss medvetenhet för att kunna utföra sina uppgifter. Det krävs bland annat att robotarna kan uppfatta och förstå sin arbetsmiljö. I den här avhandlingen behandlas ett antal delar av två stycken övergripande forskningsproblem som är relaterade till detta. Det första forskningsproblemet kallas för samtidig positionering och kartering, vilket på engelska heter Simultaneous Localization and Mapping och förkortas SLAM. Det andra forskningsproblemet kallas för målföljning. SLAM-problemet går ut på att låta roboten skapa en karta av ett område, och samtidigt som kartan skapas positionera sig i den. Exakt vad som menas med karta i det här sammanhanget varierar beroende på robotens specifika arbetsuppgift. Exempelvis kan det, för en inomhusrobot, röra sig om en virtuell modell av var golv, väggar och möbler finns i ett hus. En oundviklig del av SLAM-problemet är att roboten hela tiden gör små fel, vilket påverkar kartan som skapas, samt hur väl roboten kan positionera sig. Enskilda fel har inte särskilt stor inverkan, men om felen ackumuleras under en längre tid kan det leda till att kartan förvrängs, eller att roboten helt enkelt inte kan finna sin position i kartan. Ett sätt att undvika att så sker är att utrusta roboten med en funktion vilken gör det möjligt för roboten att känna igen platser som den har besökt tidigare, vilket kallas platsigenkänning. När roboten känner igen en plats kan den jämföra med vad kartan och positionen säger. Om kartan och positionen inte säger att roboten är tillbaka på en plats som tidigare besökts kan denna diskrepans korrigeras. Resultatet är en karta och en position som bättre representerar verkligheten. I den här avhandlingen har platsigenkänning studerats för robotar som är utrustade med laserscanners, och en funktion för platsigenkänning har skapats. I en serie experiment har det visats att funktionen kan känna igen platser såväl inomhus i kontorsmiljö, som utomhus i stadsmiljö. Det har även visats att funktionens egenskaper jämför sig väl med tidigare arbete på området. Den resulterande SLAM-kartan bör av naturliga skäl endast innehålla stationära föremål. Vår värld innehåller dock även rörliga föremål, och för att en robot ska kunna arbeta på ett säkert sätt måste den även hålla reda på alla rörliga föremål som finns i dess närhet. Det andra forskningsproblemet som behandlats i avhandlingen, målföljning, går ut på att utrusta roboten med funktioner som gör det möjligt för den att hålla reda på var de rörliga målen är, samt vart de är på väg att röra sig. Exempelvis kan den här typen av funktioner användas till att hålla reda på fotgängare, cyklister och bilar i en stadsmiljö. Tidigare har forskningen inom målföljning varit fokuserad på så kallade punktmål. Vid följning av punktmål kan följningsproblemet sägas ha två delar: den ena är att räkna ut hur många rörliga mål det finns, den andra är att räkna ut var varje enskilt mål befinner sig, samt vart det är på väg. Här har fokus istället legat på följning av vad som kallas för utsträckta mål, en typ av mål som rönt ökande uppmärksamhet i forskningsvärlden de senaste fem till tio åren. Med utsträckta mål får följningsproblemet en tredje del: att för varje enskilt mål räkna ut storleken och formen på målet, det vill säga den spatiala utsträckningen. Att känna till utsträckningen på de rörliga målen är viktigt exempelvis för en robot som ska ta sig genom ett rum där många person befinner sig. För att göra det krävs att roboten rör sig nära personerna, utan att för den skull krocka med någon. Att lösa detta på ett bra sätt kräver att roboten har kunskap inte bara om var personerna befinner sig, utan även hur mycket plats de tar upp. I avhandlingen har ett antal aspekter av följning av utsträckta mål studerats. En viktig och komplicerande aspekt av följning av såväl punktmål, som utsträckta mål, är att roboten på förhand inte vet hur många mål som finns i dess närhet. En funktion för att hantera osäkerheterna kring antalet mål som finns, samt osäkerheterna kring var varje mål befinner sig, har implementerats. I många situationer är det nödvändigt att kunna prediktera, eller förutsäga, var de olika målen kommer att befinna sig i den närmaste framtiden. Det kan exempelvis röra sig om en robot som ska köra genom en vägkorsning, och då måste undvika att krocka med övrig trafik. För detta ändamål har en prediktionsfunktion tagits fram. När ett större antal mål rör sig i robotens närhet kan det bli svårt att följa varje enskilt mål. Istället kan roboten följa grupper av mål. Det blir då nödvändigt att hålla reda på vad som sker när mål lämnar gruppen, eller nya mål ansluter till gruppen. Fritt översatt från engelska till svenska kan dessa två händelser kallas för målproduktion och målkombination. Funktioner för att hantera produktion och kombination av utsträckta mål har tagits fram. För att roboten ska kunna beräkna ett måls spatiala utsträckning krävs modeller för formen på målen. När laserscanners används kan formen på en bil sägas vara approximativt rektangulär, och formen på en person kan sägas vara approximativt elliptisk. Beräkning av storleken på rektangulära och elliptiska mål har studerats för robotar utrustade med laserscanners. Målföljningsfunktionerna som nämnts ovan har utvärderats med hjälp av såväl simulerade data, som experimentella data insamlade med laserscanners. Resultaten visar att det arbete som har utförts jämför sig väl med tidigare arbete på området.<br>CADICS<br>ETT<br>CUAS
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Niedfeldt, Peter C. "Recursive-RANSAC: A Novel Algorithm for Tracking Multiple Targets in Clutter." BYU ScholarsArchive, 2014. https://scholarsarchive.byu.edu/etd/4195.

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Multiple target tracking (MTT) is the process of identifying the number of targets present in a surveillance region and the state estimates, or track, of each target. MTT remains a challenging problem due to the NP-hard data association step, where unlabeled measurements are identified as either a measurement of an existing target, a new target, or a spurious measurement called clutter. Existing techniques suffer from at least one of the following drawbacks: divergence in clutter, underlying assumptions on the number of targets, high computational complexity, time-consuming implementation, poor performance at low detection rates, and/or poor track continuity. Our goal is to develop an efficient MTT algorithm that is simple yet effective and that maintains track continuity enabling persistent tracking of an unknown number of targets. A related field to tracking is regression analysis, where the parameters of static signals are estimated from a batch or a sequence of data. The random sample consensus (RANSAC) algorithm was developed to mitigate the effects of spurious measurements, and has since found wide application within the computer vision community due to its robustness and efficiency. The main concept of RANSAC is to form numerous simple hypotheses from a batch of data and identify the hypothesis with the most supporting measurements. Unfortunately, RANSAC is not designed to track multiple targets using sequential measurements.To this end, we have developed the recursive-RANSAC (R-RANSAC) algorithm, which tracks multiple signals in clutter without requiring prior knowledge of the number of existing signals. The basic premise of the R-RANSAC algorithm is to store a set of RANSAC hypotheses between time steps. New measurements are used to either update existing hypotheses or generate new hypotheses using RANSAC. Storing multiple hypotheses enables R-RANSAC to track multiple targets. Good tracks are identified when a sufficient number of measurements support a hypothesis track. The complexity of R-RANSAC is shown to be squared in the number of measurements and stored tracks, and under moderate assumptions R-RANSAC converges in mean to the true states. We apply R-RANSAC to a variety of simulation, camera, and radar tracking examples.
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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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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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Fuzari, Junior Gilberto de Campos [UNESP]. "Obtenção e caracterização de filmes de PHB e de blendas de PHB com borracha natural." Universidade Estadual Paulista (UNESP), 2008. http://hdl.handle.net/11449/92003.

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Made available in DSpace on 2014-06-11T19:25:33Z (GMT). No. of bitstreams: 0 Previous issue date: 2008-02-25Bitstream added on 2014-06-13T19:53:25Z : No. of bitstreams: 1 fuzarijunior_gc_me_ilha.pdf: 1800316 bytes, checksum: 6ffbd14d47f4e72c963f316d2bd14074 (MD5)<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)<br>Filmes de PHB puro e de blendas de PHB com borracha natural foram obtidos por prensagem a quente e por “casting” e foram avaliados segundo as propriedades morfológicas, estruturais, térmicas e mecânicas, além de sua suscetibilidade à biodegradação. As blendas mostraram-se imiscíveis. Verificou-se que o processamento e a presença de borracha provocaram mudanças na configuração cristalina do PHB. A presença de borracha aumentou o grau de cristalização do PHB. Filmes tratados termicamente entre 60 e 70oC, apresentaram menor grau de cristalinidade, os quais permanecem estáveis quando resfriados à temperatura ambiente. O processamento por prensagem garantiu certa estabilidade ao filme de PHB puro em relação à degradação isotérmica, por ocasionar a compactação das cadeias poliméricas, compactação essa que é influenciada pela presença de borracha. Para a degradação não isotérmica, um maior conteúdo de borracha elevou a temperatura de degradação efetiva. O aumento de borracha na blenda também ocasionou maior deformação das blendas, com decréscimo de rigidez. O processamento por prensagem garantiu uma maior deformação para filmes de PHB puro em relação ao processamento por casting, entretanto para blendas com grande quantidade de borracha o efeito mostrou-se contrário. Análises de biodegradação em solo mostraram um material potencialmente biodegradável, sendo que a presença de borracha não atrapalhou o ataque microbiano<br>Pure PHB films and blends of PHB and natural rubber (in films form), were obtained by hot pressing and by casting and its morphological, structural, thermal and mechanical properties were studied using appropriated techniques. The susceptibility to the biodegradation was also analyzed. The blends did not show miscibility and crystallization degree of PHB in the blend showed to be dependent of rubber content and also film processing. Lower degree of crystallization was observed on films treated in the temperature range of 60oC to 70oC. This crystallization remains stable when the temperature drops to room temperature. Pure PHB films obtained by hot pressing showed stability regards to the isothermal degradation due to the polymer chain compaction, which is influenced by the rubber inclusion. The temperature degradation was increased as the rubber content was increased. The increasing content of rubber in the blend films also provide higher deformation with decreasing of stiff. Pure PHB films obtained by hot pressing showed higher deformation then those obtained by casting. However, for blend films with high rubber content, the effect was observed in the other way around. Furthermore, biodegradation analyzes in soil showed that the material is biodegradable and the rubber inclusion does not disturb the microbial attack
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Books on the topic "PHD filter"

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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 "PHD filter"

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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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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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Chen, Di, and Xiangyu Zhang. "A Multipath Effect Suppression Algorithm Based on the GM-PHD Filter in Skywave-OTHR." In Lecture Notes in Electrical Engineering. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-6571-2_98.

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Conference papers on the topic "PHD filter"

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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 "PHD filter"

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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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RAYMOND, DEWBERRY. Measured HEU Content in 110-Gallon overpacks and partly filled drums by field y-PHA Assay. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/839501.

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