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Relevant bibliographies by topics / Gaussian measures Kalman filtering

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Academic literature on the topic 'Gaussian measures Kalman filtering'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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Journal articles on the topic "Gaussian measures Kalman filtering"

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Gou, Linfeng, Ruiqian Sun, and Xiaobao Han. "FDIA System for Sensors of the Aero-Engine Control System Based on the Immune Fusion Kalman Filter." Mathematical Problems in Engineering 2021 (March 18, 2021): 1–17. http://dx.doi.org/10.1155/2021/6662425.

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The Kalman filter plays an important role in the field of aero-engine control system fault diagnosis. However, the design of the Kalman filter bank is complex, the structure is fixed, and the parameter estimation accuracy in the non-Gaussian environment is low. In this study, a new filtering method, immune fusion Kalman filter, was proposed based on the artificial immune system (AIS) theory and the Kalman filter algorithm. The proposed method was used to establish the fault diagnosis, isolation, and accommodation (FDIA) system for sensors of the aero-engine control system. Through a filtering calculation, the FDIA system reconstructs the measured parameters of the faulty sensor to ensure the reliable operation of the aero engine. The AIS antibody library based on single sensor fault was constructed, and with feature combination and library update, the FDIA system can reconstruct the measured values of multiple sensors. The evaluation of the FDIA system performance is based on the Monte Carlo method. Both steady and transient simulation experiments show that, under the non-Gaussian environment, the diagnosis and isolation accuracy of the immune fusion Kalman filter is above 95%, much higher than that of the Kalman filter bank, and compared with the Kalman particle filter, the reconstruction value is smoother, more accurate, and less affected by noise.

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Ali, Wasiq, Yaan Li, Zhe Chen, Muhammad Asif Zahoor Raja, Nauman Ahmed, and Xiao Chen. "Application of Spherical-Radial Cubature Bayesian Filtering and Smoothing in Bearings Only Passive Target Tracking." Entropy 21, no. 11 (November 7, 2019): 1088. http://dx.doi.org/10.3390/e21111088.

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In this paper, an application of spherical radial cubature Bayesian filtering and smoothing algorithms is presented to solve a typical underwater bearings only passive target tracking problem effectively. Generally, passive target tracking problems in the ocean environment are represented with the state-space model having linear system dynamics merged with nonlinear passive measurements, and the system is analyzed with nonlinear filtering algorithms. In the present scheme, an application of spherical radial cubature Bayesian filtering and smoothing is efficiently investigated for accurate state estimation of a far-field moving target in complex ocean environments. The nonlinear model of a Kalman filter based on a Spherical Radial Cubature Kalman Filter (SRCKF) and discrete-time Kalman smoother known as a Spherical Radial Cubature Rauch–Tung–Striebel (SRCRTS) smoother are applied for tracking the semi-curved and curved trajectory of a moving object. The worth of spherical radial cubature Bayesian filtering and smoothing algorithms is validated by comparing with a conventional Unscented Kalman Filter (UKF) and an Unscented Rauch–Tung–Striebel (URTS) smoother. Performance analysis of these techniques is performed for white Gaussian measured noise variations, which is a significant factor in passive target tracking, while the Bearings Only Tracking (BOT) technology is used for modeling of a passive target tracking framework. Simulations based experiments are executed for obtaining least Root Mean Square Error (RMSE) among a true and estimated position of a moving target at every time instant in Cartesian coordinates. Numerical results endorsed the validation of SRCKF and SRCRTS smoothers with better convergence and accuracy rates than that of UKF and URTS for each scenario of passive target tracking problem.

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Ali, Wasiq, Yaan Li, Muhammad Asif Zahoor Raja, Wasim Ullah Khan, and Yigang He. "State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing." Entropy 23, no. 9 (August 29, 2021): 1124. http://dx.doi.org/10.3390/e23091124.

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In this study, an application of deep learning-based neural computing is proposed for efficient real-time state estimation of the Markov chain underwater maneuvering object. The designed intelligent strategy is exploiting the strength of nonlinear autoregressive with an exogenous input (NARX) network model, which has the capability for estimating the dynamics of the systems that follow the discrete-time Markov chain. Nonlinear Bayesian filtering techniques are often applied for underwater maneuvering state estimation applications by following state-space methodology. The robustness and precision of NARX neural network are efficiently investigated for accurate state prediction of the passive Markov chain highly maneuvering underwater target. A continuous coordinated turning trajectory of an underwater maneuvering object is modeled for analyzing the performance of the neural computing paradigm. State estimation modeling is developed in the context of bearings only tracking technology in which the efficiency of the NARX neural network is investigated for ideal and complex ocean environments. Real-time position and velocity of maneuvering object are computed for five different cases by varying standard deviations of white Gaussian measured noise. Sufficient Monte Carlo simulation results validate the competence of NARX neural computing over conventional generalized pseudo-Bayesian filtering algorithms like an interacting multiple model extended Kalman filter and an interacting multiple model unscented Kalman filter.

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Mulimani, neshwari, and Aziz Makandar. "Sports Video Annotation and Multi- Target Tracking using Extended Gaussian Mixture model." International Journal of Recent Technology and Engineering 10, no. 1 (May 30, 2021): 1–6. http://dx.doi.org/10.35940/ijrte.a5589.0510121.

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Video offers solutions to many of the traditional problems with coach, trainer, commenter, umpires and other security issues of modern team games. This paper presents a novel framework to perform player identification and tracking technique for the sports (Kabaddi) with extending the implementation towards the event handling process which expands the game analysis of the third umpire assessment. In the proposed methodology, video preprocessing has done with Kalman Filtering (KF) technique. Extended Gaussian Mixture Model (EGMM) implemented to detect the object occlusions and player labeling. Morphological operations have given the more genuine results on player detection on the spatial domain by applying the silhouette spot model. Team localization and player tracking has done with Robust Color Table (RCT) model generation to classify each team members. Hough Grid Transformation (HGT) and Region of Interest (RoI) method has applied for background annotation process. Through which each court line tracing and labeling in the half of the court with respect to their state-of-art for foremost event handling process is performed. Extensive experiments have been conducted on real time video samples to meet out the all the challenging aspects. Proposed algorithm tested on both Self Developed Video (SDV) data and Real Time Video (RTV) with dynamic background for the greater tracking accuracy and performance measures in the different state of video samples.

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Guardeño, Rafael, Manuel J. López, and Víctor M. Sánchez. "MIMO PID Controller Tuning Method for Quadrotor Based on LQR/LQG Theory." Robotics 8, no. 2 (May 1, 2019): 36. http://dx.doi.org/10.3390/robotics8020036.

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In this work, a new pre-tuning multivariable PID (Proportional Integral Derivative) controllers method for quadrotors is put forward. A procedure based on LQR/LQG (Linear Quadratic Regulator/Gaussian) theory is proposed for attitude and altitude control, which suposes a considerable simplification of the design problem due to only one pretuning parameter being used. With the aim to analyze the performance and robustness of the proposed method, a non-linear mathematical model of the DJI-F450 quadrotor is employed, where rotors dynamics, together with sensors drift/bias properties and noise characteristics of low-cost commercial sensors typically used in this type of applications are considered. In order to estimate the state vector and compensate bias/drift effects in the measures, a combination of filtering and data fusion algorithms (Kalman filter and Madgwick algorithm for attitude estimation) are proposed and implemented. Performance and robustness analysis of the control system is carried out by employing numerical simulations, which take into account the presence of uncertainty in the plant model and external disturbances. The obtained results show the proposed controller design method for multivariable PID controller is robust with respect to: (a) parametric uncertainty in the plant model, (b) disturbances acting at the plant input, (c) sensors measurement and estimation errors.

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Tavakoli, Reza, Sanjay Srinivasan, and Mary F. Wheeler. "Rapid Updating of Stochastic Models by Use of an Ensemble-Filter Approach." SPE Journal 19, no. 03 (December 31, 2013): 500–513. http://dx.doi.org/10.2118/163673-pa.

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Summary Applying an ensemble Kalman filter (EnKF) is an effective method for reservoir history matching. The underlying principle is that an initial ensemble of stochastic models can be progressively updated to reflect measured values as they become available. The EnKF performance is only optimal, however, if the prior-state vector is linearly related to the predicted data and if the joint distribution of the prior-state vector is multivariate Gaussian. Therefore, it is challenging to implement the filtering scheme for non-Gaussian random fields, such as channelized reservoirs, in which the continuity of permeability extremes is well-preserved. In this paper, we develop a methodology by combining model classification with multidimensional scaling (MDS) and the EnKF to create rapidly updating models of a channelized reservoir. A dissimilarity matrix is computed by use of the dynamic responses of ensemble members. This dissimilarity matrix is transformed into a lower-dimensional space by use of MDS. Responses mapped in the lower-dimension space are clustered, and on the basis of the distances between the models in a cluster and the actual observed response, the closest models to the observed response are retrieved. Model updates within the closest cluster are performed with EnKF equations. The results of an update are used to resample new models for the next step. Two-dimensional, waterflooding examples of channelized reservoirs are provided to demonstrate the applicability of the proposed method. The obtained results demonstrate that the proposed algorithm is viable both for sequentially updating reservoir models and for preserving channel features after the data-assimilation process.

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Küper, Armin, and Steffen Waldherr. "Numerical Gaussian process Kalman filtering." IFAC-PapersOnLine 53, no. 2 (2020): 11416–21. http://dx.doi.org/10.1016/j.ifacol.2020.12.577.

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Garcia-Fernandez, Angel F., and Lennart Svensson. "Gaussian MAP Filtering Using Kalman Optimization." IEEE Transactions on Automatic Control 60, no. 5 (May 2015): 1336–49. http://dx.doi.org/10.1109/tac.2014.2372909.

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Niehsen, W. "Robust Kalman filtering with generalized Gaussian measurement noise." IEEE Transactions on Aerospace and Electronic Systems 38, no. 4 (October 2002): 1409–12. http://dx.doi.org/10.1109/taes.2002.1145765.

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Todescato, Marco, Andrea Carron, Ruggero Carli, Gianluigi Pillonetto, and Luca Schenato. "Efficient spatio-temporal Gaussian regression via Kalman filtering." Automatica 118 (August 2020): 109032. http://dx.doi.org/10.1016/j.automatica.2020.109032.

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Dissertations / Theses on the topic "Gaussian measures Kalman filtering"

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Caputi, Mauro J. "NonGaussian estimation using a modified Gaussian sum adaptive filter." Diss., This resource online, 1991. http://scholar.lib.vt.edu/theses/available/etd-07282008-135232/.

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Zhao, Yong. "Ensemble Kalman filter method for Gaussian and non-Gaussian priors /." Access abstract and link to full text, 2008. http://0-wwwlib.umi.com.library.utulsa.edu/dissertations/fullcit/3305718.

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Schnatter, Sylvia. "Integration-based Kalman-filtering for a Dynamic Generalized Linear Trend Model." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1991. http://epub.wu.ac.at/424/1/document.pdf.

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The topic of the paper is filtering for non-Gaussian dynamic (state space) models by approximate computation of posterior moments using numerical integration. A Gauss-Hermite procedure is implemented based on the approximate posterior mode estimator and curvature recently proposed in 121. This integration-based filtering method will be illustrated by a dynamic trend model for non-Gaussian time series. Comparision of the proposed method with other approximations ([15], [2]) is carried out by simulation experiments for time series from Poisson, exponential and Gamma distributions. (author's abstract)
Series: Forschungsberichte / Institut für Statistik

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Frühwirth-Schnatter, Sylvia. "Applied State Space Modelling of Non-Gaussian Time Series using Integration-based Kalman-filtering." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1993. http://epub.wu.ac.at/1558/1/document.pdf.

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The main topic of the paper is on-line filtering for non-Gaussian dynamic (state space) models by approximate computation of the first two posterior moments using efficient numerical integration. Based on approximating the prior of the state vector by a normal density, we prove that the posterior moments of the state vector are related to the posterior moments of the linear predictor in a simple way. For the linear predictor Gauss-Hermite integration is carried out with automatic reparametrization based on an approximate posterior mode filter. We illustrate how further topics in applied state space modelling such as estimating hyperparameters, computing model likelihoods and predictive residuals, are managed by integration-based Kalman-filtering. The methodology derived in the paper is applied to on-line monitoring of ecological time series and filtering for small count data. (author's abstract)
Series: Forschungsberichte / Institut für Statistik

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Xu, Teng. "Ensemble Kalman filtering for hydraulic conductivity characterization: Parallelization and non-Gaussianity." Doctoral thesis, Universitat Politècnica de València, 2014. http://hdl.handle.net/10251/43769.

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The ensemble Kalman filter (EnKF) is nowadays recognized as an excellent inverse method for hydraulic conductivity characterization using transient piezometric head data. and it is proved that the EnKF is computationally efficient and capable of handling large fields compared to other inverse methods. However, it is needed a large ensemble size (Chen and Zhang, 2006) to get a high quality estimation, which means a lots of computation time. Parallel computing is an efficient alterative method to reduce the commutation time. Besides, although the EnKF is good accounting for the non linearities of the state equation, it fails when dealing with non-Gaussian distribution fields. Recently, many methods are developed trying to adapt the EnKF to non-Gaussian distributions(detailed in the History and present state chapter). Zhou et al. (2011, 2012) have proposed a Normal-Score Ensemble Kalman Filter (NS-EnKF) to character the non-Gaussian distributed conductivity fields, and already showed that transient piezometric head was enough for hydraulic conductivity characterization if a training image for the hydraulic conductivity was available. Then in this work, we will show that, when without such a training image but with enough transient piezometric head information, the performance of the updated ensemble of realizations in the characterization of the non-Gaussian reference field. In the end, we will introduce a new method for parameterizing geostatistical models coupling with the NS-EnKF in the characterization of a Heterogenous non-Gaussian hydraulic conductivity field. So, this doctor thesis is mainly including three parts, and the name of the parts as below. 1, Parallelized Ensemble Kalman Filter for Hydraulic Conductivity Characterization. 2, The Power of Transient Piezometric Head Data in Inverse Modeling: An Application of the Localized Normal-score EnKF with Covariance Inflation in a Heterogenous Bimodal Hydraulic Conductivity Field. 3, Parameterizing geostatistical models coupling with the NS-EnKF for Heterogenous Bimodal Hydraulic Conductivity characterization.
Xu, T. (2014). Ensemble Kalman filtering for hydraulic conductivity characterization: Parallelization and non-Gaussianity [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/43769
TESIS

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Sederlin, Michael. "Traffic State Estimation for Signalized Intersections : A Combined Gaussian Process Bayesian Filter Approach." Thesis, KTH, Transportplanering, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284198.

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Traffic State Estimation (TSE) is a vital component in traffic control which requires an accurate viewof the current traffic situation. Since there is no full sensor coverage and the collected measurementsare inflicted with random noise, statistical estimation techniques are necessary to accomplish this.Common methods, which have been used in highway applications for several decades, are state-spacemodels in the form of Kalman Filters and Particle Filters. These methods are forms of BayesianFilters, and rely on transition models to describe the system dynamics, and observation models torelate collected measurements to the current state. Reliable estimation of traffic in urban environmentshas been considered more difficult than in highways owing to the increased complexity.This MsC thesis build upon previous research studying the use of non-parametric Gaussian Processtransition and measurement models in an extended Kalman Filter to achieve short-term TSE. To dothis, models requiring different feature sets are developed and analysed, as well as a hybrid approchcombining non-parametric and parametric models through an analytical mean function based on vehicleconservation law. The data used to train and test the models was collected in a simulated signalizedintersection constructed in SUMO.The presented results show that the proposed method has potential to performing short-term TSE inthis context. A strength in the proposed framework comes from the probabilistic nature of the GaussianProcesses, as it removes the need to manually calibrate the filter parameters of the Kalman Filter. Themean absolute error (MAE) lies between one and five vehicles for estimation of a one hour long dataseries with varying traffic demand. More importantly, the method has desirable characteristics andcaptures short-term fluctuations as well as larger scale demand changes better than a previously proposedmodel using the same underlying framework. In the cases with poorer performance, the methodprovided estimates unrelated to the system dynamics as well as large error bounds. While the causefor this was not determined, several hypotheses are presented and analysed. These results are takento imply that the combination of BF and GP models has potential for short-term TSE in a signalizedintersection, but that more work is necessary to provide reliable algorithms with known bounds. In particular,the relative ease of augmenting an available analytical model, built on conventional knowledgein traffic modelling, with a non-parametric GP is highlighted.

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Mackenzie, Mark. "Correlation with the hermite series using artificial neural network technology." Access electronically, 2004. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20050202.122218/index.html.

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Freitas, Greice Martins de. "Rastreamento de objetos em vídeos e separação em classes." [s.n.], 2010. http://repositorio.unicamp.br/jspui/handle/REPOSIP/258882.

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Orientador: Clésio Luis Tozzi
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
Made available in DSpace on 2018-08-16T06:32:57Z (GMT). No. of bitstreams: 1 Freitas_GreiceMartinsde_M.pdf: 16453422 bytes, checksum: fa0ae64561fd346237c57310fb0d0073 (MD5) Previous issue date: 2010
Resumo: A crescente utilização de câmeras de vídeo para o monitoramento de ambientes, auxiliando no controle de entrada, saída e trânsito de indivíduos ou veículos tem aumentado a busca por sistemas visando a automatização do processo de monitoramento por vídeos. Como requisitos para estes sistemas identificam-se o tratamento da entrada e saída de objetos na cena, variações na forma e movimentação dos alvos seguidos, interações entre os alvos como encontros e separações, variações na iluminação da cena e o tratamento de ruídos presentes no vídeo. O presente trabalho analisa e avalia as principais etapas de um sistema de rastreamento de múltiplos objetos através de uma câmera de vídeo fixa e propõe um sistema de rastreamento baseado em sistemas encontrados na literatura. O sistema proposto é composto de três fases: identificação do foreground através de técnicas de subtração de fundo; associação de objetos quadro a quadro através de métricas de cor, área e posição do centróide - com o auxílio da aplicação do filtro de Kalman - e, finalmente, classificação dos objetos a cada quadro segundo um sistema de gerenciamento de objetos. Com o objetivo de verificar a eficiência do sistema de rastreamento proposto, testes foram realizados utilizando vídeos das bases de dados PETS e CAVIAR. A etapa de subtração de fundo foi avaliada através da comparação do modelo Eigenbackground, utilizado no presente sistema, com o modelo Mistura de Gaussianas, modelo de subtração de fundo mais utilizado em sistemas de rastreamento. O sistema de gerenciamento de objeto foi avaliado por meio da classificação e contagem manual dos objetos a cada quadro do vídeo. Estes resultados foram comparados à saída do sistema de gerenciamento de objetos. Os resultados obtidos mostraram que o sistema de rastreamento proposto foi capaz de reconhecer e rastrear objetos em movimento em sequências de vídeos, lidando com oclusões e separações, mostrando adequabilidade para aplicação em sistemas de segurança em tempo real
Abstract: There are immediate needs for the use of video cameras in environment monitoring, which can be verified by the task of assisting the entrance, exit and transit registering of people or vehicles in a area. In this context, automated surveillance systems based on video images are increasingly gaining interest. As requisites for these systems, it can be identified the treatment of entrances and exits of objects on a scene, shape variation and movement of followed targets, interactions between targets (such as meetings and splits), lighting variations and video noises. This work analyses and evaluates the main steps of a multiple target tracking system through a fixed video camera and proposes a tracking system based on approaches found in the literature. The proposed system is composed of three steps: foreground identification through background subtraction techniques; object association through color, area and centroid position matching, by using the Kalman filter to estimate the object's position in the next frame, and, lastly, object classification according an object management system. In order to assess the efficiency of the proposed tracking system, tests were performed by using videos from PETS and CAVIAR datasets. The background subtraction step was evaluated by means of a comparison between the Eigenbackground model, used in the proposed tracking system, and the Mixture of Gaussians model, one of the most used background subtraction models. The object management system was evaluated through manual classification and counting of objects on each video frame. These results were compared with the output of the object management system. The obtained results showed that the proposed tracking system was able to recognize and track objects in movement on videos, as well as dealing with occlusions and separations, and, at the same time, encouraging future studies in order for its application on real time security systems
Mestrado
Engenharia de Computação
Mestre em Engenharia Elétrica

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Abbassi, Noufel. "Chaînes de Markov triplets et filtrage optimal dans les systemes à sauts." Phd thesis, Institut National des Télécommunications, 2012. http://tel.archives-ouvertes.fr/tel-00873630.

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Cette thèse est consacrée à la restauration et l'estimation des paramètres par filtrage dans les modèles de chaîne de Markov cachée classique, couple et triplet à sauts Markoviens. Nous proposons deux nouvelles méthodes d'approximation dans le cas des systèmes linéaires gaussiens à sauts Markoviens. La première est fondée sur l'utilisation des chaînes de Markov cachées par du bruit à mémoire longue, on obtient alors une méthode " partiellement non supervisée" dans la quelle certains paramètres, peuvent être estimés en utilisant une version adaptative de l'algorithme EM ou ICE, les résultats obtenus sont encourageant et comparables avec les méthodes classiquement utilisées du type (Kalman/Particulaire). La deuxième exploite l'idée de ne garder à chaque instant que les trajectoires les plus probables; là aussi, on obtient une méthode très rapide donnant des résultats très intéressants. Nous proposons par la suite deux familles de modèles à sauts qui sont originaux. la première est très générale où le processus couple composé du processus d'intérêt et celui des observations conditionnellement aux sauts, est une chaîne de Markov cachée, et nous proposons une extension du filtrage particulaire à cette famille. La deuxième, est une sous famille de la première où le couple composé de la chaîne des sauts et le processus d'observations est Markovien dans ce dernier cas le filtrage optimal exact est possible avec une complexité linéaire dans le temps. L'utilisation de la deuxième famille en tant qu'approximation de la première est alors étudiée et les résultats exposés dans ce mémoire semblent très encourageants

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"Applied State Space Modelling of Non-Gaussian Time Series using Integration-based Kalman-filtering." Department of Statistics and Mathematics, 1993. http://epub.wu-wien.ac.at/dyn/dl/wp/epub-wu-01_a20.

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Books on the topic "Gaussian measures Kalman filtering"

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Kallianpur, G. White noise theory of prediction, filtering, and smoothing. New York: Gordon and Breach Science Publishers, 1988.

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Book chapters on the topic "Gaussian measures Kalman filtering"

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Gawarecki, L., and V. Mandrekar. "Non-linear filtering with Gaussian martingale noise: Kalman filter with fBm noise." In Institute of Mathematical Statistics Lecture Notes - Monograph Series, 92–97. Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2004. http://dx.doi.org/10.1214/lnms/1196285382.

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Punithakumar, Kumaradevan, Ismail Ben Ayed, Ali Islam, Ian G. Ross, and Shuo Li. "Regional Heart Motion Abnormality Detection via Information Measures and Unscented Kalman Filtering." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010, 409–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15705-9_50.

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Wu, Huaiyi, and Guanrong Chen. "Suboptimal Kalman Filtering for Linear Systems with Non-Gaussian Noise." In Approximate Kalman Filtering, 113–36. WORLD SCIENTIFIC, 1993. http://dx.doi.org/10.1142/9789814317399_0008.

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Hong, Lang. "Distributed Filtering Using Set Models for Systems with Non-Gaussian Noise." In Approximate Kalman Filtering, 161–76. WORLD SCIENTIFIC, 1993. http://dx.doi.org/10.1142/9789814317399_0010.

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"Dynamic Gaussian Force Field Controlled Kalman Filtering For Pointing Interaction." In Mensch & Computer 2013 – Tagungsband, 261–70. Oldenbourg Wissenschaftsverlag, 2013. http://dx.doi.org/10.1524/9783486781229.261.

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Bilik, I., and J. Tabriki. "MMSE-Based Filtering for Linear and Nonlinear Systems in the Presence of Non-Gaussian System and Measurement Noise." In Kalman Filter Recent Advances and Applications. InTech, 2009. http://dx.doi.org/10.5772/6800.

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Aytun, Alper, and Serol Bulkan. "Bearing-Only Target Motion Analysis." In Operations Research for Military Organizations, 330–46. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5513-1.ch014.

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The main goal of the bearing-only target motion analysis (BOTMA) is to determine the target's kinematic parameters such as position, course, and speed by only using the bearings reported by an onboard passive sensor (e.g., a sonar or an ESM [electronic support measures] device). This chapter provides a brief description of the BOTMA problem. Next, it discusses the implementation of the Kalman filtering technique to solve the problem. The authors then discuss the variations of the Kalman filtering (i.e., the extended and unscented Kalman filters). They also propose a genetic algorithm metaheuristic that incorporates a novel search space narrowing technique to solve the BOTMA problem and present numerical results for different noise conditions. They finally highlight the future research directions for modeling and solving the BOTMA problem.

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Conference papers on the topic "Gaussian measures Kalman filtering"

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Abbassi, Noufel, Dalila Benboudjema, and Wojciech Pieczynski. "Kalman filtering approximations in triplet Markov Gaussian switching models." In 2011 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2011. http://dx.doi.org/10.1109/ssp.2011.5967820.

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Gal, Janos, Andrei Caimpeanu, and Ioan Nafornita. "Estimation of Chirp Signals in Gaussian Noise by Kalman Filtering." In International Symposium on Signals, Circuits and Systems. IEEE, 2007. http://dx.doi.org/10.1109/isscs.2007.4292711.

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Ait-El-Fquih, Boujemaa, Thomas Rodet, and Ibrahim Hoteit. "Unsupervised Variational Bayesian Kalman Filtering For Large-Dimensional Gaussian Systems." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053698.

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Fasano, Antonio, Alfredo Germani, and Andrea Monteriu. "Reduced-order quadratic Kalman-like filtering for non-Gaussian systems." In 2012 IEEE 51st Annual Conference on Decision and Control (CDC). IEEE, 2012. http://dx.doi.org/10.1109/cdc.2012.6426690.

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Kuzin, Danil, Le Yang, Olga Isupova, and Lyudmila Mihaylova. "Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning." In 2018 International Conference on Information Fusion (FUSION). IEEE, 2018. http://dx.doi.org/10.23919/icif.2018.8455785.

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Hartikainen, Jouni, and Simo Sarkka. "Kalman filtering and smoothing solutions to temporal Gaussian process regression models." In 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2010. http://dx.doi.org/10.1109/mlsp.2010.5589113.

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Nsour, Ahmad, Alhaj-Saleh Abdallah, and Mohammed Zohdy. "GFSK phase estimation using Extended Kalman filtering for Non-Gaussian noise." In 2013 Wireless Telecommunications Symposium (WTS 2013). IEEE, 2013. http://dx.doi.org/10.1109/wts.2013.6566237.

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Kaibi Zhang, Yangchuan Zhang, and Subo Wan. "Research of RSSI indoor ranging algorithm based on Gaussian - Kalman linear filtering." In 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). IEEE, 2016. http://dx.doi.org/10.1109/imcec.2016.7867493.

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Hostettler, Roland, Ossi Kaltiokallio, Huseyin Yigitler, Simo Sarkka, and Riku Jantti. "RSS-based respiratory rate monitoring using periodic Gaussian processes and Kalman filtering." In 2017 25th European Signal Processing Conference (EUSIPCO). IEEE, 2017. http://dx.doi.org/10.23919/eusipco.2017.8081208.

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Nagi, Imre, Darren Yin, Ali Yousafzai, Dimitrios Tzannetos, Ole J. Mengshoel, Rodney Martin, and Chetan S. Kulkarni. "Exploring Gaussian Process Regression and Unscented Kalman Filtering for Lithium-ion Battery Prognostics." In AIAA Scitech 2019 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2019. http://dx.doi.org/10.2514/6.2019-0685.

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