scholarly journals A Reconstruction Filter for Saturated Accelerometer Signals Due to Insufficient FSR in Foot-mounted Inertial Navigation System

2021 ◽  
Author(s):  
Chi-Shih Jao ◽  
Andrei M. Shkel
Keyword(s):  
Inertial Sensors ◽  
Inertial Navigation ◽  
Gaussian Process Regression ◽  
Navigation Systems ◽  
Analog Device ◽  
Bias Drift ◽  
Pedestrian Navigation ◽  
Angular Velocities ◽  
Reconstruction Filter ◽  
Commercial Off The Shelf

In pedestrian inertial navigation, one possible placement of Inertial Measurement Units (IMUs) is on a footwear. This placement allows to limit the accumulation of navigation errors due to the bias drift of inertial sensors and is generally a preferable placement of sensors to achieve the highest precision of pedestrian inertial navigation. However, inertial sensors mounted on footwear experience significantly higher accelerations and angular velocities during regular pedestrian activities than during more conventional navigation tasks, which could exceed Full Scale Range (FSR) of many commercial-off-the-shelf IMUs, therefore degrading accuracy of pedestrian navigation systems. This paper proposes a reconstruction filter to mitigate localization error in pedestrian navigation due to insufficient FSR of inertial sensors. The proposed reconstruction filter approximates immeasurable accelerometer's signals with a triangular function and estimates the size of the triangles using a Gaussian Process regression. To evaluate performance of the proposed reconstruction filter, we conducted two series of indoor pedestrian navigation experiments with a VectorNav VN-200 IMU and an Analog Device ADIS16497-3 IMU. In the first series of experiments, forces experienced by the IMUs did not exceed the FSRs of the sensors, while in the second series, the forces surpassed the FSR of the VN-200 IMU and saturated the accelerometer's readings. The saturated readings reduced the accuracy of estimated positions using the VN-200 by 1.34× and 3.37× along horizontal and vertical directions. When applying our proposed reconstruction filter to the saturated measurements, the navigation accuracy was increased by 5% horizontally and 50% vertically, as compared to using unreconstructed signals.

scholarly journals A Reconstruction Filter for Saturated Accelerometer Signals Due to Insufficient FSR in Foot-mounted Inertial Navigation System

2021 ◽  
Author(s):  
Chi-Shih Jao ◽  
Andrei M. Shkel
Keyword(s):  
Inertial Sensors ◽  
Inertial Navigation ◽  
Gaussian Process Regression ◽  
Navigation Systems ◽  
Analog Device ◽  
Bias Drift ◽  
Pedestrian Navigation ◽  
Angular Velocities ◽  
Reconstruction Filter ◽  
Commercial Off The Shelf

In pedestrian inertial navigation, one possible placement of Inertial Measurement Units (IMUs) is on a footwear. This placement allows to limit the accumulation of navigation errors due to the bias drift of inertial sensors and is generally a preferable placement of sensors to achieve the highest precision of pedestrian inertial navigation. However, inertial sensors mounted on footwear experience significantly higher accelerations and angular velocities during regular pedestrian activities than during more conventional navigation tasks, which could exceed Full Scale Range (FSR) of many commercial-off-the-shelf IMUs, therefore degrading accuracy of pedestrian navigation systems. This paper proposes a reconstruction filter to mitigate localization error in pedestrian navigation due to insufficient FSR of inertial sensors. The proposed reconstruction filter approximates immeasurable accelerometer's signals with a triangular function and estimates the size of the triangles using a Gaussian Process regression. To evaluate performance of the proposed reconstruction filter, we conducted two series of indoor pedestrian navigation experiments with a VectorNav VN-200 IMU and an Analog Device ADIS16497-3 IMU. In the first series of experiments, forces experienced by the IMUs did not exceed the FSRs of the sensors, while in the second series, the forces surpassed the FSR of the VN-200 IMU and saturated the accelerometer's readings. The saturated readings reduced the accuracy of estimated positions using the VN-200 by 1.34× and 3.37× along horizontal and vertical directions. When applying our proposed reconstruction filter to the saturated measurements, the navigation accuracy was increased by 5% horizontally and 50% vertically, as compared to using unreconstructed signals.

scholarly journals Dual MIMU Pedestrian Navigation by Inequality Constraint Kalman Filtering

2016 ◽  
Author(s):  
Wei Shi ◽  
Yang Wang ◽  
Yuanxin Wu
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Inertial Navigation ◽  
Inequality Constraint ◽  
Time Range ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Pedestrian Navigation ◽  
External Assistance ◽  
Constraint Method

The foot-mounted inertial navigation system is an important application of pedestrian navigation as it in principle does not rely any external assistance. A real-time range decomposition constraint method is proposed in this paper to combine the information of dual foot-mounted inertial navigation systems. It is well known that low-cost inertial sensors with ZUPT (zero-velocity update) and range decomposition constraint perform better than in either single way. This paper recommends that the distance of separation between the position estimates of feet-mounted inertial navigation systems be restricted in the ellipsoidal constraint which relates to the maximum step and leg height. The performance of the proposed method is studied utilizing experimental data. The results indicate that the method can effectively correct the dual navigation systems’ position over the existing spherical constraint.

Design, modeling, and simulation of an INS system using an asymmetric structure of six accelerometers and its error analysis in the ECEF frame

2016 ◽  
Vol 231 (13) ◽  
pp. 2345-2361 ◽  
Author(s):  
Vahid Ghasemzadeh ◽  
Mohammad M Arefi
Keyword(s):  
Error Analysis ◽  
Lower Cost ◽  
Inertial Navigation ◽  
Asymmetric Structure ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Symmetric Structure ◽  
Angular Velocities ◽  
Dynamic Relations ◽  
Short Time

The inertial navigation system is one of the most important and common methods of navigation. In this system, accelerometers and gyroscopes are used to measure linear accelerations and angular velocities, respectively. Accelerometers have simpler manufacture techniques, lower cost, and smaller volume and weight in comparison with gyroscopes. Therefore, in some application of navigation systems, non-gyro inertial navigation systems based on accelerometers are used. In this paper, an asymmetric structure of six accelerometers is proposed. Then dynamic relations of this structure are extracted. This structure and its relations can determine linear accelerations and angular velocities, completely. Moreover, the algorithm of inertial navigation in earth centered earth fixed (ECEF) frame is suggested. Error analysis as of the most important issues in inertial navigation is discussed. Thus, bias, misalignment, sensitivity, and noise of accelerometers are modeled appropriately. In addition, a symmetric structure of accelerometers is proposed and its equations are derived. Finally, the designed system, error model of accelerometers, and algorithm of inertial navigation in ECEF frame are simulated. The results of simulation show that the designed system has suitable accuracy and applications for short time navigation. Furthermore, results confirm that the proposed asymmetric structure requires less accelerometer in comparison with symmetric structure.

Gravity Requirements for Compensation of Ultra-Precise Inertial Navigation

2005 ◽  
Vol 58 (3) ◽  
pp. 479-492 ◽  
Author(s):  
Jay Hyoun Kwon ◽  
Christopher Jekeli
Keyword(s):  
Inertial Sensors ◽  
Interpolation Method ◽  
Gravity Data ◽  
Inertial Navigation ◽  
Navigation Systems ◽  
Gravity Compensation ◽  
Global Gravity ◽  
Navigation Error ◽  
Global Gravity Model

Precision inertial navigation depends not only on the quality of the inertial sensors (accelerometers and gyros), but also on the accuracy of the gravity compensation. With a view toward the next-generation inertial navigation systems, based on sensors whose errors contribute as little as a few metres per hour to the navigation error budget, we have analyzed the required quality of gravity compensation to the navigation solution. The investigation considered a standard compensation method using ground data to predict the gravity vector at altitude for aircraft free-inertial navigation. The navigation effects of the compensation errors were examined using gravity data in two gravimetrically distinct areas and a navigation simulator with parameters such as data noise and resolution, supplemental global gravity model noise, and on-track interpolation method. For a typical flight trajectory at 5 km altitude and 300 km/hr aircraft speed, the error in gravity compensation contributes less than 5 m to the position error after one hour of free-inertial navigation if the ground data are gridded with 2 arcmin resolution and are accurate to better than 5 mGal.

scholarly journals Analysis and Verification of Rotation Modulation Effects on Inertial Navigation System based on MEMS Sensors

2013 ◽  
Vol 66 (5) ◽  
pp. 751-772 ◽  
Author(s):  
Xueyun Wang ◽  
Jie Wu ◽  
Tao Xu ◽  
Wei Wang
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensors ◽  
Inertial Navigation ◽  
Cost Effective ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Mems Sensors ◽  
Micro Electro Mechanical Systems ◽  
Position Accuracy

Inertial Navigation Systems (INS) were large, heavy and expensive until the development of cost-effective inertial sensors constructed with Micro-electro-mechanical systems (MEMS). However, the large errors and poor error repeatability of MEMS sensors make them inadequate for application in many situations even with frequent calibration. To solve this problem, a systematic error auto-compensation method, Rotation Modulation (RM) is introduced and detailed. RM does no damage to autonomy, which is one of the most important characteristics of an INS. In this paper, the RM effects on navigation performance are analysed and different forms of rotation schemes are discussed. A MEMS-based INS with the RM technique applied is developed and specific calibrations related to rotation are investigated. Experiments on the developed system are conducted and results verify that RM can significantly improve navigation performance of MEMS-based INS. The attitude accuracy is improved by a factor of 5, and velocity/position accuracy by a factor of 10.

Aiding Low Cost Inertial Navigation with Building Heading for Pedestrian Navigation

2011 ◽  
Vol 64 (2) ◽  
pp. 219-233 ◽  
Author(s):  
Khairi Abdulrahim ◽  
Chris Hide ◽  
Terry Moore ◽  
Chris Hill
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensors ◽  
Low Cost ◽  
Inertial Navigation ◽  
Field Trials ◽  
Basic Knowledge ◽  
Effective Solution ◽  
Pedestrian Navigation

In environments where GNSS is unavailable or not useful for positioning, the use of low cost MEMS-based inertial sensors has paved a way to a more cost effective solution. Of particular interest is a foot mounted pedestrian navigation system, where zero velocity updates (ZUPT) are used with the standard strapdown navigation algorithm in a Kalman filter to restrict the error growth of the low cost inertial sensors. However heading drift still remains despite using ZUPT measurements since the heading error is unobservable. External sensors such as magnetometers are normally used to mitigate this problem, but the reliability of such an approach is questionable because of the existence of magnetic disturbances that are often very difficult to predict. Hence there is a need to eliminate the heading drift problem for such a low cost system without relying on external sensors to give a possible stand-alone low cost inertial navigation system. In this paper, a novel and effective algorithm for generating heading measurements from basic knowledge of the orientation of the building in which the pedestrian is walking is proposed to overcome this problem. The effectiveness of this approach is demonstrated through three field trials using only a forward Kalman filter that can work in real-time without any external sensors. This resulted in position accuracy better than 5 m during a 40 minutes walk, about 0·1% in position error of the total distance. Due to its simplistic algorithm, this simple yet very effective solution is appealing for a promising future autonomous low cost inertial navigation system.

Pedestrian navigation using inertial sensors and altitude error correction

Sensor Review ◽  
2015 ◽  
Vol 35 (1) ◽  
pp. 68-75 ◽  
Author(s):  
Wen Liu ◽  
Yingjun Zhang ◽  
Xuefeng Yang ◽  
Shengwei Xing
Keyword(s):  
Error Correction ◽  
Inertial Sensors ◽  
Angular Rate ◽  
Gait Recognition ◽  
Inertial Navigation ◽  
Navigation Systems ◽  
Content Type ◽  
Positioning Errors ◽  
Walking Motion ◽  
Dynamic Time

Purpose – The aim of this article is to present a PIN (pedestrian inertial navigation) solution that incorporates altitude error correction, which eliminates the altitude error accurately without using external sensors. The main problem of PIN is the accumulation of positioning errors due to the drift caused by the noise in the sensors. Experiment results show that the altitude errors are significant when navigating in multilayer buildings, which always lead to localization to incorrect floors. Design/methodology/approach – The PIN proposed is implemented over an inertial navigation systems (INS) framework and a foot-mounted IMU. The altitude error correction idea is identifying the most probable floor of each horizontal walking motion. To recognize gait types, the walking motion is described with angular rate measured by IMU, and the dynamic time warping algorithm is used to cope with the different dimension samples due to the randomness of walking motion. After gait recognition, the altitude estimated with INS of each horizontal walking is checked for association with one of the existing in a database. Findings – Experiment results show that high accuracy altitude is achieved with altitude errors below 5 centimeters for upstairs and downstairs routes in a five floors building. Research limitations/implications – The main limitations of the study is the assumption that accuracy floor altitude information is available. Originality/value – Our PIN system eliminates altitude errors accurately and intelligently, which benefits from the new idea of combination of gait recognition and map-matching. In addition, only one IMU is used which is different from other approach that use external sensors.

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