scholarly journals ZUPT-aided INS Bypassing Stance Phase Detection by Using Foot-Instability-Based Adaptive Covariance

2021 ◽  
Author(s):  
Chi-Shih Jao ◽  
Andrei M. Shkel
Keyword(s):  
Covariance Matrix ◽  
Stance Phase ◽  
Phase Detector ◽  
Vertical Position ◽  
Navigation Systems ◽  
Phase Detection ◽  
Zero Velocity ◽  
Position Errors ◽  
Series Of Experiments ◽  
Navigation Accuracy

<p>In this paper, we propose a Foot-Instability-Based Adaptive (FIBA) covariance to dynamically adjust the covariance matrix for the pseudo-zero-velocity measurements in the Zero velocity UPdaTe (ZUPT)-aided Inertial Navigation Systems (INS). The proposed ZUPT-aided INS using the FIBA covariance is implemented in an Adaptive Extended Kalman Filter (AEKF) framework, where the measurement covariance matrix is updated in each iteration according to the FIBA covariance. The FIBA covariance is designed to have a very high value during the swing phases in a gait cycle, and the value significantly decreases during the stance phases. As a result, the proposed method eliminates a need to use a binary stance phase detector in implementation of the ZUPT-aided INS. Two series of indoor pedestrian navigation experiments were conducted to investigate the navigation performance of the algorithm. In the first series of experiments, which included cases of walking and running, localization solutions produced by the system using the FIBA covariance demonstrated 36% and 64% improvements in navigation accuracy along the horizontal and vertical directions, respectively. In the second series of experiments, which included a pedestrian walking on different indoor terrains, such as flat planes, stairs, and ramps, the navigation accuracy of the system using the FIBA covariance reduced horizontal and vertical position errors by 12% and 45%, respectively, as compared to the conventional ZUPT-aided INS.</p>

scholarly journals ZUPT-aided INS Bypassing Stance Phase Detection by Using Foot-Instability-Based Adaptive Covariance

2021 ◽  
Author(s):  
Chi-Shih Jao ◽  
Andrei M. Shkel
Keyword(s):  
Covariance Matrix ◽  
Stance Phase ◽  
Phase Detector ◽  
Vertical Position ◽  
Navigation Systems ◽  
Phase Detection ◽  
Zero Velocity ◽  
Position Errors ◽  
Series Of Experiments ◽  
Navigation Accuracy

<p>In this paper, we propose a Foot-Instability-Based Adaptive (FIBA) covariance to dynamically adjust the covariance matrix for the pseudo-zero-velocity measurements in the Zero velocity UPdaTe (ZUPT)-aided Inertial Navigation Systems (INS). The proposed ZUPT-aided INS using the FIBA covariance is implemented in an Adaptive Extended Kalman Filter (AEKF) framework, where the measurement covariance matrix is updated in each iteration according to the FIBA covariance. The FIBA covariance is designed to have a very high value during the swing phases in a gait cycle, and the value significantly decreases during the stance phases. As a result, the proposed method eliminates a need to use a binary stance phase detector in implementation of the ZUPT-aided INS. Two series of indoor pedestrian navigation experiments were conducted to investigate the navigation performance of the algorithm. In the first series of experiments, which included cases of walking and running, localization solutions produced by the system using the FIBA covariance demonstrated 36% and 64% improvements in navigation accuracy along the horizontal and vertical directions, respectively. In the second series of experiments, which included a pedestrian walking on different indoor terrains, such as flat planes, stairs, and ramps, the navigation accuracy of the system using the FIBA covariance reduced horizontal and vertical position errors by 12% and 45%, respectively, as compared to the conventional ZUPT-aided INS.</p>

Studying the Сonsistency of Extended Kalman Filter in Pedestrian Navigation with Foot-Mounted SINS

2021 ◽  
Vol 29 (2) ◽  
pp. 59-77
Author(s):  
Yu.V. Bolotin ◽  
◽  
A.V. Bragin ◽  
D.V. Gulevskii ◽  
◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Stance Phase ◽  
Navigation Systems ◽  
Velocity Data ◽  
Inertial Navigation Systems ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
Velocity Condition ◽  
Dynamic Errors

The paper focuses on pedestrian navigation with foot-mounted strapdown inertial navigation systems (SINS). Zero velocity updates (ZUPT) during the stance phase are commonly applied in such systems to improve the accuracy. Zero velocity data are processed by the extended Kalman filter (EKF). Zero velocity condition is written in two forms: in reference and body frames. The first form traditional for pedestrian navigation is shown to provide an inconsistent EKF. The second form provides a correct ZUPT algorithm, which is naturally written in so-called dynamic errors. The analyzed algorithm for data fusion from two SINS is based on the bound on foot-to-foot distance. It is shown how EKF inconsistency can be manifested, and how it can be avoided by proceeding back to dynamic errors. The results are obtained analytically using observability theory and covariance analysis.

scholarly journals A Pedestrian Navigation System Based on Low Cost IMU

2014 ◽  
Vol 67 (6) ◽  
pp. 929-949 ◽  
Author(s):  
Yan Li ◽  
Jianguo Jack Wang
Keyword(s):  
Constant Velocity ◽  
Inertial Measurement Unit ◽  
Stance Phase ◽  
Low Cost ◽  
Stair Climbing ◽  
Measurement Unit ◽  
Phase Detection ◽  
New Approach ◽  
Pedestrian Navigation ◽  
Zero Velocity

For indoor pedestrian navigation with a shoe-mounted inertial measurement unit (IMU), the zero velocity update (ZUPT) technique is implemented to constrain the sensors' error. ZUPT uses the fact that a stance phase appears in each step at zero velocity to correct IMU errors periodically. This paper introduces three main contributions we have achieved based on ZUPT. Since correct stance phase detection is critical for the success of applying ZUPT, we have developed a new approach to detect the stance phase of different gait styles, including walking, running and stair climbing. As the extension of ZUPT, we have proposed a new concept called constant velocity update (CUPT) to correct IMU errors on a moving platform with constant velocity, such as elevators or escalators where ZUPT is infeasible. A closed-loop step-wise smoothing algorithm has also been developed to eliminate discontinuities in the trajectory caused by sharp corrections. Experimental results demonstrate the effectiveness of the proposed algorithms.

scholarly journals Consistency of the Empirical Distributions of Navigation Positioning System Errors with Theoretical Distributions—Comparative Analysis of the DGPS and EGNOS Systems in the Years 2006 and 2014

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 31
Author(s):  
Mariusz Specht
Keyword(s):  
Normal Distribution ◽  
Root Mean Square ◽  
Statistical Tests ◽  
Position Error ◽  
Positioning System ◽  
Navigation Systems ◽  
Mean Square ◽  
Positioning Systems ◽  
Position Errors ◽  
System Errors

Positioning systems are used to determine position coordinates in navigation (air, land and marine). The accuracy of an object’s position is described by the position error and a statistical analysis can determine its measures, which usually include: Root Mean Square (RMS), twice the Distance Root Mean Square (2DRMS), Circular Error Probable (CEP) and Spherical Probable Error (SEP). It is commonly assumed in navigation that position errors are random and that their distribution are consistent with the normal distribution. This assumption is based on the popularity of the Gauss distribution in science, the simplicity of calculating RMS values for 68% and 95% probabilities, as well as the intuitive perception of randomness in the statistics which this distribution reflects. It should be noted, however, that the necessary conditions for a random variable to be normally distributed include the independence of measurements and identical conditions of their realisation, which is not the case in the iterative method of determining successive positions, the filtration of coordinates or the dependence of the position error on meteorological conditions. In the preface to this publication, examples are provided which indicate that position errors in some navigation systems may not be consistent with the normal distribution. The subsequent section describes basic statistical tests for assessing the fit between the empirical and theoretical distributions (Anderson-Darling, chi-square and Kolmogorov-Smirnov). Next, statistical tests of the position error distributions of very long Differential Global Positioning System (DGPS) and European Geostationary Navigation Overlay Service (EGNOS) campaigns from different years (2006 and 2014) were performed with the number of measurements per session being 900’000 fixes. In addition, the paper discusses selected statistical distributions that fit the empirical measurement results better than the normal distribution. Research has shown that normal distribution is not the optimal statistical distribution to describe position errors of navigation systems. The distributions that describe navigation positioning system errors more accurately include: beta, gamma, logistic and lognormal distributions.

An Improved and Efficient Algorithm for SINS/GPS/Doppler Integrated Navigation Systems

2012 ◽  
Vol 245 ◽  
pp. 323-329 ◽  
Author(s):  
Muhammad Ushaq ◽  
Jian Cheng Fang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Efficient Manner ◽  
Position Errors

Inertial navigation systems exhibit position errors that tend to grow with time in an unbounded mode. This degradation is due, in part, to errors in the initialization of the inertial measurement unit and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Mitigation to this growth and bounding the errors is to update the inertial navigation system periodically with external position (and/or velocity, attitude) fixes. The synergistic effect is obtained through external measurements updating the inertial navigation system using Kalman filter algorithm. It is a natural requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertia Navigation System (SINS), Global Positioning System (GPS) and Doppler radar is presented using a centralized linear Kalman filter by treating vector measurements with uncorrelated errors as scalars. Two main advantages have been obtained with this improved scheme. First is the reduced computation time as the number of arithmetic computation required for processing a vector as successive scalar measurements is significantly less than the corresponding number of operations for vector measurement processing. Second advantage is the improved numerical accuracy as avoiding matrix inversion in the implementation of covariance equations improves the robustness of the covariance computations against round off errors.

Precision Navigation Updating by Means of Digital Area Correlation

1977 ◽  
Vol 30 (2) ◽  
pp. 296-306 ◽  
Author(s):  
A. Hessel ◽  
W. Eckl
Keyword(s):  
Cost Effectiveness ◽  
Specific Task ◽  
Position Errors ◽  
Updating Procedure ◽  
The Cost ◽  
Navigation Accuracy

To fulfill any specific task a certain navigation accuracy is in general required. If this requirement can be achieved by different systems the cost effectiveness will be decisive. Apart from missions which allow large position errors, medium or high navigation accuracies will normally be desired, obtained either by high technological standards or by an unsophisticated system with an updating procedure. The cost of highly sophisticated systems increases exponentially with increasing accuracy, so that our object was to find an appropriate updating system.

Promising Map-Aided Aircraft Navigation Systems

2021 ◽  
Vol 29 (1) ◽  
pp. 32-51
Author(s):  
E.V. Karshakov ◽  
◽  
B.V. Pavlov ◽  
M.Yu. Tkhorenko ◽  
I.A. Papusha ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Field Gradient ◽  
Magnetic Field Gradient ◽  
Field Measurements ◽  
Navigation Systems ◽  
Geophysical Field ◽  
Airborne Measurements ◽  
Aircraft Navigation ◽  
Navigation Accuracy ◽  
Development Prospects

The paper analyses the development prospects for aircraft navigation systems using onboard geophysical field measurements. Prospective systems that are not widely applied yet are considered: magnetic gradiometers measuring the stationary magnetic field gradient, gravity gradiometers measuring the gravity field gradient, and electromagnetic systems measuring the alternating part of magnetic field. We discuss the main problems to be solved during airborne measurements of these parameters and give an overview of algorithms and hardware solutions. We analyse the results of onboard measurements and estimate the possible navigation accuracy.

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