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Academic literature on the topic 'Multiplicative extended Kalman filter'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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Journal articles on the topic "Multiplicative extended Kalman filter"

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Leishman, Robert C., and Timothy W. McLain. "Multiplicative Extended Kalman Filter for Relative Rotorcraft Navigation." Journal of Aerospace Information Systems 12, no. 12 (December 2015): 728–44. http://dx.doi.org/10.2514/1.i010236.

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Koch, Daniel P., David O. Wheeler, Randal W. Beard, Timothy W. McLain, and Kevin M. Brink. "Relative multiplicative extended Kalman filter for observable GPS-denied navigation." International Journal of Robotics Research 39, no. 9 (June 23, 2020): 1085–121. http://dx.doi.org/10.1177/0278364920903094.

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This work presents a multiplicative extended Kalman filter (MEKF) for estimating the relative state of a multirotor vehicle operating in a GPS-denied environment. The filter fuses data from an inertial measurement unit and altimeter with relative-pose updates from a keyframe-based visual odometry or laser scan-matching algorithm. Because the global position and heading states of the vehicle are unobservable in the absence of global measurements such as GPS, the filter in this article estimates the state with respect to a local frame that is colocated with the odometry keyframe. As a result, the odometry update provides nearly direct measurements of the relative vehicle pose, making those states observable. Recent publications have rigorously documented the theoretical advantages of such an observable parameterization, including improved consistency, accuracy, and system robustness, and have demonstrated the effectiveness of such an approach during prolonged multirotor flight tests. This article complements this prior work by providing a complete, self-contained, tutorial derivation of the relative MEKF, which has been thoroughly motivated but only briefly described to date. This article presents several improvements and extensions to the filter while clearly defining all quaternion conventions and properties used, including several new useful properties relating to error quaternions and their Euler-angle decomposition. Finally, this article derives the filter both for traditional dynamics defined with respect to an inertial frame, and for robocentric dynamics defined with respect to the vehicle’s body frame, and provides insights into the subtle differences that arise between the two formulations.

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Chang, Lubin, Baiqing Hu, and Kailong Li. "Iterated multiplicative extended kalman filter for attitude estimation using vector observations." IEEE Transactions on Aerospace and Electronic Systems 52, no. 4 (August 2016): 2053–60. http://dx.doi.org/10.1109/taes.2016.150237.

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Chen, Pengpeng, Honglu Ma, Shouwan Gao, and Yan Huang. "Modified Extended Kalman Filtering for Tracking with Insufficient and Intermittent Observations." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/981727.

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This paper is concerned with the Kalman filtering problem for tracking a single target on the fixed-topology wireless sensor networks (WSNs). Both the insufficient anchor coverage and the packet dropouts have been taken into consideration in the filter design. The resulting tracking system is modeled as a multichannel nonlinear system with multiplicative noise. Noting that the channels may be correlated with each other, we use a general matrix to express the multiplicative noise. Then, a modified extended Kalman filtering algorithm is presented based on the obtained model to achieve high tracking accuracy. In particular, we evaluate the effect of various parameters on the tracking performance through simulation studies.

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Qin, Fangjun, Lubin Chang, Sai Jiang, and Feng Zha. "A Sequential Multiplicative Extended Kalman Filter for Attitude Estimation Using Vector Observations." Sensors 18, no. 5 (May 3, 2018): 1414. http://dx.doi.org/10.3390/s18051414.

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Vellayikot, Shijoh, and M. V. Vaidyan. "ANN Approach for State Estimation of Hybrid Systems and Its Experimental Validation." Mathematical Problems in Engineering 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/382324.

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A novel artificial neural network based state estimator has been proposed to ensure the robustness in the state estimation of autonomous switching hybrid systems under various uncertainties. Taking the autonomous switching three-tank system as benchmark hybrid model working under various additive and multiplicative uncertainties such as process noise, measurement error, process–model parameter variation, initial state mismatch, and hand valve faults, real-time performance evaluation by the comparison of it with other state estimators such as extended Kalman filter and unscented Kalman Filter was carried out. The experimental results reported with the proposed approach show considerable improvement in the robustness in performance under the considered uncertainties.

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Hua-ming, Qian, Qian Lin-chen, Shen Chen, and Huang Wei. "Robust extended Kalman filter for attitude estimation with multiplicative noises and unknown external disturbances." IET Control Theory & Applications 8, no. 15 (October 16, 2014): 1523–36. http://dx.doi.org/10.1049/iet-cta.2014.0293.

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Battiston, Adrian, Inna Sharf, and Meyer Nahon. "Attitude estimation for collision recovery of a quadcopter unmanned aerial vehicle." International Journal of Robotics Research 38, no. 10-11 (August 8, 2019): 1286–306. http://dx.doi.org/10.1177/0278364919867397.

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An extensive evaluation of attitude estimation algorithms in simulation and experiments is performed to determine their suitability for a collision recovery pipeline of a quadcopter unmanned aerial vehicle. A multiplicative extended Kalman filter (MEKF), unscented Kalman filter (UKF), complementary filter, [Formula: see text] filter, and novel adaptive varieties of the selected filters are compared. The experimental quadcopter uses a PixHawk flight controller, and the algorithms are implemented using data from only the PixHawk inertial measurement unit (IMU). Performance of the aforementioned filters is first evaluated in a simulation environment using modified sensor models to capture the effects of collision on inertial measurements. Simulation results help define the efficacy and use cases of the conventional and novel algorithms in a quadcopter collision scenario. An analogous evaluation is then conducted by post-processing logged sensor data from collision flight tests, to gain new insights into algorithms’ performance in the transition from simulated to real data. The post-processing evaluation compares each algorithm’s attitude estimate, including the stock attitude estimator of the PixHawk controller, to data collected by an offboard infrared motion capture system. Based on this evaluation, two promising algorithms, the MEKF and an adaptive [Formula: see text] filter, are selected for implementation on the physical quadcopter in the control loop of the collision recovery pipeline. Experimental results show an improvement in the metric used to evaluate experimental performance, the time taken to recover from the collision, when compared with the stock attitude estimator on the PixHawk (PX4) software.

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Ghobadi, Mostafa, Puneet Singla, and Ehsan T. Esfahani. "Robust Attitude Estimation from Uncertain Observations of Inertial Sensors Using Covariance Inflated Multiplicative Extended Kalman Filter." IEEE Transactions on Instrumentation and Measurement 67, no. 1 (January 2018): 209–17. http://dx.doi.org/10.1109/tim.2017.2761230.

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Fan, Chunshi, and Zheng You. "Highly Efficient Sigma Point Filter for Spacecraft Attitude and Rate Estimation." Mathematical Problems in Engineering 2009 (2009): 1–23. http://dx.doi.org/10.1155/2009/507370.

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Nonlinearities in spacecraft attitude determination problem has been studied intensively during the past decades. Traditionally, multiplicative extended Kalman filter_MEKF_algorithm has been a good solution for most nominal space missions. But in recent years, advances in space missions deserve a revisit of the issue. Though there exist a variety of advanced nonlinear filtering algorithms, most of them are prohibited for actual onboard implementation because of their overload computational complexity. In this paper, we address this difficulty by developing a new algorithm framework based on the marginal filtering principle, which requires only 4 sigma points to give a complete 6-state attitude and angular rate estimation. Moreover, a new strategy for sigma point construction is also developed to further increase the efficiency and numerical accuracy. Incorporating the presented framework and novel sigma points, we proposed a new, nonlinear attitude and rate estimator, namely, the Marginal Geometric Sigma Point Filter. The new algorithm is of the same precision as traditional unscented Kalman filters, while keeping a significantly lower computational complexity, even when compared to the reduced sigma point algorithms. In fact, it has truly rivaled the efficiency of MEKF, even when simple closed-form solutions are involved in the latter.

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Dissertations / Theses on the topic "Multiplicative extended Kalman filter"

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Pellett, Andrew. "The Extended Kalman-Consensus Filter." Fogler Library, University of Maine, 2011. http://www.library.umaine.edu/theses/pdf/PellettA2011.pdf.

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Sanchez, M. Juan Jose. "Use of an Extended Kalman Filter." Thesis, Monterey, California : Naval Postgraduate School, 1990. http://handle.dtic.mil/100.2/ADA238639.

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Thesis (M.S. in Systems Engineering (Electronic Warfare))--Naval Postgraduate School, September 1990.
Thesis Advisor(s): Titus, Harold A. Second Reader: Powell, J. R. "September 1990." Description based on title screen as viewed on December 22, 2009. DTIC Descriptor(s): Kalman Filtering, Scenarios, Measurement, Detection, Bearing(Direction), Direction Finding, Algorithms, Tracking, Accuracy, Estimates, Surfaces, Maneuvers, Equations. DTIC Identifier(s): Direction Finding Equipment, Extended Kalman Filters, Electronic Support Measures, Function Plot Program, Program Listing. Author(s) subject terms: Kalman Filter, Direction Finding, ESM systems, Venezuela. Includes bibliographical references (p. 82). Also available in print.

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Back, Per. "Simulering av simulinkmodeller med Extended Kalman Filter." Thesis, KTH, Reglerteknik, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-109457.

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Simulations of simulink models using Kalman filters are often very time-consuming. This problem depends mainly on the fact that the Kalman correction has to be performed at each sample instance through the whole simulation. The goal for this thesis work is to reduce that time-consumption for the filtering part (the integration partis treated in a complementary report) of a simulation. Furthermore a Matlab routine to perform parameter tuning and finally a graphical user interface is developed. The filtering part of the simulation in this thesis is based on an Extended Kalman Filter (EKF). The time optimization of this filter considers searching for the possibility to replace the today’s existing Matlab functions that is used to perform the filtering calculations. Examples of such functions are routines for linearization and integration. To decrease the time-consumption, we have also developed a routine to make it possible to convert a simulink model to a state-space description. This conversion makes it possible to avoid a lot of time-consuming calls to the simulink model. In this case it is the built-in functions in Matlab that causes the large time-consumption. The main time-consuming parts in the filter are the built-in routines for linearization (linmod) and the numerical method that is used to calculate the prediction error (riccatiequation). By creating new routines to solve these problems, the total time-consumption for the filtering part is reduced by approximately a factor of eighteen. As a final step the time optimized Kalman filter and the time optimized integration (treated in a complementary report) are brought together in a time efficient routine for simulation. This final routine for simulation may further be used to perform a time efficient simulation, but also to form a routine, which can be used to estimate unknown parameters in a simulink model. Using the time optimized parts of the simulation routine will make it possible to reduce the execution time for a filtering simulation by approximately a factor of ten. Three kinds of models are used to confirm that the different element of the Kalman filter and the new developed routines work properly. These models consist of one fermentation system that describes a biological process, and two different tank systems that describe the level and the torrent of water in several water tanks.
Vid filtrerande simulering av simulinkmodeller är tidsåtgången i dagsläget mycket påtaglig, mest beroende på att kalmankorrigeringen måste appliceras i varje samplingspunkt. Målet med detta examensarbete är att minska tidsåtgången som för närvarande råder för den filtrerande delen (den integrerande delen av simuleringen behandlas i en komplimenterande rapport) av en simulering. Utöver detta utvecklas även en Matlab-rutin för parametersökning samt ett enkelt grafiskt användargränssnitt som underlättar användandet av utvecklade rutiner. Den filtrerande delen av simuleringen består i detta examensarbete av ett s.k. utvidgat kalmanfilter, EKF (Extended Kalman Filter). Tidsoptimeringen av detta filter bygger på att undersöka och eventuellt ersätta de inbyggda Matlabfunktioner som i dagsläget måste användas för att genomföra en sådan filtrering. Exempel på sådana är funktioner för linjarisering och integrering. För att minska tidsåtgången utvecklas även en rutin för konvertering av simulinkmodeller till en s.k. tillståndsbeskrivning. Detta medför bl.a. att tidsödande anrop till simulinkmodellen kan undvikas. De i Matlab inbyggda funktioner som i detta fall står för den största delen av den påtagliga tidsåtgången är linmod för linjarisering samt en inbyggd numerisk metod för att beräkna prediktionsfelets varians (riccati-ekvationen). Genom att skapa nya metoder för att lösa dessa problem, har tidsåtgången för att utföra den filtrerande delen av simuleringen reducerats med en faktor 18. I ett slutskede sammanfogas sedan det tidsoptimerade kalmanfiltret med en tidsoptimerad rutin för integrering (behandlas i en komplimenterande rapport) till en komplett simuleringsrutin. Denna simuleringsrutin kan sedan användas för tidseffektiva simuleringar av simulinkmodeller, men utnyttjas även som grundstomme vid utveckling av parametersökningsrutinen. Den sammanfogade simuleringsrutinen har med hjälp av de två tidsoptimerade delarna för kalmanfiltrering och integrering medfört att tidsåtgången för att genomföra en filtrerande simulering reducerats med ungefär en faktor 10. För att testa de olika momenten och de utvecklade rutinerna används tre olika modeller. Dessa modeller består av ett fermatorsystem som beskriver en biologisk tillväxtprocess samt två olika tanksystem som beskriver flöden och nivåer i det aktuella systemets vattentankar.

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Magnusson, Thom. "State Estimation of UAV using Extended Kalman Filter." Thesis, Linköpings universitet, Reglerteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-93931.

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In unmanned systems an autopilot controls the outputs of the vehicle withouthuman interference. All decisions made by the autopilot will depend on estimatesdelivered by an Inertial Navigation System, INS. For the autopilot to take correctdecisions it must rely on correct estimates of its orientation, position and velocity.Hence, higher performance of the autopilot can be achieved by improving its INS.Instrument Control Sweden AB has an autopilot developed for ﬁxed wing aircraft.The focus of this thesis has been on investigating the potential beneﬁts of usingExtended Kalman ﬁlters for estimating information required by the control systemin the autopilot. The Extended Kalman ﬁlter is used to fuse sensor measurementsfrom accelerometers, magnetometers, gyroscopes, GPS and pitot tubes. The ﬁlterwill be compared to the current Attitude and Heading Reference System, AHRS, tosee if better results can be achieved by utilizing sensor fusion.

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Pettersson, Martin. "Extended Kalman Filter for Robust UAV Attitude Estimation." Thesis, Linköpings universitet, Reglerteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119097.

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Attitude estimation of unmanned aerial vehicles is of great importance as it enables propercontrol of the vehicles. Attitude estimation is typically done by an attitude-heading refer-ence system (ahrs) which utilises different kind of sensors. In this thesis these include agyroscope providing angular rates measurements which can be integrated to describe the at-titude as well as an accelerometer and a magnetometer, both of which can be compared withknown reference vectors to determine the attitude. The sensor measurements are fused usinga gps augmented 7-state Extended Kalman filter (ekf) with a quaternion and gyroscope bi-ases as state variables. It uses differentiated gps velocity measurements to estimate externalaccelerations as reference vector to the accelerometer, which significantly raises robustnessof the solution. The filter is implemented in MatlabTM and in c on an ARM microprocessor.It is compared with an explicit complementary filter solution and is evaluated with flightsusing a fixed-wing uav with satisfactory results.

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Boizot, Nicolas. "Adaptative high-gain extended Kalman filter and applications." Phd thesis, Université de Bourgogne, 2010. http://tel.archives-ouvertes.fr/tel-00559107.

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The work concerns the "observability problem"--the reconstruction of a dynamic process's full state from a partially measured state-- for nonlinear dynamic systems. The Extended Kalman Filter (EKF) is a widely-used observer for such nonlinear systems. However it suffers from a lack of theoretical justifications and displays poor performance when the estimated state is far from the real state, e.g. due to large perturbations, a poor initial state estimate, etc. . . We propose a solution to these problems, the Adaptive High-Gain (EKF). Observability theory reveals the existence of special representations characterizing nonlinear systems having the observability property. Such representations are called observability normal forms. A EKF variant based on the usage of a single scalar parameter, combined with an observability normal form, leads to an observer, the High-Gain EKF, with improved performance when the estimated state is far from the actual state. Its convergence for any initial estimated state is proven. Unfortunately, and contrary to the EKF, this latter observer is very sensitive to measurement noise. Our observer combines the behaviors of the EKF and of the high-gain EKF. Our aim is to take advantage of both efficiency with respect to noise smoothing and reactivity to large estimation errors. In order to achieve this, the parameter that is the heart of the high-gain technique is made adaptive. Voila, the Adaptive High-Gain EKF. A measure of the quality of the estimation is needed in order to drive the adaptation. We propose such an index and prove the relevance of its usage. We provide a proof of convergence for the resulting observer, and the final algorithm is demonstrated via both simulations and a real-time implementation. Finally, extensions to multiple output and to continuous-discrete systems are given.

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Brett, Daniel J. "Orbital parameter estimation using an extended Kalman filter." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/26680.

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The problem of orbital parameter estimation using angles only observations is examined. Direction cosine measurements, obtained from satellite passage of an earth-based stationary planar radar beam, are assimilated by an extended Kalman filter to improve estimates of a classical orbital element set. Several progressively comprehensive orbital motion models are considered and compared. The relative effectiveness of these models is illustrated by applying them to actual satellite data..

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Masinjila, Ruslan. "Multirobot Localization Using Heuristically Tuned Extended Kalman Filter." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35489.

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A mobile robot needs to know its pose (position and orientation) in order to navigate and perform useful tasks. The problem of determining this pose with respect to a global or local frame is called localisation, and is a key component in providing autonomy to mobile robots. Thus, localisation answers the question Where am I? from the robot’s perspective. Localisation involving a single robot is a widely explored and documented problem in mobile robotics. The basic idea behind most documented localisation techniques involves the optimum combination of noisy and uncertain information that comes from various robot’s sensors. However, many complex robotic applications require multiple robots to work together and share information among themselves in order to successfully and efficiently accomplish certain tasks. This leads to research in collaborative localisation involving multiple robots. Several studies have shown that when multiple robots collaboratively localise themselves, the resulting accuracy in their estimated positions and orientations outperforms that of a single robot, especially in scenarios where robots do not have access to information about their surrounding environment. This thesis presents the main theme of most of the existing collaborative, multi-robot localisation solutions, and proposes an alternative or complementary solution to some of the existing challenges in multirobot localisation. Specifically, in this thesis, a heuristically tuned Extended Kalman Filter is proposed to localise a group of mobile robots. Simulations show that when certain conditions are met, the proposed tuning method significantly improves the accuracy and reliability of poses estimated by the Extended Kalman Filter. Real world experiments performed on custom-made robotic platforms validate the simulation results.

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Marins, Joõ Luʹis. "An extended Kalman filter for quaternion-based attitude estimation." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2000. http://handle.dtic.mil/100.2/ADA384973.

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Thesis (Degree in Electrical Engineer and M.S. in Electrical Engineering) Naval Postgraduate School, Sept. 2000.
Thesis advisor(s): Yun, Xiaoping; Backman, Eric R.; Hutchins, Robert G. "September 2000." Includes bibliographical references (p. 91). Also available in print.

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Marins, Joao L. "An extended Kalman filter for quaternion-based attitude estimation." Thesis, Monterey, California. Naval Postgraduate School, 2000. http://hdl.handle.net/10945/9411.

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The filter represents rotations using quaternions rather than Euler angles, which eliminates the long-standing problem of singularities associated with those angles. A process model for rigid body angular motions and angular rate measurements is defined. The process model converts angular rates into quaternion rates, which are in turn integrated to obtain quaternions. The outputs of the model are values of three-dimensional angular rates, three-dimensional linear accelerations, and three-dimensional magnetic field vector. Gauss-Newton iteration is utilized to find the best quaternion that relates the measured linear accelerations and earth magnetic field in the body coordinate frame to calculated values in the earth coordinate frame. The quaternion obtained from the optimization algorithm is used as part of the observations for the Kalman filter. As a result, the measurement equations become linear. A new approach to attitude estimation is introduced in this thesis. The computational requirements related to the extended Kalman filter developed using this approach are significantly reduced, making it possible to estimate attitude in real-time. Extensive static and dynamic simulation of the filter using Matlab proved it to be robust. Test cases included the presence of large initial errors as well as high noise levels. In all cases the filter was able to converge and accurately track attitude.

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Books on the topic "Multiplicative extended Kalman filter"

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Brett, Daniel J. Orbital parameter estimation using an extended Kalman filter. Monterey, Calif: Naval Postgraduate School, 1992.

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Civera, Javier, Andrew J. Davison, and José María Martínez Montiel. Structure from Motion using the Extended Kalman Filter. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24834-4.

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Üner, Muhittin. Frequency, amplitude, and phase tracking of nonsinusoidal signal in noise with extended Kalman filter. Monterey, Calif: Naval Postgraduate School, 1991.

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Taylor, Michael Eric. System identification and control of an Arleigh Burke Class Destroyer using an extended Kalman Filter. Springfield, Va: Available from National Technical Information Service, 2000.

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Galinis, William J. Fixed interval smoothing algorithm for an extended Kalman filter for over-the-horizon ship tracking. Monterey, California: Naval Postgraduate School, 1989.

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Civera, Javier, Andrew J. Davison, and José María Martínez Montiel. Structure from Motion using the Extended Kalman Filter. Springer, 2011.

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Civera, Javier, Andrew J. Davison, and José María Martínez Montiel. Structure from Motion using the Extended Kalman Filter. Springer, 2014.

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Structure From Motion Using The Extended Kalman Filter. Springer, 2011.

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An Extended Kalman Filter for Quaternion-Based Attitude Estimation. Storming Media, 2000.

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D, Andrisani, and Dryden Flight Research Facility, eds. Estimating short-period dynamics using an extended Kalman filter. Edwards, Calif: National Aeronautics and Space Administration, Ames Research Center, Dryden Flight Research Facility, 1990.

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Book chapters on the topic "Multiplicative extended Kalman filter"

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Chui, Charles K., and Guanrong Chen. "Extended Kalman Filter and System Identification." In Kalman Filtering, 115–37. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47612-4_8.

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Chui, Charles K., and Guanrong Chen. "Extended Kalman Filter and System Identification." In Kalman Filtering, 108–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03859-8_8.

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Chui, Charles K., and Guanrong Chen. "Extended Kalman Filter and System Identification." In Kalman Filtering, 108–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-662-02666-3_8.

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Chui, Charles K., and Guanrong Chen. "Extended Kalman Filter and System Identification." In Kalman Filtering with Real-Time Applications, 111–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-662-02508-6_8.

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Laurinec, M. "Extended Kalman Filter and Discrete Difference Filter Comparison." In Mechatronics, 321–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23244-2_40.

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Kranthi Kumar, R., R. Sandhya, R. Laxman, and A. Chandrakanth. "LOS Rate Estimation Using Extended Kalman Filter." In Learning and Analytics in Intelligent Systems, 36–43. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24318-0_5.

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Sworder, David D., and John E. Boyd. "Target Location Using the Extended Kalman Filter." In Locating, Classifying and Countering Agile Land Vehicles, 37–66. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-19431-8_2.

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Kallapur, Abhijit G., Shaaban S. Ali, and Sreenatha G. Anavatti. "Application of Extended Kalman Filter Towards UAV Identification." In Autonomous Robots and Agents, 199–207. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73424-6_23.

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Hlávka, Zdeněk, and Marek Svojik. "Application of Extended Kalman Filter to SPD Estimation." In Applied Quantitative Finance, 233–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-69179-2_11.

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Shao, Hongshuo, Dongkyun Kim, and Kwanho You. "TDOA/FDOA Geolocation with Adaptive Extended Kalman Filter." In Communications in Computer and Information Science, 226–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17625-8_23.

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Conference papers on the topic "Multiplicative extended Kalman filter"

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Seba, A., A. El Hadri, L. Benziane, and A. Benallegue. "Attitude estimation using line-based vision and multiplicative extended Kalman filter." In 2014 13th International Conference on Control, Automation, Robotics & Vision (ICARCV). IEEE, 2014. http://dx.doi.org/10.1109/icarcv.2014.7064348.

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Martin, Philippe, and Erwan Salaün. "Generalized Multiplicative Extended Kalman Filter for Aided Attitude and Heading Reference System." In AIAA Guidance, Navigation, and Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2010. http://dx.doi.org/10.2514/6.2010-8300.

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Fan, Chunshi, Ziyang Meng, and Xiaoyun Liu. "Multiplicative quaternion extended consensus Kalman filter for attitude and augmented state estimation." In 2016 35th Chinese Control Conference (CCC). IEEE, 2016. http://dx.doi.org/10.1109/chicc.2016.7554634.

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Xia, Ruican, and Hailong Pei. "Ranging-aided Aerobridge Navigation Using Dual Quaternion Based Multiplicative Extended Kalman Filter." In 2020 International Symposium on Autonomous Systems (ISAS). IEEE, 2020. http://dx.doi.org/10.1109/isas49493.2020.9378843.

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Hall, James K., Nathan B. Knoebel, and Timothy W. McLain. "Quaternion attitude estimation for miniature air vehicles using a multiplicative extended Kalman filter." In 2008 IEEE/ION Position, Location and Navigation Symposium. IEEE, 2008. http://dx.doi.org/10.1109/plans.2008.4570043.

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Wang, Yunlong, Mohsen Soltani, and Dil Muhammad Akbar Hussain. "An adaptive Multiplicative Extened Kalman Filter for attitude estimation of Marine Satellite Tracking Antenna." In OCEANS 2016 - Shanghai. IEEE, 2016. http://dx.doi.org/10.1109/oceansap.2016.7485526.

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Yang, Shishan, and Marcus Baum. "Extended Kalman filter for extended object tracking." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952985.

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Jiang, Yanguang, Wenchao Xue, Yi Huang, and Haitao Fang. "The consistent extended Kalman filter." In 2014 33rd Chinese Control Conference (CCC). IEEE, 2014. http://dx.doi.org/10.1109/chicc.2014.6896126.

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Fisher, James L., David P. Casasent, and Charles P. Neuman. "A Factorized Extended Kalman Filter." In 29th Annual Technical Symposium, edited by Keith Bromley and William J. Miceli. SPIE, 1986. http://dx.doi.org/10.1117/12.949713.

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Zhang, Hongwei, Xiaohu Zhang, Weixin Xie, and Jinliang Du. "Smoothly Constrained Extended Kalman Filter." In 2020 15th IEEE International Conference on Signal Processing (ICSP). IEEE, 2020. http://dx.doi.org/10.1109/icsp48669.2020.9320955.

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Reports on the topic "Multiplicative extended Kalman filter"

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Maley, James M. Multiplicative Quaternion Extended Kalman Filtering for Nonspinning Guided Projectiles. Fort Belvoir, VA: Defense Technical Information Center, July 2013. http://dx.doi.org/10.21236/ada588831.

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Erwin, R. S., and Dennis S. Bernstein. Spacecraft Trajectory Estimation Using a Sampled-Data Extended Kalman Filter with Range-Only Measurements. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada439012.

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AZAD, Ali, Rick J. CHALATURNYK, and Sahar MOVAGHATI. Reservoir Characterization: Application of Extended Kalman Filter and Analytical Physics-Based Proxy Models in Thermal Recovery. Cogeo@oeaw-giscience, September 2011. http://dx.doi.org/10.5242/iamg.2011.0253.

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