A self-alignment method for gravitational apparent acceleration identification and accelerometer bias estimation based on repeated navigation solution

2021 ◽  
Vol 92 (6) ◽  
pp. 064505
Author(s):  
Yongjiang Huang ◽  
Xixiang Liu ◽  
Zixuan Wang ◽  
Xiaoqiang Wu ◽  
Wei Liu
Keyword(s):  
Alignment Method ◽  
Bias Estimation ◽  
Navigation Solution ◽  
Apparent Acceleration

scholarly journals A Method for Gravitational Apparent Acceleration Identification and Accelerometer Bias Estimation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 38115-38122 ◽  
Author(s):  
Xixiang Liu ◽  
Songbing Wang ◽  
Xiaole Guo ◽  
Wenqiang Yang ◽  
Guangfu Xu
Keyword(s):  
Bias Estimation ◽  
Apparent Acceleration

scholarly journals Network-based Collaborative Navigation in GPS-Denied Environment

2012 ◽  
Vol 65 (3) ◽  
pp. 445-457 ◽  
Author(s):  
Jong Ki Lee ◽  
Dorota A. Grejner-Brzezinska ◽  
Charles Toth
Keyword(s):  
Kalman Filter ◽  
Least Squares ◽  
Primary Source ◽  
Entire Group ◽  
Navigation Systems ◽  
Bias Estimation ◽  
Positioning Systems ◽  
Navigation Solution ◽  
Least Squares Solution ◽  
Navigation Algorithms

Global Positioning System (GPS) has been used as a primary source of navigation in land and airborne applications. However, challenging environments cause GPS signal blockage or degradation, and prevent reliable and seamless positioning and navigation using GPS only. Therefore, multi-sensor based navigation systems have been developed to overcome the limitations of GPS by adding some forms of augmentation. The next step towards assured robust navigation is to combine information from multiple ground-users, to further improve the chance of obtaining reliable navigation and positioning information. Collaborative (or cooperative) navigation can improve the individual navigation solution in terms of both accuracy and coverage, and may reduce the system's design cost, as equipping all users with high performance multi-sensor positioning systems is not cost effective. Generally, ‘Collaborative Navigation’ uses inter-nodal range measurements between platforms (users) to strengthen the navigation solution. In the collaborative navigation approach, the inter-nodal distance vectors from the known or more accurate positions to the unknown locations can be established. Therefore, the collaborative navigation technique has the advantage in that errors at the user's position can be compensated by other known (or more accurate) positions of other platforms, and may result in the improvement of the navigation solutions for the entire group of users. In this paper, three statistical network-based collaborative navigation algorithms, the Restricted Least-Squares Solution (RLESS), the Stochastic Constrained Least-Squares Solution (SCLESS) and the Best Linear Minimum Partial Bias Estimation (BLIMPBE) are proposed and compared to the Kalman filter. The proposed statistical collaborative navigation algorithms for network solution show better performance than the Kalman filter.

A fast compass alignment method for SINS based on saved data and repeated navigation solution

Measurement ◽  
2013 ◽  
Vol 46 (10) ◽  
pp. 3836-3846 ◽  
Author(s):  
Xixiang Liu ◽  
Xiaosu Xu ◽  
Lihui Wang ◽  
Yiting Liu
Keyword(s):  
Alignment Method ◽  
Navigation Solution

scholarly journals S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices

Geomatics ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 148-176
Author(s):  
Maan Khedr ◽  
Naser El-Sheimy
Keyword(s):  
Smart Cities ◽  
Dead Reckoning ◽  
Human Motion ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Navigation Systems ◽  
Short Term ◽  
Navigation Solution ◽  
Correction Technique ◽  
Pedestrian Dead Reckoning

Mobile location-based services (MLBS) are attracting attention for their potential public and personal use for a variety of applications such as location-based advertisement, smart shopping, smart cities, health applications, emergency response, and even gaming. Many of these applications rely on Inertial Navigation Systems (INS) due to the degraded GNSS services indoors. INS-based MLBS using smartphones is hindered by the quality of the MEMS sensors provided in smartphones which suffer from high noise and errors resulting in high drift in the navigation solution rapidly. Pedestrian dead reckoning (PDR) is an INS-based navigation technique that exploits human motion to reduce navigation solution errors, but the errors cannot be eliminated without aid from other techniques. The purpose of this study is to enhance and extend the short-term reliability of PDR systems for smartphones as a standalone system through an enhanced step detection algorithm, a periodic attitude correction technique, and a novel PCA-based motion direction estimation technique. Testing shows that the developed system (S-PDR) provides a reliable short-term navigation solution with a final positioning error that is up to 6 m after 3 min runtime. These results were compared to a PDR solution using an Xsens IMU which is known to be a high grade MEMS IMU and was found to be worse than S-PDR. The findings show that S-PDR can be used to aid GNSS in challenging environments and can be a viable option for short-term indoor navigation until aiding is provided by alternative means. Furthermore, the extended reliable solution of S-PDR can help reduce the operational complexity of aiding navigation systems such as RF-based indoor navigation and magnetic map matching as it reduces the frequency by which these aiding techniques are required and applied.

Sign in / Sign up

Export Citation Format

Share Document