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Journal articles on the topic 'Multiplicative extended Kalman filter'

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Published: 4 June 2021

Last updated: 6 February 2022

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Ghanbarpourasl, Habib. "A new robust quaternion-based initial alignment algorithm for stationary strapdown inertial navigation systems." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 234, no. 12 (April 27, 2020): 1913–25. http://dx.doi.org/10.1177/0954410020920473.

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A new robust quaternion Kalman filter is developed for accurate alignment of stationary strapdown inertial navigation system. Most fine alignment algorithms have tried to estimate the biases of gyroscopes and accelerometers to reduce the errors of the alignment process. In stationary platforms, due to fixed inputs for sensors, the summation of various errors such as fixed bias, misalignment, scale factor, and nonlinear errors acts like one bias error, and then the identification of each error will be impossible. The observability of gyros and accelerometers’ biases has also been studied. But, nowadays, we know that all of these unknown parameters are not observable. Then this problem can increase the complication of the alignment algorithm. The accelerometers’ errors mainly affect the errors of the roll and pitch angles, but a big portion of the heading’s error results from the gyroscopes’ errors. Modeling of all errors as additional states without considering the observability parameters has no benefits, but will increase the filter’s dimension, so the filter’s performance will decrease. In this study, due to the observability problem, a new robust multiplicative quaternion Kalman filter is designed for the alignment of a stationary platform. The presented algorithm does not estimate the sensors’ errors, but it is robust to uncertainty in the sensors’ errors. In the proposed scheme, the bounds of parameters’ errors are introduced to filter, and the filter tries to remain robust with respect to these uncertainties. The method uses the benefits of quaternions in attitude modeling, and then the robust filter is adapted to work with quaternions. The ability of the new algorithm is evaluated with MATLAB simulations. The outcomes show that the presented algorithm is more accurate than other traditional methods. The extended Kalman filter with accelerometers’ outputs and the horizontal velocities as the measurement equations and additive quaternion Kalman filter are used for comparisons.

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12

Houtekamer, P. L., and Fuqing Zhang. "Review of the Ensemble Kalman Filter for Atmospheric Data Assimilation." Monthly Weather Review 144, no. 12 (November 1, 2016): 4489–532. http://dx.doi.org/10.1175/mwr-d-15-0440.1.

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Abstract This paper reviews the development of the ensemble Kalman filter (EnKF) for atmospheric data assimilation. Particular attention is devoted to recent advances and current challenges. The distinguishing properties of three well-established variations of the EnKF algorithm are first discussed. Given the limited size of the ensemble and the unavoidable existence of errors whose origin is unknown (i.e., system error), various approaches to localizing the impact of observations and to accounting for these errors have been proposed. However, challenges remain; for example, with regard to localization of multiscale phenomena (both in time and space). For the EnKF in general, but higher-resolution applications in particular, it is desirable to use a short assimilation window. This motivates a focus on approaches for maintaining balance during the EnKF update. Also discussed are limited-area EnKF systems, in particular with regard to the assimilation of radar data and applications to tracking severe storms and tropical cyclones. It seems that relatively less attention has been paid to optimizing EnKF assimilation of satellite radiance observations, the growing volume of which has been instrumental in improving global weather predictions. There is also a tendency at various centers to investigate and implement hybrid systems that take advantage of both the ensemble and the variational data assimilation approaches; this poses additional challenges and it is not clear how it will evolve. It is concluded that, despite more than 10 years of operational experience, there are still many unresolved issues that could benefit from further research. Contents Introduction...4490 Popular flavors of the EnKF algorithm...4491 General description...4491 Stochastic and deterministic filters...4492 The stochastic filter...4492 The deterministic filter...4492 Sequential or local filters...4493 Sequential ensemble Kalman filters...4493 The local ensemble transform Kalman filter...4494 Extended state vector...4494 Issues for the development of algorithms...4495 Use of small ensembles...4495 Monte Carlo methods...4495 Validation of reliability...4497 Use of group filters with no inbreeding...4498 Sampling error due to limited ensemble size: The rank problem...4498 Covariance localization...4499 Localization in the sequential filter...4499 Localization in the LETKF...4499 Issues with localization...4500 Summary...4501 Methods to increase ensemble spread...4501 Covariance inflation...4501 Additive inflation...4501 Multiplicative inflation...4502 Relaxation to prior ensemble information...4502 Issues with inflation...4503 Diffusion and truncation...4503 Error in physical parameterizations...4504 Physical tendency perturbations...4504 Multimodel, multiphysics, and multiparameter approaches...4505 Future directions...4505 Realism of error sources...4506 Balance and length of the assimilation window...4506 The need for balancing methods...4506 Time-filtering methods...4506 Toward shorter assimilation windows...4507 Reduction of sources of imbalance...4507 Regional data assimilation...4508 Boundary conditions and consistency across multiple domains...4509 Initialization of the starting ensemble...4510 Preprocessing steps for radar observations...4510 Use of radar observations for convective-scale analyses...4511 Use of radar observations for tropical cyclone analyses...4511 Other issues with respect to LAM data assimilation...4511 The assimilation of satellite observations...4512 Covariance localization...4512 Data density...4513 Bias-correction procedures...4513 Impact of covariance cycling...4514 Assumptions regarding observational error...4514 Recommendations regarding satellite observations...4515 Computational aspects...4515 Parameters with an impact on quality...4515 Overview of current parallel algorithms...4516 Evolution of computer architecture...4516 Practical issues...4517 Approaching the gray zone...4518 Summary...4518 Hybrids with variational and EnKF components...4519 Hybrid background error covariances...4519 E4DVar with the α control variable...4519 Not using linearized models with 4DEnVar...4520 The hybrid gain algorithm...4521 Open issues and recommendations...4521 Summary and discussion...4521 Stochastic or deterministic filters...4522 The nature of system error...4522 Going beyond the synoptic scales...4522 Satellite observations...4523 Hybrid systems...4523 Future of the EnKF...4523 APPENDIX A...4524 Types of Filter Divergence...4524 Classical filter divergence...4524 Catastrophic filter divergence...4524 APPENDIX B...4524 Systems Available for Download...4524 References...4525

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13

Bocquet, M., P. N. Raanes, and A. Hannart. "Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation." Nonlinear Processes in Geophysics 22, no. 6 (November 3, 2015): 645–62. http://dx.doi.org/10.5194/npg-22-645-2015.

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Abstract. The ensemble Kalman filter (EnKF) is a powerful data assimilation method meant for high-dimensional nonlinear systems. But its implementation requires somewhat ad hoc procedures such as localization and inflation. The recently developed finite-size ensemble Kalman filter (EnKF-N) does not require multiplicative inflation meant to counteract sampling errors. Aside from the practical interest in avoiding the tuning of inflation in perfect model data assimilation experiments, it also offers theoretical insights and a unique perspective on the EnKF. Here, we revisit, clarify and correct several key points of the EnKF-N derivation. This simplifies the use of the method, and expands its validity. The EnKF is shown to not only rely on the observations and the forecast ensemble, but also on an implicit prior assumption, termed hyperprior, that fills in the gap of missing information. In the EnKF-N framework, this assumption is made explicit through a Bayesian hierarchy. This hyperprior has so far been chosen to be the uninformative Jeffreys prior. Here, this choice is revisited to improve the performance of the EnKF-N in the regime where the analysis is strongly dominated by the prior. Moreover, it is shown that the EnKF-N can be extended with a normal-inverse Wishart informative hyperprior that introduces additional information on error statistics. This can be identified as a hybrid EnKF–3D-Var counterpart to the EnKF-N.

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14

Bocquet, M., P. N. Raanes, and A. Hannart. "Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation." Nonlinear Processes in Geophysics Discussions 2, no. 4 (July 24, 2015): 1091–136. http://dx.doi.org/10.5194/npgd-2-1091-2015.

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Abstract. The ensemble Kalman filter (EnKF) is a powerful data assimilation method meant for high-dimensional nonlinear systems. But its implementation requires fixes such as localization and inflation. The recently developed finite-size ensemble Kalman filter (EnKF-N) does not require multiplicative inflation meant to counteract sampling errors. Aside from the practical interest of avoiding the tuning of inflation in perfect model data assimilation experiments, it also offers theoretical insights and a unique perspective on the EnKF. Here, we revisit, clarify and correct several key points of the EnKF-N derivation. This simplifies the use of the method, and expands its validity. The EnKF is shown to not only rely on the observations and the forecast ensemble but also on an implicit prior assumption, termed hyperprior, that fills in the gap of missing information. In the EnKF-N framework, this assumption is made explicit through a Bayesian hierarchy. This hyperprior has been so far chosen to be the uninformative Jeffreys' prior. Here, this choice is revisited to improve the performance of the EnKF-N in the regime where the analysis strongly relaxes to the prior. Moreover, it is shown that the EnKF-N can be extended with a normal-inverse-Wishart informative hyperprior that additionally introduces climatological error statistics. This can be identified as a hybrid 3D-Var/EnKF counterpart to the EnKF-N.

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15

Harlim, John, and Andrew J. Majda. "Filtering Turbulent Sparsely Observed Geophysical Flows." Monthly Weather Review 138, no. 4 (April 1, 2010): 1050–83. http://dx.doi.org/10.1175/2009mwr3113.1.

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Abstract Filtering sparsely turbulent signals from nature is a central problem of contemporary data assimilation. Here, sparsely observed turbulent signals from nature are generated by solutions of two-layer quasigeostrophic models with turbulent cascades from baroclinic instability in two separate regimes with varying Rossby radius mimicking the atmosphere and the ocean. In the “atmospheric” case, large-scale turbulent fluctuations are dominated by barotropic zonal jets with non-Gaussian statistics while the “oceanic” case has large-scale blocking regime transitions with barotropic zonal jets and large-scale Rossby waves. Recently introduced, cheap radical linear stochastic filtering algorithms utilizing mean stochastic models (MSM1, MSM2) that have judicious model errors are developed here as well as a very recent cheap stochastic parameterization extended Kalman filter (SPEKF), which includes stochastic parameterization of additive and multiplicative bias corrections “on the fly.” These cheap stochastic reduced filters as well as a local least squares ensemble adjustment Kalman filter (LLS-EAKF) are compared on the test bed with 36 sparse regularly spaced observations for their skill in recovering turbulent spectra, spatial pattern correlations, and RMS errors. Of these four algorithms, the cheap SPEKF algorithm has the superior overall skill on the stringent test bed, comparable to LLS-EAKF in the atmospheric regime with and without model error and far superior to LLS-EAKF in the ocean regime. LLS-EAKF has special difficulty and high computational cost in the ocean regime with small Rossby radius, which creates stiffness in the perfect dynamics. The even cheaper mean stochastic model, MSM1, has high skill, which is comparable to SPEKF for the oceanic case while MSM2 has significantly worse filtering performance than MSM1 with the same inexpensive computational cost. This is interesting because MSM1 is based on a simple new regression strategy while MSM2 relies on the conventional regression strategy used in stochastic models for shear turbulence.

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Ma, Haining, Zhengliang Lu, Xiang Zhang, Wenhe Liao, and Klaus Briess. "High-Accuracy and Low-Cost Attitude Measurement Unit of the CubeSat." International Journal of Aerospace Engineering 2020 (August 28, 2020): 1–13. http://dx.doi.org/10.1155/2020/4973970.

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This paper proposes high-accuracy and reliable attitude measurement methods exclusive for CubeSat with restrictions of low cost, limited space, and low power consumption. The attitude measurement unit is equipped with Commercial Off-The-Shelf (COTS) components including Micro-Electro-Mechanical System (MEMS) gyro and two simultaneously operating star trackers (STR) to enhance the measurement accuracy. The Multiplicative Extended Kalman Filter (MEKF) is used to estimate the attitude of CubeSat, and four kinds of attitude estimation layouts are put forward according to the idea of weighted average of two quaternions from two STR and different architectures of information fusion. Using the proposed methods, the attitude measurement unit can continuously provide accurate and reliable attitude knowledge for attitude control unit when the CubeSat is running in orbit. Numerical simulation is performed to verify the effectiveness of the proposed methods, and it offers a reference for CubeSat developers from the perspective of engineering application.

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17

Germani, A., C. Manes, and P. Palumbo. "Polynomial extended Kalman filter." IEEE Transactions on Automatic Control 50, no. 12 (December 2005): 2059–64. http://dx.doi.org/10.1109/tac.2005.860256.

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18

Mohammaddadi, Gh, N. Pariz, and A. Karimpour. "Extended modal Kalman filter." International Journal of Dynamics and Control 7, no. 3 (February 15, 2019): 981–95. http://dx.doi.org/10.1007/s40435-019-00519-8.

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19

Stovner, Bård Nagy, Tor Arne Johansen, Thor I. Fossen, and Ingrid Schjølberg. "Attitude estimation by multiplicative exogenous Kalman filter." Automatica 95 (September 2018): 347–55. http://dx.doi.org/10.1016/j.automatica.2018.05.038.

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20

Ainscough, Thomas, Renato Zanetti, John Christian, and Pol D. Spanos. "Q-Method Extended Kalman Filter." Journal of Guidance, Control, and Dynamics 38, no. 4 (April 2015): 752–60. http://dx.doi.org/10.2514/1.g000118.

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21

Psiaki, Mark L. "Backward-Smoothing Extended Kalman Filter." Journal of Guidance, Control, and Dynamics 28, no. 5 (September 2005): 885–94. http://dx.doi.org/10.2514/1.12108.

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22

Glielmo, L., R. Setola, and F. Vasca. "An interlaced extended Kalman filter." IEEE Transactions on Automatic Control 44, no. 8 (1999): 1546–49. http://dx.doi.org/10.1109/9.780418.

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23

Emzir, Muhammad F., Matthew J. Woolley, and Ian R. Petersen. "A quantum extended Kalman filter." Journal of Physics A: Mathematical and Theoretical 50, no. 22 (May 9, 2017): 225301. http://dx.doi.org/10.1088/1751-8121/aa6e5e.

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24

Zanetti, Renato, and Kyle J. DeMars. "Fully Multiplicative Unscented Kalman Filter for Attitude Estimation." Journal of Guidance, Control, and Dynamics 41, no. 5 (May 2018): 1183–89. http://dx.doi.org/10.2514/1.g003221.

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25

He, Lina, Hairui Zhou, and Gongyuan Zhang. "Improving extended Kalman filter algorithm in satellite autonomous navigation." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 231, no. 4 (August 6, 2016): 743–59. http://dx.doi.org/10.1177/0954410016641708.

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With the goal of reducing dependence on ground tracking systems, satellite autonomous navigation technologies are developed quickly in the recent several decades. However, precise orbit determination at high orbital altitudes is an important and challenging problem. In this paper, the nonlinear real-time orbit determination problem is investigated. Combined with satellite dynamical model, extended Kalman filter is explored to estimate satellite orbit parameters. Further, considering errors occur in linearization processing, two improvements for the extended Kalman filter algorithm, i.e. extended Kalman filter-I and extended Kalman filter-II, are proposed based on Lagrange’s mean value theorem, and respectively focus on choosing better linear expansion point and Jacobian matrix calculation point. Extensive simulations show that extended Kalman filter-I and extended Kalman filter-II significantly enhance orbit accuracy, compared with extended Kalman filter. And the increases in calculation complexity are acceptable. Finally, the robustness of extended Kalman filter-I and extended Kalman filter-II is analyzed by given different initial position errors, and results show that extended Kalman filter-I and extended Kalman filter-II have better robustness than extended Kalman filter.

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Syarifuddin, Agus N. A., Dian A. Merdekawati, and Erna Apriliani. "Perbandingan Metode Kalman Filter, Extended Kalman Filter, dan Ensambel Kalman Filter pada Model penyebaran virus HIV/AIDS." Limits: Journal of Mathematics and Its Applications 15, no. 1 (March 27, 2018): 17. http://dx.doi.org/10.12962/limits.v15i1.3344.

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Ullah, Inam, Xin Su, Xuewu Zhang, and Dongmin Choi. "Simultaneous Localization and Mapping Based on Kalman Filter and Extended Kalman Filter." Wireless Communications and Mobile Computing 2020 (June 8, 2020): 1–12. http://dx.doi.org/10.1155/2020/2138643.

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For more than two decades, the issue of simultaneous localization and mapping (SLAM) has gained more attention from researchers and remains an influential topic in robotics. Currently, various algorithms of the mobile robot SLAM have been investigated. However, the probability-based mobile robot SLAM algorithm is often used in the unknown environment. In this paper, the authors proposed two main algorithms of localization. First is the linear Kalman Filter (KF) SLAM, which consists of five phases, such as (a) motionless robot with absolute measurement, (b) moving vehicle with absolute measurement, (c) motionless robot with relative measurement, (d) moving vehicle with relative measurement, and (e) moving vehicle with relative measurement while the robot location is not detected. The second localization algorithm is the SLAM with the Extended Kalman Filter (EKF). Finally, the proposed SLAM algorithms are tested by simulations to be efficient and viable. The simulation results show that the presented SLAM approaches can accurately locate the landmark and mobile robot.

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Park, Sung-Yong, Jong-Hun Park, Hai-Yun Wang, Jin-Hong No, and Uk-Youl Huh. "Localization using Fuzzy-Extended Kalman Filter." Transactions of The Korean Institute of Electrical Engineers 63, no. 2 (February 1, 2014): 277–83. http://dx.doi.org/10.5370/kiee.2014.63.2.277.

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29

Tsai, J. S. H. "Extended-Kalman-filter-based chaotic communication." IMA Journal of Mathematical Control and Information 22, no. 1 (March 1, 2005): 58–79. http://dx.doi.org/10.1093/imamci/dni005.

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30

Jiang, Yanguang, Yi Huang, Wenchao Xue, and Haitao Fang. "On designing consistent extended Kalman filter." Journal of Systems Science and Complexity 30, no. 4 (May 2, 2017): 751–64. http://dx.doi.org/10.1007/s11424-017-5151-7.

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31

Kumar, Kundan, Shovan Bhaumik, and Paresh Date. "Extended Kalman Filter Using Orthogonal Polynomials." IEEE Access 9 (2021): 59675–91. http://dx.doi.org/10.1109/access.2021.3073289.

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32

Li, Wenling, Yingmin Jia, and Junping Du. "Tobit Kalman filter with time-correlated multiplicative measurement noise." IET Control Theory & Applications 11, no. 1 (January 6, 2017): 122–28. http://dx.doi.org/10.1049/iet-cta.2016.0624.

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33

Qiu, Zhenbing, and Huaming Qian. "Modified multiplicative quaternion cubature Kalman filter for attitude estimation." International Journal of Adaptive Control and Signal Processing 32, no. 8 (June 22, 2018): 1182–90. http://dx.doi.org/10.1002/acs.2895.

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34

Ullah, Inam, Xin Su, Jinxiu Zhu, Xuewu Zhang, Dongmin Choi, and Zhenguo Hou. "Evaluation of Localization by Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter-Based Techniques." Wireless Communications and Mobile Computing 2020 (October 2, 2020): 1–15. http://dx.doi.org/10.1155/2020/8898672.

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Mobile robot localization has attracted substantial consideration from the scientists during the last two decades. Mobile robot localization is the basics of successful navigation in a mobile network. Localization plays a key role to attain a high accuracy in mobile robot localization and robustness in vehicular localization. For this purpose, a mobile robot localization technique is evaluated to accomplish a high accuracy. This paper provides the performance evaluation of three localization techniques named Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF). In this work, three localization techniques are proposed. The performance of these three localization techniques is evaluated and analyzed while considering various aspects of localization. These aspects include localization coverage, time consumption, and velocity. The abovementioned localization techniques present a good accuracy and sound performance compared to other techniques.

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Wang, Yuyan, Xiuyun Meng, and Jilu Liu. "An Improved Adaptive Extended Kalman Filter Algorithm of SINS/GPS Loosely-Coupled Integrated Navigation System." International Journal of Engineering & Technology 7, no. 4.27 (November 30, 2018): 87. http://dx.doi.org/10.14419/ijet.v7i4.27.22488.

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The Kalman Filter algorithm usually cannot estimate noise statistics in real-time, in order to deal with this issue, a new kind of improved Adaptive Extended Kalman Filter algorithm is proposed. Based on residual sequence, this algorithm mainly improves the adaptive estimator of the filter algorithm, which can estimate measurement noise in real-time. Furthermore, this new filter algorithm is applied to a SINS/GPS loosely-coupled integrated navigation system, which can automatically adjust the covariance matrix of measurement noise as noise varies in the system. Finally, the original Extended Kalman Filter and the improved Adaptive Extended Kalman Filter are applied respectively to simulate for the SINS/GPS loosely-coupled model. Tests demonstrate that, the improved Adaptive Extended Kalman Filter reduces both position error and velocity error compared with the original Extended Kalman Filter.

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36

Sheng Zheng. "Tracking refractivity from radar clutter using extended Kalman filter and unscented Kalman filter." Acta Physica Sinica 60, no. 11 (2011): 119301. http://dx.doi.org/10.7498/aps.60.119301.

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Na, Wooyoung, and Chulsang Yoo. "Real-Time Parameter Estimation of a Dual-Pol Radar Rain Rate Estimator Using the Extended Kalman Filter." Remote Sensing 13, no. 12 (June 17, 2021): 2365. http://dx.doi.org/10.3390/rs13122365.

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The extended Kalman filter is an extended version of the Kalman filter for a non-linear problem. This study applies this extended Kalman filter to the real-time estimation of the parameters of the dual-pol radar rain rate estimator. The estimated parameters are also compared with those based on the least squares method. As an application example, this study considers four storm events observed by the Beaslesan radar in Korea. The findings derived include, first, that the parameters of the radar rain rate estimator obtained by the extended Kalman filter are totally different from those by the least squares method. In fact, the parameters obtained by the extended Kalman filter are found to be more reasonable, and are similar to those reported in previous studies. Second, the estimated rain rates based on the parameters obtained by the extended Kalman filter are found to be similar to those observed on the ground. Even though the parameters estimated by applying the least squares method are quite different from previous studies as well as those based on the extended Kalman filter, the resulting radar rain rate is found to be quite similar to that based on the extended Kalman filter. In conclusion, the extended Kalman filter can be a reliable method for real-time estimation of the parameters of the dual-pol radar rain rate estimator. The resulting rain rate is also found to be of sufficiently high quality to be applicable for other purposes, such as various flood warning systems.

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Liu, Changyun, Penglang Shui, and Song Li. "Unscented extended Kalman filter for target tracking." Journal of Systems Engineering and Electronics 22, no. 2 (April 2011): 188–92. http://dx.doi.org/10.3969/j.issn.1004-4132.2011.02.002.

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Gao, Kai, and David Day‐Uei Li. "Estimating fluorescence lifetimes using extended Kalman filter." Electronics Letters 53, no. 15 (July 2017): 1027–29. http://dx.doi.org/10.1049/el.2017.1085.

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Fisher, James L., David P. Casasent, and Charles P. Neuman. "Factorized extended Kalman filter: case study results." Applied Optics 27, no. 9 (May 1, 1988): 1877. http://dx.doi.org/10.1364/ao.27.001877.

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Fisher, James L., David P. Casasent, and Charles P. Neuman. "Factorized extended Kalman filter for optical processing." Applied Optics 25, no. 10 (May 15, 1986): 1615. http://dx.doi.org/10.1364/ao.25.001615.

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Peng, Gu, Wang Kai, and Zhang Shicang. "A Method of Stable Extended Kalman filter." IOP Conference Series: Materials Science and Engineering 569 (August 9, 2019): 032018. http://dx.doi.org/10.1088/1757-899x/569/3/032018.

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43

Kumar, K., D. Yadav, and B. V. Srinivas. "Adaptive noise models for extended Kalman filter." Journal of Guidance, Control, and Dynamics 14, no. 2 (March 1991): 475–77. http://dx.doi.org/10.2514/3.20665.

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Rajeswari, K., and Anjali. "Extended Kalman Filter for Vehicle Suspension System." Applied Mechanics and Materials 573 (June 2014): 317–21. http://dx.doi.org/10.4028/www.scientific.net/amm.573.317.

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This paper presents an estimator for a nonlinear active suspension system considering the hydraulic actuator dynamics. PID controller is used to control the Active suspension system of nonlinear quarter car model. Extended Kalman filter is designed to estimate the states from the measurement model perturbed with noise. Simulation results demonstrate the effectiveness of the PID based active suspension system in reducing the vertical acceleration transmitted to the passengers thereby improving the ride comfort. Also the effectiveness of the Extended Kalman filter in estimating the actual vehicle states is demonstrated.

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Honda, T., S. Takahashi, H. Takauji, and S. Kaneko. "Vision-Based Tracking with Extended Kalman Filter." IFAC Proceedings Volumes 44, no. 1 (January 2011): 9644–49. http://dx.doi.org/10.3182/20110828-6-it-1002.02664.

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Inoue, Roberto Santos, João Paulo Cerri, and Marco Henrique Terra. "Extended robust Kalman filter for attitude estimation." IET Control Theory & Applications 10, no. 2 (January 19, 2016): 162–72. http://dx.doi.org/10.1049/iet-cta.2015.0235.

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Ferrero, Roberto, Paolo Attilio Pegoraro, and Sergio Toscani. "Dynamic Synchrophasor Estimation by Extended Kalman Filter." IEEE Transactions on Instrumentation and Measurement 69, no. 7 (July 2020): 4818–26. http://dx.doi.org/10.1109/tim.2019.2955797.

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Cortina, E., D. Otero, and C. E. D'Attellis. "Maneuvering target tracking using extended Kalman filter." IEEE Transactions on Aerospace and Electronic Systems 27, no. 1 (1991): 155–58. http://dx.doi.org/10.1109/7.68158.

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Chen, B. C., and F. C. Hsieh. "Sideslip angle estimation using extended Kalman filter." Vehicle System Dynamics 46, sup1 (September 2008): 353–64. http://dx.doi.org/10.1080/00423110801958550.

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Choi, Hongjun, Mingi Kim, and Onseok Lee. "An extended Kalman filter for mouse tracking." Medical & Biological Engineering & Computing 56, no. 11 (May 19, 2018): 2109–23. http://dx.doi.org/10.1007/s11517-018-1805-4.

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