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Accuracy Improvement Methods and New Applications of Inertial-Based Navigation System

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Navigation and Positioning".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 29333

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Special Issue Editors

Dr. Gongmin Yan

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Guest Editor

School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
Interests: error modeling and testing of inertial devices and calibration of strapdown inertial navigation system; transfer alignment technology of inertial navigation system and initial alignment technology of moving base; inertial based vehicle autonomous positioning and orientation technology; theory and application of high precision integrated navigation and information fusion; autonomous driving technology; robot autonomous navigation technology
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Dr. Qiangwen Fu

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Research Center of AIMCNT, School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Interests: error modeling of optical gyroscope; autopilot of vehicle

Dr. Jun Weng

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Guest Editor

School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China
Interests: robotic navigation; intelligent fault detection

Prof. Dr. Yueyang Ben

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Guest Editor

College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
Interests: high-accuracy updating algorithm of SINS; visual navigation; integrated navigation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Inertial measurement, which has been widely applied in aerospace, aviation, automatic pilot, AUV, and many other fields, is a key technology for motion detection and control for most vehicles. The inertial navigation system with multi-source information fusion has become a mainstream solution to the problem that the navigation errors of the pure inertial navigation system essentially increase over time. A stable inertial-based navigation system to provide more accurate navigation information is urgently required for smart city construction, precision agriculture, precision-guided munition, etc., in future. Hence, improving the measurement accuracy and measurement reliability of attitude, velocity, and position has always been a hot topic in the field of inertial-based navigation system research.

The scope of this Special Issue will be the principle and performance improvement of inertial sensors, design methods of high-accuracy navigation, the technology of multi-sensor information fusion, and new applications of inertial-based navigation systems. The topics of interest include, but are not limited to, the following:

Novel principles of inertial sensors;
Accuracy improvement approach and technology of inertial sensors;
Modeling and compensating technology of inertial sensor errors;
Accurate testing and calibration of the inertial navigation system;
Accurate alignment of the inertial-based navigation system on the moving base;
High-accuracy updating algorithms for inertial navigation systems;
Novel filtering methods of multi-sensor integrated navigation (GNSS/DOV/visual navigation/LiDAR/map matching);
Intelligent fault detection approach for navigation systems;
New applications of inertial-based navigation systems in new fields (autopilot/energy and resource extraction/smart farming and precision agriculture).

Dr. Gongmin Yan
Dr. Qiangwen Fu
Dr. Jun Weng
Prof. Dr. Yueyang Ben
Guest Editors

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Keywords

inertial navigation
error modeling
error calibration and compensation
initial alignment on moving base
multi-sensor information fusion
intelligent fault detection

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Published Papers (11 papers)

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Research

13 pages, 1764 KiB

Open AccessCommunication

A Scalable Distributed Control Algorithm for Bearing-Only Passive UAV Formation Maintenance

by Yuchong Gao, Huiqi Feng, Jiexiang Chen, Junhui Li and Zhiqing Wei

Sensors 2023, 23(8), 3849; https://doi.org/10.3390/s23083849 - 10 Apr 2023

Cited by 5 | Viewed by 1925

Abstract

Unmanned Aerial Vehicles (UAVs) can cooperate through formations to perform tasks. Wireless communication allows UAVs to exchange information, but for the situations requiring high security, electromagnetic silence is needed to avoid potential threats. The passive UAV formation maintenance strategies can fulfill the requirement of electromagnetic silence at the cost of heavy real-time computing and precise locations of UAVs. To pursue high real-time performance without the localization of UAVs, this paper proposes a scalable distributed control algorithm for bearing-only passive UAV formation maintenance. By minimizing necessary communication, pure angle information is applied to maintain UAV formations through distributed control, without the knowledge of the UAVs’ precise locations. The convergency of the proposed algorithm is proven strictly and the converging radius is derived. Through simulation, the proposed algorithm is proven to be suitable for a general case and demonstrates fast convergence speed, strong anti-interference capability, and high scalability. Full article

(This article belongs to the Special Issue Accuracy Improvement Methods and New Applications of Inertial-Based Navigation System)

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21 pages, 3085 KiB

Open AccessArticle

A Rapid Self-Alignment Strategy for a Launch Vehicle on an Offshore Launching Platform

by Rongjun Mu, Tengfei Zhang and Shoupeng Li

Sensors 2023, 23(1), 339; https://doi.org/10.3390/s23010339 - 28 Dec 2022

Cited by 2 | Viewed by 1871

Abstract

To reduce the impact of offshore launching platform motion and swaying on the self-alignment accuracy of a launch vehicle, a rapid self-alignment strategy, which involves an optimal combination of anti-swaying coarse alignment (ASCA), backtracking navigation, and reverse Kalman filtering is proposed. During the entire alignment process, the data provided by the strapdown inertial navigation system (SINS) are stored and then applied to forward and backtrack self-alignment. This work elaborates the basic principles of coarse alignment and then analyzes the influence of ASCA time on alignment accuracy. An error model was built for the reverse fine alignment system. The coarse alignment was carried out based on the above work, then the state of the alignment system was retraced using the reverse inertial navigation solution and reverse Kalman filtering with the proposed strategy. A cycle-index control function was designed to approximate strict backtracking navigation. Finally, the attitude error was compensated for after the completion of the first and the last forward navigation. To demonstrate the effectiveness of the proposed strategy, numerical simulations were carried out in a scenario of launch vehicle motion and swaying. The proposed strategy can maximize the utilization of SINS data and hence improve the alignment accuracy and further reduce the alignment time. The results show that the fully autonomous alignment technology of the SINS can replace the complex optical aiming system and realize the determination of the initial attitude of a launch vehicle before launch. Full article

(This article belongs to the Special Issue Accuracy Improvement Methods and New Applications of Inertial-Based Navigation System)

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29 pages, 9634 KiB

Open AccessArticle

A Context-Aware Smartphone-Based 3D Indoor Positioning Using Pedestrian Dead Reckoning

by Boshra Khalili, Rahim Ali Abbaspour, Alireza Chehreghan and Nahid Vesali

Sensors 2022, 22(24), 9968; https://doi.org/10.3390/s22249968 - 17 Dec 2022

Cited by 8 | Viewed by 2566

Abstract

The rise in location-based service (LBS) applications has increased the need for indoor positioning. Various methods are available for indoor positioning, among which pedestrian dead reckoning (PDR) requires no infrastructure. However, with this method, cumulative error increases over time. Moreover, the robustness of the PDR positioning depends on different pedestrian activities, walking speeds and pedestrian characteristics. This paper proposes the adaptive PDR method to overcome these problems by recognizing various phone-carrying modes, including texting, calling and swinging, as well as different pedestrian activities, including ascending and descending stairs and walking. Different walking speeds are also distinguished. By detecting changes in speed during walking, PDR positioning remains accurate and robust despite speed variations. Each motion state is also studied separately based on gender. Using the proposed classification approach consisting of SVM and DTree algorithms, different motion states and walking speeds are identified with an overall accuracy of 97.03% for women and 97.67% for men. The step detection and step length estimation model parameters are also adjusted based on each walking speed, gender and motion state. The relative error values of distance estimation of the proposed method for texting, calling and swinging are 0.87%, 0.66% and 0.92% for women and 1.14%, 0.92% and 0.76% for men, respectively. Accelerometer, gyroscope and magnetometer data are integrated with a GDA filter for heading estimation. Furthermore, pressure sensor measurements are used to detect surface transmission between different floors of a building. Finally, for three phone-carrying modes, including texting, calling and swinging, the mean absolute positioning errors of the proposed method on a trajectory of 159.2 m in a multi-story building are, respectively, 1.28 m, 0.98 m and 1.29 m for women and 1.26 m, 1.17 m and 1.25 m for men. Full article

(This article belongs to the Special Issue Accuracy Improvement Methods and New Applications of Inertial-Based Navigation System)

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17 pages, 1259 KiB

Open AccessArticle

Movable Surface Rotation Angle Measurement System Using IMU

by Changfa Wang, Xiaowei Tu, Qi Chen, Qinghua Yang and Tao Fang

Sensors 2022, 22(22), 8996; https://doi.org/10.3390/s22228996 - 21 Nov 2022

Cited by 2 | Viewed by 4129

Abstract

In this paper, we describe a rotation angle measurement system (RAMS) based on an inertial measurement unit (IMU) developed to measure the rotation angle of a movable surface. The existing IMU-based attitude (tilt) sensor can only accurately measure the rotation angle when the rotation axis of the movable surface is perfectly aligned with the X axis or Y axis of the sensor, which is always not possible in real-world engineering. To overcome the difficulty of sensor installation and ensure measurement accuracy, first, we build a model to describe the relationship between the rotation axis and the IMU. Then, based on the built model, we propose a simple online method to estimate the direction of the rotation axis without using a complicated apparatus and a method to estimate the rotation angle using the known rotation axis based on the extended Kalman filter (EKF). Using the estimated rotation axis direction, we can effectively eliminate the influence of the mounting position on the measurement results. In addition, the zero-velocity detection (ZVD) technique is used to ensure the reliability of the rotation axis direction estimation and is used in combination with the EKF as the switching signal to adaptively adjust the noise covariance matrix. Finally, the experimental results show that the developed RAMS has a static measurement error of less than 0.05° and a dynamic measurement error of less than 1° in the range of ±180°. Full article

(This article belongs to the Special Issue Accuracy Improvement Methods and New Applications of Inertial-Based Navigation System)

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10 pages, 2328 KiB

Open AccessArticle

GNSS Based Low-Cost Magnetometer Calibration

by Ján Andel, Vojtech Šimák, Alžbeta Kanálikova and Rastislav Pirník

Sensors 2022, 22(21), 8447; https://doi.org/10.3390/s22218447 - 3 Nov 2022

Cited by 4 | Viewed by 3055

Abstract

With the development of MEMS sensors, the magnetometer has increasingly become a part of various wearable devices. The magnetometer measures the intensity of the magnetic field in all three axes, resulting in a 3D vector—direction and power. Calibration must be done before using a magnetometer, especially in wearable electronics, due to the low quality of the sensor and high proximity to other electromagnetic emission sources. Several magnetometer calibration algorithms exist in the literature, with most of them requiring multi-sided rotation. However, such calibration is highly impractical when the sensor is mounted on larger objects, e.g., vehicles, which cannot easily be rotated. Vehicles contain a large amount of ferromagnetic soft and hard material that affects the measured magnetic field. A magnetometer can be useful for an INS system in a car as long as it does not drift over time. This article describes how to calibrate a magnetometer using the GNSS motion vector. The calibration is performed using data from the initial section of the vehicle’s trajectory. The quality of the calibration is then validated using the remaining section of the trajectory, comparing the deviation between the azimuth obtained by GNSS and by the calibrated magnetometer. Based on the azimuth and speed of the vehicle, we predicted the position of the vehicle and plotted the prediction on the map. The experiment showed that such calibration is functional. The uncalibrated data were unusable due to the strong effect of ferromagnetic soft and hard materials in the vehicle. Full article

(This article belongs to the Special Issue Accuracy Improvement Methods and New Applications of Inertial-Based Navigation System)

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24 pages, 5723 KiB

Open AccessArticle

Autonomous Navigation of Unmanned Aircraft Using Space Target LOS Measurements and QLEKF

by Kai Xiong, Peng Zhou and Chunling Wei

Sensors 2022, 22(18), 6992; https://doi.org/10.3390/s22186992 - 15 Sep 2022

Cited by 4 | Viewed by 1915

Abstract

An autonomous navigation method based on the fusion of INS (inertial navigation system) measurements with the line-of-sight (LOS) observations of space targets is presented for unmanned aircrafts. INS/GNSS (global navigation satellite system) integration is the conventional approach to achieving the long-term and high-precision navigation of unmanned aircrafts. However, the performance of INS/GNSS integrated navigation may be degraded gradually in a GNSS-denied environment. INS/CNS (celestial navigation system) integrated navigation has been developed as a supplement to the GNSS. A limitation of traditional INS/CNS integrated navigation is that the CNS is not efficient in suppressing the position error of the INS. To solve the abovementioned problems, we studied a novel integrated navigation method, where the position, velocity and attitude errors of the INS were corrected using a star camera mounted on the aircraft in order to observe the space targets whose absolute positions were available. Additionally, a QLEKF (Q-learning extended Kalman filter) is designed for the performance enhancement of the integrated navigation system. The effectiveness of the presented autonomous navigation method based on the star camera and the IMU (inertial measurement unit) is demonstrated via CRLB (Cramer–Rao lower bounds) analysis and numerical simulations. Full article

(This article belongs to the Special Issue Accuracy Improvement Methods and New Applications of Inertial-Based Navigation System)

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16 pages, 4316 KiB

Open AccessArticle

Research on Nonlinear Compensation of the MEMS Gyroscope under Tiny Angular Velocity

by Chunhua Ren, Dongning Guo, Lu Zhang and Tianhe Wang

Sensors 2022, 22(17), 6577; https://doi.org/10.3390/s22176577 - 31 Aug 2022

Cited by 7 | Viewed by 2067

Abstract

The Micro-Electro-Mechanical System (MEMS) gyroscope has been widely used in various fields, but the output of the MEMS gyroscope has strong nonlinearity, especially in the range of tiny angular velocity. This paper proposes an adaptive Fourier series compensation method (AFCM) based on the steepest descent method and Fourier series residual correction. The proposed method improves the Fourier series fitting method according to the output characteristics of the MEMS gyroscope under tiny angular velocity. Then, the optimal weights are solved by the steepest descent method, and finally the fitting residuals are corrected by Fourier series to further improve the compensation accuracy. In order to verify the effectiveness of the proposed method, the angle velocity component of the earth’s rotation is used as the input of the MEMS gyroscope to obtain the output of the MEMS gyroscope under tiny angular velocities. Experimental characterization resulted in an input angular velocity between −0.0036°/s and 0.0036°/s, compared with the original data, the polynomial compensation method, and the Fourier series compensation method, and the output nonlinearity of the MEMS gyroscope was reduced from 1150.87 ppm, 641.13 ppm, and 250.55 ppm to 68.89 ppm after AFCM compensation, respectively, which verifies the effectiveness and superiority of the proposed method. Full article

(This article belongs to the Special Issue Accuracy Improvement Methods and New Applications of Inertial-Based Navigation System)

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12 pages, 2823 KiB

Open AccessArticle

Study of the Steady-State Operation of a Dual-Longitudinal-Mode and Self-Biasing Laser Gyroscope

by Jianning Liu, Jun Weng, Junbiao Jiang, Yujie Liu, Mingxing Jiao, Kai Zhao and Yi Zheng

Sensors 2022, 22(16), 6300; https://doi.org/10.3390/s22166300 - 22 Aug 2022

Cited by 2 | Viewed by 2768

Abstract

In order to stabilize the self-biasing state of a laser gyroscope, a dual-longitudinal-mode asymmetric frequency stabilization technique was studied. The special frequency stabilization is based on the accurate control of the intensity tuning curve in the prism ring laser. In this study, the effects of the ratio of the Ne isotopes, the inflation pressure, and the frequencies coupling on the intensity tuning curve in a laser gyro were examined. The profiles of the intensity tuning curve were simulated under the mixing ratios of Ne²⁰ and Ne²² of 1:1 and 7:3, and the inflation pressures were 350 Pa, 400 Pa, and 450 Pa. The mixing ratio of Ne²⁰ and Ne²⁷ was dealt with similarly. The method for precisely adjusting the profiles of the intensity tuning curve was analyzed. The profiles were verified by experiments under different isotope ratios and pressures. Finally, based on a prism ring laser with an optical length of 0.47 m, the proposed frequency stabilization method was preliminarily verified. Full article

(This article belongs to the Special Issue Accuracy Improvement Methods and New Applications of Inertial-Based Navigation System)

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24 pages, 16636 KiB

Open AccessArticle

One-Step Initial Alignment Algorithm for SINS in the ECI Frame Based on the Inertial Attitude Measurement of the CNS

by Jun Tang, Hongwei Bian, Heng Ma and Rongying Wang

Sensors 2022, 22(14), 5123; https://doi.org/10.3390/s22145123 - 8 Jul 2022

Cited by 7 | Viewed by 2073

Abstract

To solve the problem of high-precision and fast initial alignment for the Strapdown Inertial Navigation System (SINS) under both dynamic and static conditions, the high-precision attitude measured by the celestial navigation system (CNS) is used as the reference information for the initial alignment. The alignment algorithm is derived in the Earth-centered inertial (ECI) frame. Compared with the alignment algorithm in the navigation frame, it is independent of position parameters and avoids the influence of the approximate error caused by the dynamic deflection angle. In addition, hull deformation is considered in attitude optimal estimation, which can realize initial the alignment of the SINS installed in various parts of the carrier. On this basis, the velocity measurement information is added to the alignment process, which further improves the accuracy and speed of the initial alignment under static conditions. The experimental results show that the algorithms proposed in this paper have better performance in alignment accuracy, speed, and stability. The attitude and velocity matching algorithm in the ECI frame can achieve alignment accuracy better than 0.6′. The attitude matching algorithm in the ECI frame has better robustness and can be used for both dynamic and static conditions, which can achieve alignment accuracy better than 1.3′. Full article

(This article belongs to the Special Issue Accuracy Improvement Methods and New Applications of Inertial-Based Navigation System)

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23 pages, 9505 KiB

Open AccessArticle

A Method for Autonomous Multi-Motion Modes Recognition and Navigation Optimization for Indoor Pedestrian

by Zhengchun Wang, Zhi Xiong, Li Xing, Yiming Ding and Yinshou Sun

Sensors 2022, 22(13), 5022; https://doi.org/10.3390/s22135022 - 3 Jul 2022

Cited by 6 | Viewed by 2010

Abstract

The indoor navigation method shows great application prospects that is based on a wearable foot-mounted inertial measurement unit and a zero-velocity update principle. Traditional navigation methods mainly support two-dimensional stable motion modes such as walking; special tasks such as rescue and disaster relief, medical search and rescue, in addition to normal walking, are usually accompanied by running, going upstairs, going downstairs and other motion modes, which will greatly affect the dynamic performance of the traditional zero-velocity update algorithm. Based on a wearable multi-node inertial sensor network, this paper presents a method of multi-motion modes recognition for indoor pedestrians based on gait segmentation and a long short-term memory artificial neural network, which improves the accuracy of multi-motion modes recognition. In view of the short effective interval of zero-velocity updates in motion modes with fast speeds such as running, different zero-velocity update detection algorithms and integrated navigation methods based on change of waist/foot headings are designed. The experimental results show that the overall recognition rate of the proposed method is 96.77%, and the navigation error is 1.26% of the total distance of the proposed method, which has good application prospects. Full article

(This article belongs to the Special Issue Accuracy Improvement Methods and New Applications of Inertial-Based Navigation System)

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14 pages, 2567 KiB

Open AccessArticle

Research on the Error of Global Positioning System Based on Time Series Analysis

by Lijun Song, Lei Zhou, Peiyu Xu, Wanliang Zhao, Shaoliang Li and Zhe Li

Sensors 2022, 22(10), 3614; https://doi.org/10.3390/s22103614 - 10 May 2022

Cited by 1 | Viewed by 2533

Abstract

Due to the poor dynamic positioning precision of the Global Positioning System (GPS), Time Series Analysis (TSA) and Kalman filter technology are used to construct the positioning error of GPS. According to the statistical characteristics of the autocorrelation function and partial autocorrelation function of sample data, the Autoregressive (AR) model which is based on a Kalman filter is determined, and the error model of GPS is combined with a Kalman filter to eliminate the random error in GPS dynamic positioning data. The least square method is used for model parameter estimation and adaptability tests, and the experimental results show that the absolute value of the maximum error of longitude and latitude, the mean square error of longitude and latitude and average absolute error of longitude and latitude are all reduced, and the dynamic positioning precision after correction has been significantly improved. Full article

(This article belongs to the Special Issue Accuracy Improvement Methods and New Applications of Inertial-Based Navigation System)

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