Kalman Filter-Based Fusion Estimation Method of Steering Feedback Torque for Steer-by-Wire Systems

As an underground metal detection technology, the electromagnetic induction (EMI) method is widely used in many cases. Therefore, the EMI detection algorithms with excellent performance are worth studying. One of the EMI detection methods in the underground metal detection is the filter method, which first obtains the secondary magnetic field data and then uses the Kalman filter (KF) and the extended Kalman filter (EKF) to estimate the parameters of metal targets. However, the traditional KF methods used in the underground metal detection have an unsatisfactory performance of the convergence as the algorithms are given a random or a fixed initial value. Here, an initial state estimation algorithm for the underground metal detection is proposed. The initial state of the target’s horizontal position is estimated by the first order central moments of the secondary field strength map. In addition, the initial state of the target’s depth is estimated by the full width at half maximum (FWHM) method. In addition, the initial state of the magnetic polarizability tensor is estimated by the least squares method. Then, these initial states are used as the initial values for KF and EKF. Finally, the position, posture and polarizability of the target are recursively calculated. A simulation platform for the underground metal detection is built in this paper. The simulation results show that the initial value estimation method proposed for the filtering algorithm has an excellent performance in the underground metal detection.

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Real-time SOC Estimation of the EKF and UKF Algorithms Under Different Working Conditions

Journal of Electrochemical Energy Conversion and Storage ◽

10.1115/1.4051254 ◽

2021 ◽

pp. 1-29

Author(s):

Xiongbin Peng ◽

Yuwu Li ◽

Wei Yang ◽

Akhil Garg

Keyword(s):

Kalman Filter ◽

Working Conditions ◽

Unscented Kalman Filter ◽

Stress Test ◽

Estimation Method ◽

Influential Factor ◽

Recursive Least Squares ◽

Maximum Deviation ◽

Soc Estimation ◽

Proposed Model

Abstract In the battery thermal management system (BMS), the state of charge (SOC) is a very influential factor, which can prevent overcharge and over-discharge of the lithium-ion battery (LIB). This paper proposed a battery modeling and online battery parameter identification method based on the Thevenin equivalent circuit model (ECM) and recursive least squares (RLS) algorithm. The proposed model proved to have high accuracy. The error between the ECM terminal voltage value and the actual value basically fluctuates between ±0.1V. The extended Kalman filter (EKF) algorithm and the unscented Kalman filter (UKF) algorithm were applied to estimate the SOC of the battery based on the proposed model. The SOC experimental results obtained under dynamic stress test (DST), federal urban driving schedule (FUDS), and US06 cycle conditions were analyzed. The maximum deviation of the SOC based on EKF was 1.4112%~2.5988%, and the maximum deviation of the SOC based on UKF was 0.3172%~0.3388%. The SOC estimation method based on UKF and RLS provides a smaller deviation and better adaptability in different working conditions, which makes it more implementable in a real-world automobile application.

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A Modified Extended Kalman Filter for a Two-Antenna GPS/INS Vehicular Navigation System

Sensors ◽

10.3390/s18113809 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3809 ◽

Cited By ~ 6

Author(s):

Yushi Hao ◽

Aigong Xu ◽

Xin Sui ◽

Yulei Wang

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Navigation System ◽

Adaptive Estimation ◽

Estimation Method ◽

Measurement Noise ◽

Model Errors ◽

State Process ◽

Stability And Accuracy ◽

The Impact

Recently, the integration of an inertial navigation system (INS) and the Global Positioning System (GPS) with a two-antenna GPS receiver has been suggested to improve the stability and accuracy in harsh environments. As is well known, the statistics of state process noise and measurement noise are critical factors to avoid numerical problems and obtain stable and accurate estimates. In this paper, a modified extended Kalman filter (EKF) is proposed by properly adapting the statistics of state process and observation noises through the innovation-based adaptive estimation (IAE) method. The impact of innovation perturbation produced by measurement outliers is found to account for positive feedback and numerical issues. Measurement noise covariance is updated based on a remodification algorithm according to measurement reliability specifications. An experimental field test was performed to demonstrate the robustness of the proposed state estimation method against dynamic model errors and measurement outliers.

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A Kalman Filter-Based Queue Length Estimation Method with Low-Penetration Mobile Sensor Data at Signalized Intersections

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198118798734 ◽

2018 ◽

Vol 2672 (45) ◽

pp. 253-264 ◽

Cited By ~ 7

Author(s):

Juyuan Yin ◽

Jian Sun ◽

Keshuang Tang

Keyword(s):

Kalman Filter ◽

Queue Length ◽

Estimation Method ◽

Mobile Internet ◽

Internet Technology ◽

Sensor Data ◽

Linear Regression Method ◽

Mobile Sensor ◽

Length Estimation ◽

Queue Lengths

Queue length estimation is of great importance for signal performance measures and signal optimization. With the development of connected vehicle technology and mobile internet technology, using mobile sensor data instead of fixed detector data to estimate queue length has become a significant research topic. This study proposes a queue length estimation method using low-penetration mobile sensor data as the only input. The proposed method is based on the combination of Kalman Filtering and shockwave theory. The critical points are identified from raw spatiotemporal points and allocated to different cycles for subsequent estimation. To apply the Kalman Filter, a state-space model with two state variables and the system noise determined by queue-forming acceleration is established, which can characterize the stochastic property of queue forming. The Kalman Filter with joining points as measurement input recursively estimates real-time queue lengths; on the other hand, queue-discharging waves are estimated with a line fitted to leaving points. By calculating the crossing point of the queue-forming wave and the queue-discharging wave of a cycle, the maximum queue length is also estimated. A case study with DiDi mobile sensor data and ground truth maximum queue lengths at Huanggang-Fuzhong intersection, Shenzhen, China, shows that the mean absolute percentage error is only 11.2%. Moreover, the sensitivity analysis shows that the proposed estimation method achieves much better performance than the classical linear regression method, especially in extremely low penetration rates.

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Simultaneous estimation of land surface scheme states and parameters using the ensemble Kalman filter: identical twin experiments

Hydrology and Earth System Sciences ◽

10.5194/hess-15-2437-2011 ◽

2011 ◽

Vol 15 (8) ◽

pp. 2437-2457 ◽

Cited By ~ 25

Author(s):

S. Nie ◽

J. Zhu ◽

Y. Luo

Keyword(s):

Parameter Estimation ◽

Soil Moisture ◽

Kalman Filter ◽

Ensemble Kalman Filter ◽

Land Surface ◽

Estimation Method ◽

State Parameter ◽

Hydraulic Parameters ◽

Constrained Estimation ◽

Estimated Parameters

Abstract. The performance of the ensemble Kalman filter (EnKF) in soil moisture assimilation applications is investigated in the context of simultaneous state-parameter estimation in the presence of uncertainties from model parameters, soil moisture initial condition and atmospheric forcing. A physically based land surface model is used for this purpose. Using a series of identical twin experiments in two kinds of initial parameter distribution (IPD) scenarios, the narrow IPD (NIPD) scenario and the wide IPD (WIPD) scenario, model-generated near surface soil moisture observations are assimilated to estimate soil moisture state and three hydraulic parameters (the saturated hydraulic conductivity, the saturated soil moisture suction and a soil texture empirical parameter) in the model. The estimation of single imperfect parameter is successful with the ensemble mean value of all three estimated parameters converging to their true values respectively in both NIPD and WIPD scenarios. Increasing the number of imperfect parameters leads to a decline in the estimation performance. A wide initial distribution of estimated parameters can produce improved simultaneous multi-parameter estimation performances compared to that of the NIPD scenario. However, when the number of estimated parameters increased to three, not all parameters were estimated successfully for both NIPD and WIPD scenarios. By introducing constraints between estimated hydraulic parameters, the performance of the constrained three-parameter estimation was successful, even if temporally sparse observations were available for assimilation. The constrained estimation method can reduce RMSE much more in soil moisture forecasting compared to the non-constrained estimation method and traditional non-parameter-estimation assimilation method. The benefit of this method in estimating all imperfect parameters simultaneously can be fully demonstrated when the corresponding non-constrained estimation method displays a relatively poor parameter estimation performance. Because all these constraints between parameters were obtained in a statistical sense, this constrained state-parameter estimation scheme is likely suitable for other land surface models even with more imperfect parameters estimated in soil moisture assimilation applications.

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Unscented Kalman filter for airship model uncertainties and wind disturbance estimation

PLoS ONE ◽

10.1371/journal.pone.0257849 ◽

2021 ◽

Vol 16 (11) ◽

pp. e0257849

Author(s):

Muhammad Wasim ◽

Ahsan Ali ◽

Mohammad Ahmad Choudhry ◽

Faisal Saleem ◽

Inam Ul Hasan Shaikh ◽

...

Keyword(s):

Kalman Filter ◽

Precision Agriculture ◽

Model Uncertainty ◽

Unscented Kalman Filter ◽

Estimation Method ◽

Wind Disturbance ◽

Model Uncertainties ◽

Nonlinear Simulation ◽

Lumped Model ◽

Disturbance Estimation

An airship is lighter than an air vehicle with enormous potential in applications such as communication, aerial inspection, border surveillance, and precision agriculture. An airship model is made up of dynamic, aerodynamic, aerostatic, and propulsive forces. However, the computation of aerodynamic forces remained a challenge. In addition to aerodynamic model deficiencies, airship mass matrix suffers from parameter variations. Moreover, due to the lighter-than-air nature, it is also susceptible to wind disturbances. These modeling issues are the key challenges in developing an efficient autonomous flight controller for an airship. This article proposes a unified estimation method for airship states, model uncertainties, and wind disturbance estimation using Unscented Kalman Filter (UKF). The proposed method is based on a lumped model uncertainty vector that unifies model uncertainties and wind disturbances in a single vector. The airship model is extended by incorporating six auxiliary state variables into the lumped model uncertainty vector. The performance of the proposed methodology is evaluated using a nonlinear simulation model of a custom-developed UETT airship and is validated by conducting a kind of error analysis. For comparative studies, EKF estimator is also developed. The results show the performance superiority of the proposed estimator over EKF; however, the proposed estimator is a bit expensive on computational grounds. However, as per the requirements of the current application, the proposed estimator can be a preferred choice.

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End-Effector Pose Estimation in Complex Environments Using Complementary Enhancement and Adaptive Fusion of Multisensor

Journal of Sensors ◽

10.1155/2021/5550850 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Mingrui Luo ◽

En Li ◽

Rui Guo ◽

Jiaxin Liu ◽

Zize Liang

Keyword(s):

Kalman Filter ◽

Pose Estimation ◽

Unscented Kalman Filter ◽

Rbf Neural Network ◽

Estimation Error ◽

Estimation Method ◽

Original Data ◽

Measurement Unit ◽

Complex Environments ◽

End Effector

Redundant manipulators are suitable for working in narrow and complex environments due to their flexibility. However, a large number of joints and long slender links make it hard to obtain the accurate end-effector pose of the redundant manipulator directly through the encoders. In this paper, a pose estimation method is proposed with the fusion of vision sensors, inertial sensors, and encoders. Firstly, according to the complementary characteristics of each measurement unit in the sensors, the original data is corrected and enhanced. Furthermore, an improved Kalman filter (KF) algorithm is adopted for data fusion by establishing the nonlinear motion prediction of the end-effector and the synchronization update model of the multirate sensors. Finally, the radial basis function (RBF) neural network is used to adaptively adjust the fusion parameters. It is verified in experiments that the proposed method achieves better performances on estimation error and update frequency than the original extended Kalman filter (EKF) and unscented Kalman filter (UKF) algorithm, especially in complex environments.

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New adaptive droop control with combined line impedance estimation method for parallel inverters

Science and Technology Development Journal ◽

10.32508/stdj.v19i4.781 ◽

2016 ◽

Vol 19 (4) ◽

pp. 45-64

Author(s):

Phuong Minh Le ◽

Duy Vo Duc Hoang ◽

Hoa Thi Xuan Pham ◽

Huy Minh Nguyen

Keyword(s):

Kalman Filter ◽

Control Method ◽

Least Squares Method ◽

Estimation Method ◽

Load Sharing ◽

Phase Lock Loop ◽

Power Sharing ◽

Voltage Source ◽

Droop Control ◽

Voltage Source Inverters

This paper presents a new load sharing control between paralleled three-phase inverters in an islanded-microgrid based on the line impedance estimation online by the use of the Kalman filter. We can solve the mismatch of power sharing when the line impedance changes due to the temperature and frequency, significant differences of line parameters and requirements of Plug-and-Play mode of inverters connected to the microgrid. Moreover, the paper also presents a new Droop control method working with the line impedance which is different from the Droop traditional algorithm when the line impedance is assumed pure resistance R or pure inductance X. In the paper, the line impedance estimation for parallel inverters uses the least squares method combined with Kalman filter. In addition, secondary control loops are designed to restore the voltage amplitude and frequency of the microgrid by using a combined nominal value SOGI-PLL with generalized integral block and phase lock loop to exactly monitor the voltage magnitude and frequency phase at common PCC. Control model has been simulated in Matlab/Simulink with three voltage source inverters connected in parallel for different ratios of the power sharing. The simulation results have shown the accuracy of the proposed control method. Therefore, the proposed adaptive droop control method based on line impedance estimation can be an alternative one for load sharing control in islanded microgrids.

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An extended Kalman-filter for regional scale inverse emission estimation

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-11-29195-2011 ◽

2011 ◽

Vol 11 (10) ◽

pp. 29195-29249 ◽

Cited By ~ 3

Author(s):

D. Brunner ◽

S. Henne ◽

C. A. Keller ◽

S. Reimann ◽

M. K. Vollmer ◽

...

Keyword(s):

Kalman Filter ◽

Dispersion Model ◽

Regional Scale ◽

Estimation Method ◽

Particle Dispersion ◽

Montreal Protocol ◽

Correlated Noise ◽

Temporal Correlations ◽

Emission Estimation ◽

Hfc 152A

Abstract. A Kalman-filter based inverse emission estimation method for long-lived trace gases is presented for use in conjunction with a Lagrangian particle dispersion model like FLEXPART. The sequential nature of the approach allows tracing slow seasonal and interannual changes rather than estimating a single period-mean emission field. Other important features include the estimation of a slowly varying concentration background at each measurement station, the possibility to constrain the solution to non-negative emissions, the quantification of uncertainties, the consideration of temporal correlations in the residuals, and the applicability to potentially large inversion problems. The method is first demonstrated for a set of synthetic observations created from a prescribed emission field with different levels of (correlated) noise, which closely mimics true observations. It is then applied to real observations of the three halocarbons HFC-125, HFC-152a and HCFC-141b at the remote research stations Jungfraujoch and Mace Head for the quantification of emissions in Western European countries from 2006 to 2010. Estimated HFC-125 emissions are mostly consistent with national totals reported to the Kyoto protocol and show a generally increasing trend over the considered period. Results for HFC-152a are much more variable with estimated emissions being both higher and lower in different countries. The highest emissions of the order of 1000 Mg yr−1 are estimated for Italy which so far does not report HFC-152a emissions. Emissions of HCFC-141b show a continuing strong decrease as expected due to its ban under the Montreal Protocol. Emissions from France, however, were still rather large (near 1000 Mg yr−1) in the years 2006 and 2007 but strongly declined thereafter.

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Orbit Propagation and Determination of Low Earth Orbit Satellites

International Journal of Antennas and Propagation ◽

10.1155/2014/903026 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Ho-Nien Shou

Keyword(s):

Kalman Filter ◽

Real Time ◽

Unscented Kalman Filter ◽

Estimation Method ◽

Nonlinear Problems ◽

Low Earth Orbit ◽

Earth Orbit ◽

Positioning Accuracy ◽

Orbit Propagation

This paper represents orbit propagation and determination of low Earth orbit (LEO) satellites. Satellite global positioning system (GPS) configured receiver provides position and velocity measures by navigating filter to get the coordinates of the orbit propagation (OP). The main contradictions in real-time orbit which is determined by the problem are orbit positioning accuracy and the amount of calculating two indicators. This paper is dedicated to solving the problem of tradeoffs. To plan to use a nonlinear filtering method for immediate orbit tasks requires more precise satellite orbit state parameters in a short time. Although the traditional extended Kalman filter (EKF) method is widely used, its linear approximation of the drawbacks in dealing with nonlinear problems was especially evident, without compromising Kalman filter (unscented Kalman Filter, UKF). As a new nonlinear estimation method, it is measured at the estimated measurements on more and more applications. This paper will be the first study on UKF microsatellites in LEO orbit in real time, trying to explore the real-time precision orbit determination techniques. Through the preliminary simulation results, they show that, based on orbit mission requirements and conditions using UKF, they can satisfy the positioning accuracy and compute two indicators.

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