An Improved Strong Tracking Kalman Filter Algorithm for the Initial Alignment of the Shearer

The strap-down inertial navigation system (SINS) is a commonly used sensor for autonomous underground navigation, which can be used for shearer positioning under a coal mine. During the process of initial alignment, inaccurate or time-varying noise covariance matrices will significantly degrade the accuracy of the initial alignment of the shearer. To overcome the performance degradation of the existing initial alignment algorithm under complex underground environment, a novel adaptive filtering algorithm is proposed by the integration of the strong tracking Kalman filter and the sequential filter for the initial alignment of the shearer with complex underground environment. Compared with the traditional multiple fading factor strong tracking Kalman filter (MSTKF) method, the proposed MSTSKF algorithm integrates the advantage of strong tracking Kalman filter and sequential filter, and multiple fading factor and forgetting factor for east and north velocity measurement are designed in the algorithm, respectively, which can effectively weaken the coupling relationship between the different states and increase strong robustness against process uncertainties. The simulation and experiment results show that the proposed MSTSKF method has better initial alignment accuracy and robustness than existing strong tracking Kalman filter algorithm.

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Research on an Improved Method for Foot-Mounted Inertial/Magnetometer Pedestrian-Positioning Based on the Adaptive Gradient Descent Algorithm

Sensors ◽

10.3390/s18124105 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4105 ◽

Cited By ~ 4

Author(s):

Qiuying Wang ◽

Juan Yin ◽

Aboelmagd Noureldin ◽

Umar Iqbal

Keyword(s):

Kalman Filter ◽

Gradient Descent ◽

Rapid Development ◽

Alignment Algorithm ◽

Magnetic Disturbance ◽

Misalignment Angle ◽

Initial Alignment ◽

Positioning Systems ◽

Descent Algorithm ◽

Gradient Descent Algorithm

Foot-mounted Inertial Pedestrian-Positioning Systems (FIPPSs) based on Micro Inertial Measurement Units (MIMUs), have recently attracted widespread attention with the rapid development of MIMUs. The can be used in challenging environments such as firefighting and the military, even without augmenting with Global Navigation Satellite System (GNSS). Zero Velocity Update (ZUPT) provides a solution for the accumulated positioning errors produced by the low precision and high noise of the MIMU, however, there are some problems using ZUPT for FIPPS, include fast-initial alignment and unobserved heading misalignment angle, which are addressed in this paper. Our first contribution is proposing a fast-initial alignment algorithm for foot-mounted inertial/magnetometer pedestrian positioning based on the Adaptive Gradient Descent Algorithm (AGDA). Considering the characteristics of gravity and Earth’s magnetic field, measured by accelerometers and magnetometers, respectively, when the pedestrian is standing at one place, the AGDA is introduced as the fast-initial alignment. The AGDA is able to estimate the initial attitude and enhance the ability of magnetic disturbance suppression. Our second contribution in this paper is proposing an inertial/magnetometer positioning algorithm based on an adaptive Kalman filter to solve the problem of the unobserved heading misalignment angle. The algorithm utilizes heading misalignment angle as an observation for the Kalman filter and can improve the accuracy of pedestrian position by compensating for magnetic disturbances. In addition, introducing an adaptive parameter in the Kalman filter is able to compensate the varying magnetic disturbance for each ZUPT instant during the walking phase of the pedestrian. The performance of the proposed method is examined by conducting pedestrian test trajectory using MTi-G710 manufacture by XSENS. The experimental results verify the effectiveness and applicability of the proposed method.

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Research on Adaptive Kalman Filter for INS

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.872.316 ◽

2017 ◽

Vol 872 ◽

pp. 316-320

Author(s):

Kai Xia Wei

Keyword(s):

Kalman Filter ◽

Response Speed ◽

Measured Data ◽

Specific Method ◽

Alignment Error ◽

Initial Alignment ◽

Adaptive Kalman Filter ◽

Noise Interference ◽

Kalman Filter Algorithm ◽

The Stability

Due to sensor accuracy and noise interference and other reasons, the measured data may be inaccurate or even wrong. This will reduce the accuracy of the filter and the reliability and response speed of the Kalman filter, and even make the Kalman filter lose the stability. In this paper, a new INS initial alignment error model and observation model are derived for the errors in INS initial alignment. The adaptive Kalman filter is built to improve the stability and the accuracy of filter. The specific method is to make the adaptive Kalman filter manage to correct the filter online by getting the observed data. The simulation results show the proposed algorithm improves the accuracy of initial alignment in SINS, and prove the adaptive Kalman filter is effective. The main innovation in this paper is to manage to build the adaptive Kalman filter to modify the filter online by using the observed data. The adaptive Kalman filter algorithm improves the accuracy of the filter.

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An Improved Adaptive Algorithm for INS/GPS System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.397-400.1606 ◽

2013 ◽

Vol 397-400 ◽

pp. 1606-1610 ◽

Cited By ~ 1

Author(s):

Li Dong Wang ◽

Ying Zhao ◽

Ni Zhang

Keyword(s):

Adaptive Filtering ◽

Adaptive Filter ◽

Adaptive Algorithm ◽

Initial Conditions ◽

Forgetting Factor ◽

Kalman Filter Algorithm ◽

Simulation Results ◽

The Stability ◽

Gps System

In INS/GPS system, the changing of initial conditions and the quality of the data can affect the convergence of the conventional Kalman filter algorithm. Sage-Husa adaptive filter algorithm is adopted in the INS/GPS system in this paper. The effecting of the forgetting factor to the improved Sage-Husa adaptive filter algorithm is studied and the simulation results show that when the forgetting factor taken near 0.97, the adaptive filtering result is best, the stability of the system is guaranteed and the convergent speed of error can be reduced.

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Initial Alignment of Shipborne SINS under Ship Motion

Giroskopiya i Navigatsiya ◽

10.17285/0869-7035.0043 ◽

2020 ◽

Vol 28 (3) ◽

pp. 3-17 ◽

Cited By ~ 1

Author(s):

G.I. Emel’yantsev ◽

◽

A.P. Stepanov ◽

B.A. Blazhnov ◽

◽

...

Keyword(s):

Kalman Filter ◽

Ship Motion ◽

Second Step ◽

Alignment Algorithm ◽

Initial Alignment ◽

Log Data ◽

Current Attitude ◽

Alignment Errors ◽

Filter Algorithms ◽

Water Speed

The paper focuses on improving the accuracy and shortening the time of shipborne SINS initial alignment under the ship yaw, roll and pitch. This is achieved by implementing a two-step SINS alignment algorithm. At the first step, the ship current attitude parameters are approximately autonomously estimated by data from gyros and accelerometers with account for its dynamics and using water speed log data. At the second step, the system fine alignment is performed with account for alignment errors after the completion of the first step. Speed and position measurements from external aids are additionally applied during the fine alignment. Kalman filter algorithms are used in the first and second steps. Results from bench and sea tests for SINS on navigation grade FOGs under the ship yaw, roll and pitch motion are provided.

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Application of Adaptive Kalman Filter Technique in Initial Alignment of Single-Axial Rotation Strap-Down Inertial Navigation System

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.466-467.617 ◽

2012 ◽

Vol 466-467 ◽

pp. 617-621

Author(s):

Song Tian Shang ◽

Wen Shao Gao

Keyword(s):

Kalman Filter ◽

Axial Rotation ◽

Estimation Accuracy ◽

Initial Alignment ◽

Adaptive Kalman Filter ◽

Rotation System ◽

Kalman Filter Algorithm ◽

Axis Rotation ◽

Single Axis Rotation ◽

Better Than

In order to improve the accuracy of initial alignment which determines the accuracy of navigation, a Sage-Husa adaptive kalman filter algorithm is applied to SINS initial alignment of single-axis rotation system. The simulation result further shows that in the case of inaccurate statistical property of noise, the estimation accuracy of Sage-Husa adaptive kalman filter is better than the conventional kalman filter.

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SOC Estimation of Lithium-Ion Battery Based on Kalman Filter Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.347-350.1852 ◽

2013 ◽

Vol 347-350 ◽

pp. 1852-1855 ◽

Cited By ~ 1

Author(s):

Ding Xuan Yu ◽

Yan Xia Gao

Keyword(s):

Kalman Filter ◽

Lithium Ion Battery ◽

Open Circuit Voltage ◽

Lithium Ion ◽

Open Circuit ◽

First Order ◽

Soc Estimation ◽

Simulation And Experiment ◽

Kalman Filter Algorithm ◽

Model Curve

This paper presents Extended Kalman-filter (EKF) algorithm which is based on a first-order Lithium-ion batteries model. Curve fitting According to the OCV(open circuit voltage),SOC(state of charge) parameters measured in experiments, descript status equation and observation equation of Lithium-ion battery in detail , so that it can accurately demonstrates the characteristics of the Lithium-ion battery. Simulation and experiment results show the feasibility and effectiveness of the algorithm.

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Multi-Fading Factor and Update Monitoring Strategy Adaptive Kalman Filter Based Variational Bayesian with Inaccurate Time-Varying Process and Measure-Ment Noise Covariance Matrices

10.20944/preprints202012.0164.v1 ◽

2020 ◽

Author(s):

Chenghao Shan ◽

Weidong Zhou ◽

Yefeng Yang ◽

Zihao Jiang

Keyword(s):

Kalman Filter ◽

Covariance Matrix ◽

Measurement Noise ◽

Time Varying ◽

Monitoring Strategy ◽

Adaptive Kalman Filter ◽

Variational Bayesian ◽

Measurement Noise Covariance ◽

Noise Covariance ◽

Fading Factor

Aiming at the problem that the performance of Adaptive Kalman filter estimation will be affected when the statistical characteristics of the process and measurement noise matrix are inaccurate and time-varying in the linear Gaussian state-space model, an algorithm of Multi-fading factor and update monitoring strategy adaptive Kalman filter based variational Bayesian is proposed. Inverse Wishart distribution is selected as the measurement noise model, the system state vector and measurement noise covariance matrix are estimated with the variational Bayesian method. The process noise covariance matrix is estimated by the maximum a posteriori principle, and the update monitoring strategy with adjustment factors is used to maintain the positive semi-definite of the updated matrix. The above optimal estimation results are introduced as time-varying parameters into the multiple fading factors to improve the estimation accuracy of the one-step state predicted covariance matrix. The application of the proposed algorithm in target tracking is simulated. The results show that compared with the current filters, the proposed filtering algorithm has better accuracy and convergence performance, and realizes the simultaneous estimation of inaccurate time-varying process and measurement noise covariance matrices.

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An Improved SINS Alignment Method Based on Adaptive Cubature Kalman Filter

Sensors ◽

10.3390/s19245509 ◽

2019 ◽

Vol 19 (24) ◽

pp. 5509 ◽

Cited By ~ 2

Author(s):

Yonggang Zhang ◽

Geng Xu ◽

Xin Liu

Keyword(s):

Kalman Filter ◽

Covariance Matrix ◽

Real Life ◽

Covariance Matrices ◽

The Body ◽

Alignment Method ◽

Initial Alignment ◽

Noise Covariance Matrix ◽

Cubature Kalman Filter ◽

Noise Covariance

Initial alignment is critical and indispensable for the inertial navigation system (INS), which determines the initial attitude matrix between the reference navigation frame and the body frame. The conventional initial alignment methods based on the Kalman-like filter require an accurate noise covariance matrix of state and measurement to guarantee the high estimation accuracy. However, in a real-life practical environment, the uncertain noise covariance matrices are often induced by the motion of the carrier and external disturbance. To solve the problem of initial alignment with uncertain noise covariance matrices and a large initial misalignment angle in practical environment, an improved initial alignment method based on an adaptive cubature Kalman filter (ACKF) is proposed in this paper. By virtue of the idea of the variational Bayesian (VB) method, the system state, one step predicted error covariance matrix, and measurement noise covariance matrix of initial alignment are adaptively estimated together. Simulation and vehicle experiment results demonstrate that the proposed method can improve the accuracy of initial alignment compared with existing methods.

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Polar Transversal Initial Alignment Algorithm for UUV with a Large Misalignment Angle

Sensors ◽

10.3390/s18103231 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3231

Author(s):

Zheping Yan ◽

Lu Wang ◽

Tongda Wang ◽

Honghan Zhang ◽

Zewen Yang

Keyword(s):

Polar Region ◽

Unscented Kalman Filter ◽

Equatorial Region ◽

Alignment Algorithm ◽

Misalignment Angle ◽

Initial Alignment ◽

Unmanned Underwater Vehicles ◽

Alignment Algorithms ◽

Simulation And Experiment ◽

Dynamics Models

The conventional initial alignment algorithms are invalid in the polar region. This is caused by the rapid convergence of the Earth meridians in the high-latitude areas. However, the initial alignment algorithms are important for the accurate navigation of Unmanned Underwater Vehicles. The polar transversal initial alignment algorithm is proposed to overcome this problem. In the polar transversal initial alignment algorithm, the transversal geographic frame is chosen as the navigation frame. The polar region in the conventional frames is equivalent to the equatorial region in the transversal frames. Therefore, the polar transversal initial can be effectively applied in the polar region. According to the complex environment in the polar region, a large misalignment angle is considered in this paper. Based on the large misalignment angle condition, the non-linear dynamics models are established. In addition, the simplified unscented Kalman filter (UKF) is chosen to realize the data fusion. Two comparison simulations and an experiment are performed to verify the performance of the proposed algorithm. The simulation and experiment results indicate the validity of the proposed algorithm, especially when large misalignment angles occur.

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In-Flight Alignment Algorithm Based on ADD2 for Airborne POS

Journal of Navigation ◽

10.1017/s0373463312000446 ◽

2012 ◽

Vol 66 (2) ◽

pp. 209-225 ◽

Cited By ~ 3

Author(s):

Jiancheng Fang ◽

Xiaoying Han

Keyword(s):

Global Positioning System ◽

Adaptive Filtering ◽

Integrated System ◽

Alignment Accuracy ◽

Alignment Algorithm ◽

Initial Alignment ◽

Gps Measurement ◽

Global Positioning ◽

Difference Filter ◽

Divided Difference Filter

The Position and Orientation System (POS) is a special Strapdown Inertial Navigation System (SINS)/Global Positioning System (GPS) integrated system, widely employed in airborne remote sensing. In-Flight Alignment (IFA) is an effective way to improve the accuracy and speed of initial alignment for an airborne POS. IFA is normally accomplished with references from the position and velocity of GPS for SINS, so that unstable GPS measurements will result in poor alignment accuracy. To improve alignment accuracy under unstable GPS conditions, an adaptive filtering algorithm of the Second-order Divided Difference filter (DD2) based on adaptive innovation estimation is proposed, which introduces calculated innovation covariance directly into computation of the filter gain matrix. Then, the adaptive DD2 algorithm is used for the IFA of the POS with a large initial heading error. To validate the proposed algorithm, simulations are undertaken, followed by IFA experiments for the prototype of the airborne POS (TX-F30) under a turning manoeuvre in a car-mounted experiment, and under an “8” manoeuvre in-flight. The simulations and experimental results show that the proposed algorithm can reach better alignment accuracy under unknown statistical characteristic of GPS measurement noises.

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