A Universal Approach for Processing any MEMS Inertial Sensor Configuration for Land-Vehicle Navigation

2007 ◽  
Vol 60 (2) ◽  
pp. 233-245 ◽  
Author(s):  
Xiaoji Niu ◽  
Sameh Nasser ◽  
Chris Goodall ◽  
Naser El-Sheimy
Keyword(s):  
Inertial Sensor ◽  
Low Cost ◽  
Navigation Systems ◽  
Micro Electro Mechanical Systems ◽  
Land Vehicle ◽  
Mems Sensor ◽  
Navigation Algorithm ◽  
Main Challenge ◽  
Universal Approach ◽  
Sensor Configuration

Recent navigation systems integrating GPS with Micro-Electro-Mechanical Systems (MEMS) Inertial Measuring Units (IMUs) have shown promising results for several applications based on low-cost devices such as vehicular and personal navigation. However, as a trend in the navigation market, some applications require further reductions in size and cost. To meet such requirements, a MEMS full IMU configuration (three gyros and three accelerometers) may be simplified. In this context, different partial IMU configurations such as one gyro plus three accelerometers or one gyro plus two accelerometers could be investigated. The main challenge in this case is to develop a specific navigation algorithm for each configuration since this is a time-consuming and costly task. In this paper, a universal approach for processing any MEMS sensor configuration for land vehicular navigation is introduced. The proposed method is based on the assumption that the omitted sensors provide relatively less navigation information and hence, their output can be replaced by pseudo constant signals plus noise. Using standard IMU/GPS navigation algorithms, signals from existing sensors and pseudo signals for the omitted sensors are processed as a full IMU. The proposed approach is tested using land-vehicle MEMS/GPS data and implemented with different sensor configurations. Compared to the full IMU case, the results indicate the differences are within the expected levels and that the accuracy obtained meets the requirements of several land-vehicle applications.

scholarly journals Attitude Angle Compensation for a Synchronous Acquisition Method Based on an MEMS Sensor

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 483 ◽  
Author(s):  
Huanhuan Tian ◽  
Yixiao Liu ◽  
Jiqin Zhou ◽  
Ying Wang ◽  
Jing Wang ◽  
...  
Keyword(s):  
Measurement Accuracy ◽  
Inertial Sensor ◽  
Measuring Method ◽  
Frequency Measurement ◽  
Process Time ◽  
Navigation Systems ◽  
Micro Electro Mechanical Systems ◽  
Mems Sensor ◽  
Attitude Angle ◽  
Aircraft Navigation

As a new type of micro-electro-mechanical systems (MEMS) inertial sensor, the Quartz Vibrating Beam Accelerometer (QVBA) is widely used in intelligent sweeping robots, small aircraft, navigation systems, etc. For these applications, correcting and compensating the attitude angle with the result of acceleration plays an important role to improve the measurement accuracy. The synchronization error between the measurement of the accelerometer and gyroscope attitude angle has an adverse impact on the accuracy of the attitude angle. In this paper, a synchronous acquisition scheme of the accelerometer and gyroscope attitude angle in a strapdown inertial navigation system (SINS) is proposed. At the same time, to improve the sampling accuracy and the conversion speed of QVBA, an improved equal-precision frequency measuring method is also implemented in this paper. The hardware float point unit (FPU) is used to accelerate the calculation of the frequency measurement value. The long-term cumulative error of the frequency measurement value is less than 10 − 4 . The calculation process time from sampling to attitude angle compensation calculation is reduced by 40.8%. This work has played a very good role in improving the measurement accuracy and speed of the SINS.

scholarly journals INVESTIGATION OF DIFFERENT LOW-COST LAND VEHICLE NAVIGATION SYSTEMS BASED ON CPD SENSORS AND VEHICLE INFORMATION

2020 ◽  
Vol V-1-2020 ◽  
pp. 189-197
Author(s):  
M. Moussa ◽  
A. Moussa ◽  
M. Elhabiby ◽  
N. El-Sheimy
Keyword(s):  
Inertial Sensor ◽  
Low Cost ◽  
Integrated System ◽  
Steering Wheel ◽  
Vehicle Navigation ◽  
Portable Devices ◽  
Navigation Systems ◽  
Land Vehicle ◽  
Land Vehicle Navigation ◽  
Vehicle Information

Abstract. Recently, many companies and research centres have been working on research and development of navigation technologies for self-driving cars. Many navigation technologies were developed based on the fusion of various sensors. However, most of these techniques used expensive sensors and consequently increase the overall cost of such cars. Therefore, low-cost sensors are now a rich research topic in land vehicle navigation. Consumer Portable Devices (CPDs) such as smartphones and tablets are being widely used and contain many sensors (e.g. cameras, barometers, magnetometers, accelerometers, gyroscopes, and GNSS receivers) that can be used in the land vehicle navigation applications.This paper investigates various land vehicle navigation systems based on low-cost self-contained inertial sensors in CPD, vehicle information and on-board sensors with a focus on GNSS denied environment. Vehicle motion information such as forward speed is acquired from On-Board Diagnosis II (OBD-II) while the land vehicle heading change is estimated using CPD attached to the steering wheel. Additionally, a low-cost on-board GNSS/inertial integrated system is also employed. The paper investigates many navigation schemes such as different Dead Reckoning (DR) systems, Reduced Inertial Sensor System (RISS) based systems, and aided loosely coupled GNSS/inertial integrated system.An experimental road test is performed, and different simulated GNSS signal outages were applied to the data. The results show that the modified RISS system based on OBD-II velocity, onboard gyroscopes, accelerometers, and CPD-based heading change provides a better navigation estimation than the typical RISS system for 90s GNSS signal outage. On the other hand, typical inertial aided with CPD heading change, OBD-II velocity updates, and Non-Holonomic Constraint (NHC) provide the best navigation result.

GPS-based Land Vehicle Navigation System Assisted by a Low-Cost Gyro-Free INS Using Neural Network

2004 ◽  
Vol 57 (3) ◽  
pp. 417-428 ◽  
Author(s):  
Jau-Hsiung Wang ◽  
Yang Gao
Keyword(s):  
Neural Network ◽  
Navigation System ◽  
Urban Areas ◽  
Low Cost ◽  
Vehicle Navigation ◽  
Navigation Systems ◽  
Land Vehicle ◽  
Land Vehicle Navigation ◽  
Artificial Neural Network Ann ◽  
Signal Fading

GPS-based land vehicle navigation systems are subject to signal fading in urban areas and require aid from other enabling sensors. A low-cost gyro-free inertial navigation system (INS) without accumulated attitude errors and complicated initializations could be an effective solution to the problem. This paper investigates a Constrained Navigation Algorithm (CNA) and the Artificial Neural Network (ANN) technique to compensate velocity output from a gyro-free INS. The vehicle's heading will be calibrated by a full circle test so that the magnetometer's bias and scale factor error could be removed. Experiments with a vehicle driven over level terrain have been conducted to assess the performance of the compensated gyro-free INS solutions. The effect of the architecture of Neural Network on prediction performance has also been discussed as well as the applicability of the proposed solution to land vehicle navigation with GPS outages.

scholarly journals MEMS Inertial Sensor for Strata Stability Monitoring in Underground Mining: An Experimental Study

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Kaizhi Zhang ◽  
Songtao Ji ◽  
Yang Zhang ◽  
Jie Zhang ◽  
Ruikai Pan
Keyword(s):  
Underground Mining ◽  
Inertial Sensor ◽  
Low Cost ◽  
Equivalent Material ◽  
Ultimate State ◽  
Stability Monitoring ◽  
Mems Sensor ◽  
Sensor Module ◽  
Roof Strata ◽  
Stable Stage

To investigate the fracture and deformation characteristics of the strata in underground mining as well as the effectiveness and sensitivity of the MEMS inertial sensor for strata stability monitoring, a low cost, small size, and easy implementation inertial MEMS sensor module was redeveloped. Sensor modules were installed on roof strata in an underground mining equivalent material simulation experiment. Then, monitoring signal of two modules near the middle and end section of caving strata was processed. The processed signal presents stepped change, and each step consists a vibration stage and a stable stage. Further analysis of each stage, a strategy to estimate the deformation and stability of strata, can be reached: the duration of each vibration stage and complete stage with rising trend indicates that the deformation of strata is growing to the ultimate state. In this study, this method could recognize the destructive deformation of strata at least 1 hour before the strata caving.

scholarly journals Vehicle heading estimation of INS/magnetometer integrated system based on constant hard iron interference calibration

2021 ◽  
pp. 002029402110218
Author(s):  
Xufei Cui ◽  
Yibing Li ◽  
Qiuying Wang ◽  
Malek Karaim ◽  
Aboelmagd Noureldin
Keyword(s):  
Magnetic Field ◽  
Inertial Sensor ◽  
Low Cost ◽  
Ferromagnetic Material ◽  
Estimation Algorithm ◽  
Integrated System ◽  
Local Magnetic Field ◽  
Navigation Systems ◽  
Heading Estimation ◽  
Iron Interference

The integrated INS/magnetometer measurement is widely used in low-cost navigation systems. The integration has proven more effective in suppressing the divergence of heading than relying solely on a magnetometer because this is susceptible to local magnetic field interference, reducing heading accuracy. Magnetometers sense the local magnetic field that may be interfered by the nearby ferromagnetic material or strong electric currents. Hence, the magnetometer must be calibrated in the vehicle before use. When a magnetometer is installed near power components (engines, etc.), soft iron interference can be ignored. In the vehicle’s external environment, the time-varying hard iron interference can reach 100 times the strength of the geomagnetic field, meaning that a magnetometer cannot function efficiently because its accuracy is so reduced. Hence, the constant hard magnetic interference inside the vehicle is mainly concerned in this paper. An INS/Magnetometer heading estimation algorithm based on a two-stage Kalman filter is proposed to solve the problem by combining inertial sensor and magnetometer with attitude information. In the first stage filter, the constant hard iron interference is estimated by setting upward standing the three IMU axes. In the second stage filter, the INS/Magnetometer heading estimation is implemented. Finally, the results show that the algorithm improves the accuracy of vehicle heading calculations.

Performance Analysis of Medium Accuracy FOG-Based IMU Integrated with Optical Odometer for Land Vehicle Navigation

2012 ◽  
Vol 245 ◽  
pp. 334-339 ◽  
Author(s):  
Muhammad Ilyas ◽  
Ren Zhang ◽  
Qiu Shi Qian ◽  
Yun Chun Yang
Keyword(s):  
Kalman Filter ◽  
Performance Analysis ◽  
Fiber Optics ◽  
Low Cost ◽  
Measurement Noise ◽  
Vehicle Navigation ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Land Vehicle ◽  
Land Vehicle Navigation

The aim of this work is to research the feasibility of using optical odometer as the aided sensor for accuracy improvement of medium accuracy (FOG)-based IMU for land vehicle navigation. Usually GNSS is integrated with low cost SINS (strapdown inertial navigation systems) for land vehicle navigation but GNSS is not always reliable in land vehicle applications. The focus is on analysis of position and velocity accuracy of SINS/Odometer integration using close loop kalman filter. Integrated navigation algorithm for vehicle states estimation and correction have been designed and implemented. The measurement error model for odometer in navigation frame is developed for Kalman filter implementation. As the prior knowledge of measurement noise which represents the stochastic properties of odometer is not exactly known, so an adaptive Kalman filter (AKF) is also proposed for online estimation of the measurement noise matrix in order to improve the accuracy. For the performance analysis of the designed system, field test is carried out and results show that the accuracy of the medium accuracy fiber optics gyro (FOG)-based SINS is improved and the systems is capable for land vehicle navigation application

scholarly journals Performance Assessment of an Ultra Low-Cost Inertial Measurement Unit for Ground Vehicle Navigation

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3865 ◽  
Author(s):  
Rodrigo Gonzalez ◽  
Paolo Dabove
Keyword(s):  
Automotive Industry ◽  
Inertial Measurement Unit ◽  
Microelectromechanical Systems ◽  
Inertial Sensor ◽  
Low Cost ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Mass Market ◽  
Inertial Measurement ◽  
Mems Imu

Nowadays, navigation systems are becoming common in the automotive industry due to advanced driver assistance systems and the development of autonomous vehicles. The MPU-6000 is a popular ultra low-cost Microelectromechanical Systems (MEMS) inertial measurement unit (IMU) used in several applications. Although this mass-market sensor is used extensively in a variety of fields, it has not caught the attention of the automotive industry. Moreover, a detailed performance analysis of this inertial sensor for ground navigation systems is not available in the previous literature. In this work, a deep examination of one MPU-6000 IMU as part of a low-cost navigation system for ground vehicles is provided. The steps to characterize the performance of the MPU-6000 are divided in two phases: static and kinematic analyses. Besides, an additional MEMS IMU of superior quality is also included in all experiments just for the purpose of comparison. After the static analysis, a kinematic test is conducted by generating a real urban trajectory registering an MPU-6000 IMU, the higher-grade MEMS IMU, and two GNSS receivers. The kinematic trajectory is divided in two parts, a normal trajectory with good satellites visibility and a second part where the Global Navigation Satellite System (GNSS) signal is forced to be lost. Evaluating the attitude and position inaccuracies from these two scenarios, it is concluded in this preliminary work that this mass-market IMU can be considered as a convenient inertial sensor for low-cost integrated navigation systems for applications that can tolerate a 3D position error of about 2 m and a heading angle error of about 3 °.

scholarly journals Robust Special Strategies Re Sampling for Mobile Inertial Navigation Systems

2019 ◽  
Vol 9 (2) ◽  
pp. 3196-3204
Keyword(s):  
Particle Filtering ◽  
Inertial Sensor ◽  
Low Cost ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Computing Platform ◽  
Mobile Navigation ◽  
Global Positioning ◽  
Particle Measurement ◽  
Special Strategy

The mobile navigation services in an obstructed area can be extremely challenging especially if the Global Positioning System (GPS) is blocked. In such conditions, users will find it difficult to navigate directly on-site. This needs to use inertial sensor in order to determine the location as standalone, low cost and ubiquity. However, the usage of accurate inertial sensor and fast localization module in the system would lead the phenomenon of sample impoverishment, which it is contribute computation burden to the system. There are different situation of the sample impoverishment, and the solution by using special strategies resampling algorithm cannot be used or fitted in different cases in altogether. Adaptations relating to particle filtering attribute need to be made to the algorithm in order to make resampling more intelligent, reliable and robust. In this paper, we are proposes a robust special strategy resampling algorithm by adapting particle filtering attribute such as; noise and particle measurement. This adaptation is used to counteract sample impoverishment in different cases in altogether. Finally, the paper presents the proposed solution can survive in three (3) types of sample impoverishment situation inside mobile computing platform.

scholarly journals Low-Cost MEMS-Based NARX Model for GPS-Denied Areas

2020 ◽  
pp. 58-70
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Autonomous Vehicle ◽  
Field Tests ◽  
Micro Electro Mechanical System ◽  
Integrated System ◽  
Main Challenge ◽  
Autonomous Vehicle Navigation ◽  
Narx Model

Autonomous vehicle navigation has witnessed a huge revolutionary revision regarding development in Micro-Electro Mechanical System (MEMS) technology. Most recently, Strapdown Inertial Navigation System (SDINS) has successfully been integrated with Global Positioning System (GPS). However, different grades of MEMS inertial sensors are available and choosing the convenient grade is quite important. Noises in inertial sensor are mostly treated through de-noising the additive errors to improve the precision of SDINS output. Unfortunately, integration in SDINS mechanization causes a growing in SDINS error output which considered the main challenge in integrating MEMS inertial sensors with GPS. This paper aims to promote the long-term performance of the MEMS-SDINS/GPS integrated system. A new integrated structure is proposed to model the nonlinearities that exist in SDINS dynamics in addition to the error uncertainty in the inertial sensors’ measurements. A robust Nonlinear AutoRegressive models with eXogenous inputs (NARX) based algorithm are designed for data fusion in the proposed GPS/INS integrated system. Validation for the proposed integrated system has been carried out using different field tests data in order to assess the accuracy of the system during GPS denied environment. The results obtained demonstrate that the proposed NARX model is applicative and satisfactory which shows a desired prediction performance.

scholarly journals Fusing Stereo Camera and Low-Cost Inertial Measurement Unit for Autonomous Navigation in a Tightly-Coupled Approach

2014 ◽  
Vol 68 (3) ◽  
pp. 434-452 ◽  
Author(s):  
Zhiwen Xian ◽  
Xiaoping Hu ◽  
Junxiang Lian
Keyword(s):  
Autonomous Navigation ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Three Dimensional ◽  
Fast Response ◽  
Location Estimation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Stereo Camera

Exact motion estimation is a major task in autonomous navigation. The integration of Inertial Navigation Systems (INS) and the Global Positioning System (GPS) can provide accurate location estimation, but cannot be used in a GPS denied environment. In this paper, we present a tight approach to integrate a stereo camera and low-cost inertial sensor. This approach takes advantage of the inertial sensor's fast response and visual sensor's slow drift. In contrast to previous approaches, features both near and far from the camera are simultaneously taken into consideration in the visual-inertial approach. The near features are parameterised in three dimensional (3D) Cartesian points which provide range and heading information, whereas the far features are initialised in Inverse Depth (ID) points which provide bearing information. In addition, the inertial sensor biases and a stationary alignment are taken into account. The algorithm employs an Iterative Extended Kalman Filter (IEKF) to estimate the motion of the system, the biases of the inertial sensors and the tracked features over time. An outdoor experiment is presented to validate the proposed algorithm and its accuracy.

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