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    Wyświetlanie 1-9 z 9
    Tytuł:
    Estimation of orientation and position of the manipulator using the extended kalman filter based on measurements from the accelerometers
    Autorzy:
    Kobierska, Agnieszka
    Rakowski, Piotr
    Podsędkowski, Leszek
    Powiązania:
    https://bibliotekanauki.pl/articles/950743.pdf
    Data publikacji:
    2019
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    EKF
    accelerometers
    measuring arm
    Opis:
    Authors present the kinematic structure of measurement arm along with its construction for efficient estimation of orientation and position of the manipulator using extended Kalman filter. The major innovation of the arm is that it only uses accelerometers as gravity sensors for determining relative positions of the links. This article presents the problem of position estimation based on measurements with high noise and the use of the extended Kalman filter to limit the impact of noise on the measurement. Repeatability tests were performed using custom made test stand.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2019, 13, 2; 23-27
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Simulation and experimental evaluation of the EKF simultaneous localization and mapping algorithm on the wifibot mobile robot
    Autorzy:
    Ayadi, N.
    Derbel, N.
    Morette, N.
    Novales, C.
    Poisson, G.
    Powiązania:
    https://bibliotekanauki.pl/articles/91547.pdf
    Data publikacji:
    2018
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    mobile robot
    localisation
    EKF
    SLAM
    consistency
    Opis:
    In recent years, autonomous navigation for mobile robots has been considered a highly active research field. Within this context, we are interested to apply the Simultaneous Localization And Mapping (SLAM) approach for a wheeled mobile robot. The Extended Kalman Filter has been chosen to perform the SLAM algorithm. In this work, we explicit all steps of the approach. Performances of the developed algorithm have been assessed through simulation in the case of a small scale map. Then, we present several experiments on a real robot that are proceeded in order to exploit a programmed SLAM unit and to generate the navigation map. Based on experimental results, simulation of the SLAM method in the case of a large scale map is then realized. Obtained results are exploited in order to evaluate and compare the algorithm’s consistency and robustness for both cases.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2018, 8, 2; 91-101
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Joint Channel and Carrier Estimation Using Extended Kalman Filter
    Autorzy:
    Hanza, G.
    Powiązania:
    https://bibliotekanauki.pl/articles/227355.pdf
    Data publikacji:
    2013
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    extended Kalman filter
    EKF
    joint channel
    carrier estimation
    synchronization
    digital receiver
    Opis:
    In this paper a proposal of joint channel and carrier estimation using extended Kalman filter (EKF) is presented. For the proposed algorithm simulations are performed and the results are compared with the separate channel estimation based on the Kalman filter (KF) and carrier estimation based on the extended Kalman filter (EKF).
    Źródło:
    International Journal of Electronics and Telecommunications; 2013, 59, 1; 33-39
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Real-time parameter estimation study for inertia properties of ground vehicles
    Metody estymacji parametrów w czasie rzeczywistym dla wyznaczania właściwości inercyjnych pojazdu terenowego
    Autorzy:
    Kolansky, J.
    Sandu, C.
    Powiązania:
    https://bibliotekanauki.pl/articles/139960.pdf
    Data publikacji:
    2013
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    parameter estimation
    EKF
    polynomial chaos
    bayesian statistics
    estymacja parametrów
    chaos wielomianowy
    statystyka bayesowska
    Opis:
    Vehicle parameters have a significant impact on handling, stability, and rollover propensity. This study demonstrates two methods that estimate the inertia values of a ground vehicle in real-time. Through the use of the Generalized Polynomial Chaos (gPC) technique for propagating the uncertainties, the uncertain vehicle model outputs a probability density function for each of the variables. These probability density functions (PDFs) can be used to estimate the values of the parameters through several statistical methods. The method used here is the Maximum A-Posteriori (MAP) estimate. The MAP estimate maximizes the distribution of P(β ׀z) where β is the vector of the PDFs of the parameters and z is the measurable sensor comparison. An alternative method is the application of an adaptive filtering method. The Kalman Filter is an example of an adaptive filter. This method, when blended with the gPC theory is capable at each time step of updating the PDFs of the parameter distributions. These PDF’s have their median values shifted by the filter to approximate the actual values.
    Parametry pojazdu mają znaczny wpływ na jego właściwości, takie jak sterowalność, stabilność i odporność na wywrócenie. W pracy przedstawiono dwie metody estymacji parametrów inercyjnych pojazdu terenowego w czasie rzeczywistym. W modelu pojazdu z niepewnościami wyznacza się funkcje gęstości prawdopodobieństwa (PDF) dla każdej wielkości opisując propagację niepewności przez zastosowanie techniki uogólnionego chaosu wielomianowego (gPC). Funkcje te mogą być użyte do estymacji wartości parametrów przy wykorzystaniu różnych metod statystycznych. W pracy zastosowano metodę maksymalnego estymatora a posteriori (MAP). Estymator MAP maksymalizuje funkcję rozkładu P(β ׀z), gdzie β jest wektorem funkcji gęstości prawdopodobieństwa parametrów, a z jest wielkością mierzalną, otrzymaną z porównania wyjść czujników. Metodą alternatywną jest zastosowanie filtru adaptacyjnego, którego przykładem jest filtr Kalmana. Metoda ta, w połączeniu z techniką uogólnionego chaosu wielomianowego (gPC), umożliwia, w każdym kroku adaptacji, uaktualnianie funkcji gęstości prawdopodobieństwa (PDF) parametrów systemu. Działanie filtru powoduje, że mediany tych funkcji zmieniają się dążąc do rzeczywistych wartości poszukiwanych parametrów.
    Źródło:
    Archive of Mechanical Engineering; 2013, LX, 1; 7-21
    0004-0738
    Pojawia się w:
    Archive of Mechanical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Identification of Ship Maneuvering Model Using Extended Kalman Filters
    Autorzy:
    Shi, C.
    Zhao, D.
    Peng, J.
    Shen, C.
    Powiązania:
    https://bibliotekanauki.pl/articles/116777.pdf
    Data publikacji:
    2009
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    Manoeuvring
    Ship Manoeuvering Model
    Kalman filter
    extended Kalman filter (EKF)
    Ship Manoeuvrability
    ship handling simulator
    Turning Circle Test
    Zig-Zag Test
    Opis:
    Ship maneuvering models are the keys to the research of ship maneuverability, design of ship motion control system and development of ship handling simulators. For various frames of ship maneuvering models, determining the parameters of the models is always a tedious task. System identification theory can be used to establish system mathematical models by the system’s input data and output data. In this paper, based on the analysis of ship hydrodynamics, a nonlinear model frame of ship maneuvering is established. System identification theory is employed to estimate the parameters of the model. An algorithm based on the extended Kalman filter theory is proposed to calculate the parameters. In order to gain the system’s input and output data, which is necessary for the parameters identification experiment, turning circle tests and Zig-zag tests are performed on shiphandling simulator and the initial data is collected. Based on the Fixed Interval Kalman Smoothing algorithm, a pre-processing algorithm is proposed to process the raw data of the tests. With this algorithm, the errors introduced during the measurement process are eliminated. Parameters identification experiments are designed to estimate the model parameters, and the ship maneuvering model parameters estimation algorithm is extended to modify the parameters being estimated. Then the model parameters and the ship maneuvering model are determined. Simulation validation was carried out to simulate the ship maneuverability. Comparisons have been made to the simulated data and measured data. The results show that the ship maneuvering model determined by our approach can seasonably reflect the actual motion of ship, and the parameter estimation procedure and algorithms are effective.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2009, 3, 1; 105-110
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Dynamic performance of estimator-based speed sensorless control of induction machines using extended and unscented Kalman filters
    Autorzy:
    Horváth, K.
    Kuslits, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/1193590.pdf
    Data publikacji:
    2018
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    induction machine
    speed sensorless control
    field-oriented control
    FOC
    non-linear state estimation
    load torque estimation
    extended Kalman filter
    EKF
    unscented Kalman filter
    UKF
    Opis:
    This paper presents an estimator-based speed sensorless field-oriented control (FOC) method for induction machines, where the state estimator is based on a self-contained, non-linear model. This model characterises both the electrical and the mechanical behaviours of the machine and describes them with seven state variables. The state variables are estimated from the measured stator currents and from the known stator voltages by using an estimator algorithm. An important aspect is that one of the state variables is the load torque and, hence, it is also estimated by the estimator. Using this feature, the applied estimator-based speed sensorless control algorithm may be operated adequately besides varying load torque. In this work, two different variants of the control algorithm are developed based on the extended and the unscented Kalman filters (EKF, UKF) as state estimators. The dynamic performance of these variants is tested and compared using experiments and simulations. Results show that the variants have comparable performance in general, but the UKF-based control provides better performance if a stochastically varying load disturbance is present.
    Źródło:
    Power Electronics and Drives; 2018, 3, 38; 129-144
    2451-0262
    2543-4292
    Pojawia się w:
    Power Electronics and Drives
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Data Integration from GPS and Inertial Navigation Systems for Pedestrians in Urban Area
    Autorzy:
    Bikonis, K.
    Demkowicz, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/115987.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    Inertial Measurement Unit (IMU)
    Urban Area
    inertial navigation system (INS)
    Global Positioning System GPS
    extended Kalman filter (EKF)
    pedestrian trajectory
    Micro Electro Mechanical Systems (MEMS)
    Integration of Navigation
    Opis:
    The GPS system is widely used in navigation and the GPS receiver can offer long-term stable absolute positioning information. The overall system performance depends largely on the signal environments. The position obtained from GPS is often degraded due to obstruction and multipath effect caused by buildings, city infrastructure and vegetation, whereas, the current performance achieved by inertial navigation systems (INS) is still relatively poor due to the large inertial sensor errors. The complementary features of GPS and INS are the main reasons why integrated GPS/INS systems are becoming increasingly popular. GPS/INS systems offer a high data rate, high accuracy position and orientation that can work in all environments, particularly those where satellite availability is restricted. In the paper integration algorithm of GPS and INS systems data for pedestrians in urban area is presented. For data integration an Extended Kalman Filter (EKF) algorithm is proposed. Complementary characteristics of GPS and INS with EKF can overcome the problem of huge INS drifts, GPS outages, dense multipath effect and other individual problems associated with these sensors.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2013, 7, 3; 401-406
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Parameter identification of ship maneuvering models using recursive least square method based on support vector machines
    Autorzy:
    Zhu, M.
    Hahn, A.
    Wen, Y.
    Bolles, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/116455.pdf
    Data publikacji:
    2017
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    ship manoeuvering
    recursive least square method
    ship manoeuvering model
    ship maneuverability prediction
    Support Vector Machines (SVM)
    empirical mode decomposition (EMD)
    Computational Fluid Dynamics (CFD)
    Extended Kalman Filter (EKF)
    Opis:
    Determination of ship maneuvering models is a tough task of ship maneuverability prediction. Among several prime approaches of estimating ship maneuvering models, system identification combined with the full-scale or free- running model test is preferred. In this contribution, real-time system identification programs using recursive identification method, such as the recursive least square method (RLS), are exerted for on-line identification of ship maneuvering models. However, this method seriously depends on the objects of study and initial values of identified parameters. To overcome this, an intelligent technology, i.e., support vector machines (SVM), is firstly used to estimate initial values of the identified parameters with finite samples. As real measured motion data of the Mariner class ship always involve noise from sensors and external disturbances, the zigzag simulation test data include a substantial quantity of Gaussian white noise. Wavelet method and empirical mode decomposition (EMD) are used to filter the data corrupted by noise, respectively. The choice of the sample number for SVM to decide initial values of identified parameters is extensively discussed and analyzed. With de-noised motion data as input-output training samples, parameters of ship maneuvering models are estimated using RLS and SVM-RLS, respectively. The comparison between identification results and true values of parameters demonstrates that both the identified ship maneuvering models from RLS and SVM-RLS have reasonable agreements with simulated motions of the ship, and the increment of the sample for SVM positively affects the identification results. Furthermore, SVM-RLS using data de-noised by EMD shows the highest accuracy and best convergence.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2017, 11, 1; 23-29
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Study of the effectiveness of different Kalman filtering methods and smoothers in object tracking based on simulation tests
    Autorzy:
    Malinowski, M.
    Kwiecień, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/106773.pdf
    Data publikacji:
    2014
    Wydawca:
    Politechnika Warszawska. Wydział Geodezji i Kartografii
    Tematy:
    Kalman filtering
    smoother
    extended Kalman filter
    derivative-free filtering
    Central Difference Kalman Filter
    unscented Kalman filter
    object tracing
    filtr Kalmana
    filtracja
    rozszerzony filtr Kalmana
    EKF
    bezśladowy filtr Kalmana
    UKF
    śledzenie obiektu
    Opis:
    In navigation practice, there are various navigational architecture and integration strategies of measuring instruments that affect the choice of the Kalman filtering algorithm. The analysis of different methods of Kalman filtration and associated smoothers applied in object tracing was made on the grounds of simulation tests of algorithms designed and presented in this paper. EKF (Extended Kalman Filter) filter based on approximation with (jacobians) partial derivations and derivative-free filters like UKF (Unscented Kalman Filter) and CDKF (Central Difference Kalman Filter) were implemented in comparison. For each method of filtration, appropriate smoothers EKS (Extended Kalman Smoother), UKS (Unscented Kalman Smoother) and CDKS (Central Difference Kalman Smoother) were presented as well. Algorithms performance is discussed on the theoretical base and simulation results of two cases are presented.
    Źródło:
    Reports on Geodesy and Geoinformatics; 2014, 97; 1-22
    2391-8365
    2391-8152
    Pojawia się w:
    Reports on Geodesy and Geoinformatics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-9 z 9

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