scholarly journals Augmented Kalman Filter and Map Matching for 3D RISS/GPS Integration for Land Vehicles

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Matthew Cossaboom ◽  
Jacques Georgy ◽  
Tashfeen Karamat ◽  
Aboelmagd Noureldin
Keyword(s):  
Kalman Filter ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Map Matching ◽  
Loosely Coupled ◽  
The Road ◽  
Land Vehicles ◽  
Global Positioning ◽  
Reduced Inertial Sensor System

Owing to their complimentary characteristics, global positioning system (GPS) and inertial navigation system (INS) are integrated, traditionally through Kalman filter (KF), to obtain improved navigational solution. To reduce the overall cost of the system, microelectromechanical system- (MEMS-) based INS is utilized. One of the approaches is to reduce the number of low-cost inertial sensors, decreasing their error contribution which leads to a reduced inertial sensor system (RISS). This paper uses KF to integrate GPS and 3D RISS in a loosely coupled fashion to enhance navigational solution while further improvement is achieved by augmenting it with map matching (MM). The 3D RISS consists of only one gyroscope and two accelerometers along with the vehicle’s built-in odometer. MM limits the error growth during GPS outages by restricting the predicted positions to the road networks. The performance of proposed method is compared with KF-only 3D RISS/GPS integration to demonstrate the efficacy of the proposed technique.

Global Positioning System (GPS): A Low-Cost Velocity Sensor for Correcting Inertial Sensor Errors on Ground Vehicles

2004 ◽  
Vol 126 (2) ◽  
pp. 255-264 ◽  
Author(s):  
David M. Bevly
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Positioning System ◽  
Ground Vehicles ◽  
Velocity Sensor ◽  
Global Positioning ◽  
Sensor Errors

This paper demonstrates the ability of a standard low-cost Global Positioning System (GPS) receiver to reduce errors inherent in low-cost accelerometers and rate gyroscopes used on ground vehicles. Specifically GPS velocity is used to obtain vehicle course, velocity, and road grade, as well as to correct inertial sensors errors, providing accurate longitudinal and lateral acceleration, and pitch, roll, and yaw angular velocities. Additionally, it is shown that transient changes in sideslip (or lateral velocity), roll, and pitch angles can be measured. The method utilizes GPS velocity measurements to determine the inertial sensor errors using a kinematic Kalman Filter estimator. Simple models of the inertial sensors, which take into account the sensor noise and bias drift properties, are developed and used to design the estimator. Based on the characteristics of low-cost GPS receivers and IMU sensors, this paper presents the achievable performance of the combined system using the covariance analysis from the Kalman filter. Subsequent simulations and experiments validate both the error analysis and the methodology for utilizing GPS as a velocity sensor for correcting low-cost inertial sensor errors and providing critical vehicle state measurements.

scholarly journals Adaptive Filtering on GPS-Aided MEMS-IMU for Optimal Estimation of Ground Vehicle Trajectory

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5357 ◽  
Author(s):  
Haseeb Ahmed ◽  
Ihsan Ullah ◽  
Uzair Khan ◽  
Muhammad Bilal Qureshi ◽  
Sajjad Manzoor ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Autonomous Navigation ◽  
Inertial Sensors ◽  
Low Cost ◽  
Optimal Estimation ◽  
Cost Effective ◽  
Estimation Accuracy ◽  
Ground Vehicle ◽  
Navigation Data ◽  
Mems Imu

Fusion of the Global Positioning System (GPS) and Inertial Navigation System (INS) for navigation of ground vehicles is an extensively researched topic for military and civilian applications. Micro-electro-mechanical-systems-based inertial measurement units (MEMS-IMU) are being widely used in numerous commercial applications due to their low cost; however, they are characterized by relatively poor accuracy when compared with more expensive counterparts. With a sudden boom in research and development of autonomous navigation technology for consumer vehicles, the need to enhance estimation accuracy and reliability has become critical, while aiming to deliver a cost-effective solution. Optimal fusion of commercially available, low-cost MEMS-IMU and the GPS may provide one such solution. Different variants of the Kalman filter have been proposed and implemented for integration of the GPS and the INS. This paper proposes a framework for the fusion of adaptive Kalman filters, based on Sage-Husa and strong tracking filtering algorithms, implemented on MEMS-IMU and the GPS for the case of a ground vehicle. The error models of the inertial sensors have also been implemented to achieve reliable and accurate estimations. Simulations have been carried out on actual navigation data from a test vehicle. Measurements were obtained using commercially available GPS receiver and MEMS-IMU. The solution was shown to enhance navigation accuracy when compared to conventional Kalman filter.

A rapid seismic data calibration technique using integrated micro-electro-mechanical system inertial sensor groups for a 3C vertical seismic profile

Geophysics ◽  
2017 ◽  
Vol 82 (6) ◽  
pp. P109-P118
Author(s):  
Huailiang Li ◽  
Xianguo Tuo ◽  
Tong Shen ◽  
Mark Julian Henderson ◽  
Jérémie Courtois
Keyword(s):  
Mechanical System ◽  
Seismic Data ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Measuring Method ◽  
Seismic Profile ◽  
Micro Electro Mechanical System ◽  
Vertical Seismic Profile ◽  
Sensor Package

Calibration of 3C vertical seismic profile (VSP) data is an exciting challenge because the orientation of the tool is random when only seismic data are considered. We have augmented the sensor package on the VSP tool with micro-electro-mechanical system (MEMS) inertial sensors and applied a gesture measuring method to determine the tool orientation and calibration. This technique can quickly produce high precision, orientation, and angle information when integrated with the seismometer. The augmented sensor package consists of a low-cost triaxial MEMS gyroscope, an electronic compass, and an accelerometer. The technique to process the gesture information is based on the OpenGL software for 3D modeling. We have tested this approach on a large number of field data sets and it appeared to be faster and more reliable than other approaches.

Kalman filter-based yaw angle estimation by fusing inertial and magnetic sensing: a case study using low cost sensors

Sensor Review ◽  
2015 ◽  
Vol 35 (3) ◽  
pp. 244-250 ◽  
Author(s):  
Pedro Neto ◽  
Nuno Mendes ◽  
A. Paulo Moreira
Keyword(s):  
Kalman Filter ◽  
Inertial Sensor ◽  
Low Cost ◽  
Human Robot Interaction ◽  
Magnetic Sensor ◽  
Robot Interaction ◽  
Magnetic Sensing ◽  
Content Type ◽  
Reliable Estimation ◽  
Yaw Angle

Purpose – The purpose of this paper is to achieve reliable estimation of yaw angles by fusing data from low-cost inertial and magnetic sensing. Design/methodology/approach – In this paper, yaw angle is estimated by fusing inertial and magnetic sensing from a digital compass and a gyroscope, respectively. A Kalman filter estimates the error produced by the gyroscope. Findings – Drift effect produced by the gyroscope is significantly reduced and, at the same time, the system has the ability to react quickly to orientation changes. The system combines the best of each sensor, the stability of the magnetic sensor and the fast response of the inertial sensor. Research limitations/implications – The system does not present a stable behavior in the presence of large vibrations. Considerable calibration efforts are needed. Practical implications – Today, most of human–robot interaction technologies need to have the ability to estimate orientation, especially yaw angle, from small-sized and low-cost sensors. Originality/value – Existing methods for inertial and magnetic sensor fusion are combined to achieve reliable estimation of yaw angle. Experimental tests in a human–robot interaction scenario show the performance of the system.

scholarly journals A Tightly Coupled Positioning Solution for Land Vehicles in Urban Canyons

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xu Li ◽  
Rong Jiang ◽  
Xianghui Song ◽  
Bin Li
Keyword(s):  
Urban Areas ◽  
Unscented Kalman Filter ◽  
Inertial Sensor ◽  
Low Cost ◽  
Information Source ◽  
Satellite Measurement ◽  
3D Navigation ◽  
Filter Model ◽  
Land Vehicles ◽  
Tightly Coupled

The integration between Global Navigation Satellite System (GNSS) and on-board sensors is widely used for vehicle positioning. However, as the main information source in the integration, the positioning performance of single- or multiconstellation GNSSs is severely degraded in urban canyons due to the effects of Non-Line-Of-Sight (NLOS) and multipath propagations. How to mitigate such effects is vital to achieve accurate positioning performance in urban canyons. This paper proposes a tightly coupled positioning solution for land vehicles, fusing dual-constellation GNSSs with other low-cost complementary sensors. First, the nonlinear filter model is established based on a cost-effective reduced inertial sensor system with 3D navigation solution. Then, an adaptive fuzzy unscented Kalman filter (AF-UKF) algorithm is developed to achieve the global fusion. In the implementation of AF-UKF, the fuzzy calibration logic (FCL) is designed and introduced to adaptively adjust the dependence on each received satellite measurement to effectively mitigate the NLOS and multipath interferences in urban areas. Finally, the proposed solution is evaluated through experiments. The results validate the feasibility and effectiveness of the proposed solution.

Human Arm Motion Tracking by Orientation-Based Fusion of Inertial Sensors and Kinect Using Unscented Kalman Filter

2016 ◽  
Vol 138 (9) ◽  
Author(s):  
Arash Atrsaei ◽  
Hassan Salarieh ◽  
Aria Alasty
Keyword(s):  
Kalman Filter ◽  
Motion Capture ◽  
Motion Tracking ◽  
Inertial Sensors ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Human Motion ◽  
Home Based ◽  
Human Arm ◽  
Arm Motion

Due to various applications of human motion capture techniques, developing low-cost methods that would be applicable in nonlaboratory environments is under consideration. MEMS inertial sensors and Kinect are two low-cost devices that can be utilized in home-based motion capture systems, e.g., home-based rehabilitation. In this work, an unscented Kalman filter approach was developed based on the complementary properties of Kinect and the inertial sensors to fuse the orientation data of these two devices for human arm motion tracking during both stationary shoulder joint position and human body movement. A new measurement model of the fusion algorithm was obtained that can compensate for the inertial sensors drift problem in high dynamic motions and also joints occlusion in Kinect. The efficiency of the proposed algorithm was evaluated by an optical motion tracker system. The errors were reduced by almost 50% compared to cases when either inertial sensor or Kinect measurements were utilized.

Research on Data Fusion Technology of Body Posture Detection Based on Kalman Filter

2014 ◽  
Vol 668-669 ◽  
pp. 1003-1006 ◽  
Author(s):  
Xian Wei Wang ◽  
Fu Cheng Cao
Keyword(s):  
Kalman Filter ◽  
Attitude Determination ◽  
Inertial Sensors ◽  
Low Cost ◽  
Micro Electro Mechanical System ◽  
Angle Measurement ◽  
The Body ◽  
Body Posture ◽  
Posture Estimation ◽  
The Impact

This paper discusses the body posture detection problem using low cost Micro-Electro-Mechanical System (MEMS) inertial sensors, for which a complementary sensor fusion solution is proposed. Considering the impact from the noise and bias drifts, through Kalman filter to complete the multi-sensor information fusion, achieved an accurate attitude determination. The experimental results show that, after using Kalman filtering algorithm to fuse acceleration sensor and signal gyroscope, it can effectively eliminate the accumulative error and significantly better dynamic characteristics of attitude angle measurement, Improving the reliability and accuracy of body posture estimation.

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