scholarly journals In-Motion Iterative Fine Alignment Algorithm for On-Board Vehicular Odometer-Aided SINS

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Baichun Gong ◽  
Chenglong He ◽  
Xiaoyue Wang ◽  
Xin Li
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Dead Reckoning ◽  
Alignment Algorithm ◽  
Map Matching ◽  
Error Angle ◽  
Landmark Point ◽  
Principle Of Similarity ◽  
Digital Simulations

This research proposes a novel in-motion fine alignment algorithm for vehicular dead reckoning (DR) with odometer-aided strapdown inertial navigation system (SINS) while the map matching result is used for a group of landmark points to estimate misalignment angles. The proposed algorithm is designed based on principle of similarity, that is, trajectory of DR is similar to the true trajectory that the main difference between these two trajectories is rotation and scale. Further, the results from map matching are introduced as a group of landmark points to estimate the residual of azimuth error angle after coarse alignment and the scale factor error of the odometer. It is theoretically proved that the alignment effectiveness based on the results from map matching is equivalent to that on single zero error landmark point. Finally, digital simulations are conducted to verify the presented algorithm and test the performance.

Self-Alignment Algorithm for Strapdown Inertial Navigation System under Strong Flurry Interference

2013 ◽  
Vol 347-350 ◽  
pp. 3667-3671 ◽  
Author(s):  
Yue Gang Wang ◽  
Jia Sheng Yang
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
The Body ◽  
Alignment Algorithm ◽  
Result Show ◽  
Linear Movement ◽  
Initial Attitude ◽  
Strapdown Inertial Navigation

For the strong flurry interrupting, the body will suffer large swaying motion when it is in erecting state ,the output of its strapdown inertial navigation system (SINS) will be disturbed for the high gravitational. center of IMU, the conventional methods are difficult to achieve alignment rapidly and accurately, to solve this problem, an anti-interference self-alignment algorithm for SINS which under strong flurry is presented, which utilizes the continuous attitude update in inertial reference frame to record the attitude changes caused by sway interrupt to remove the angular interrupting, and uses the characteristics that the body exists a shake center whose speed is zero to remove the linear movement interrupting by acquiring the equivalent specific force of the shake center, and then uses the estimation of the initial attitude to determinate the attitude of the body. The simulation result show that the presented algorithm can accomplish alignment quickly even in the presence of strong flurry interference without coarse alignment phase.

Navigation Based on Sensors in Smartphones

2018 ◽  
pp. 368-396
Author(s):  
Guenther Retscher ◽  
Allison Kealy
Keyword(s):  
Navigation System ◽  
Inertial Navigation ◽  
Dead Reckoning ◽  
Activity Monitoring ◽  
Navigation Systems ◽  
Location Tracking ◽  
Map Matching ◽  
Complex Environments ◽  
Positioning Systems ◽  
Cooperative Positioning

With the increasing ubiquity of smartphones and tablets, users are now routinely carrying a variety of sensors with them wherever they go. These devices are enabling technologies for ubiquitous computing, facilitating continuous updates of a user's context. They have built-in MEMS-based accelerometers for ubiquitous activity monitoring and there is a growing interest in how to use these together with gyroscopes and magnetometers to build dead reckoning (DR) systems for location tracking. Navigation in complex environments is needed mainly by consumer users, private vehicles, and pedestrians. Therefore, the navigation system has to be small, easy to use, and have reasonably low levels of power consumption and price. The technologies and techniques discussed here include the fusion of inertial navigation (IN) and other sensors, positioning based on signals from wireless networks (such as Wi-Fi), image-based methods, cooperative positioning systems, and map matching (MM). The state-of-the-art of MEMS-based location sensors and their integration into modern navigation systems are also presented.

scholarly journals An Improved Strapdown Inertial Navigation System Initial Alignment Algorithm for Unmanned Vehicles

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3297 ◽  
Author(s):  
Ya Zhang ◽  
Fei Yu ◽  
Wei Gao ◽  
Yanyan Wang
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Unmanned Vehicles ◽  
Frequency Noise ◽  
Alignment Algorithm ◽  
Initial Alignment ◽  
System Nonlinearity ◽  
Strapdown Inertial Navigation

Along with the development of computer technology and informatization, the unmanned vehicle has become an important equipment in military, civil and some other fields. The navigation system is the basis and core of realizing the autonomous control and completing the task for unmanned vehicles, and the Strapdown Inertial Navigation System (SINS) is the preferred due to its autonomy and independence. The initial alignment technique is the premise and the foundation of the SINS, whose performance is susceptible to system nonlinearity and uncertainty. To improving system performance for SINS, an improved initial alignment algorithm is proposed in this manuscript. In the procedure of this presented initial alignment algorithm, the original signal of inertial sensors is denoised by utilizing the improved signal denoising method based on the Empirical Mode Decomposition (EMD) and the Extreme Learning Machine (ELM) firstly to suppress the high-frequency noise on coarse alignment. Afterwards, the accuracy and reliability of initial alignment is further enhanced by utilizing an improved Robust Huber Cubarure Kalman Filer (RHCKF) method to minimize the influence of system nonlinearity and uncertainty on the fine alignment. In addition, real tests are used to verify the availability and superiority of this proposed initial alignment algorithm.

scholarly journals Accurate integrated position and orientation method for vehicles based on strapdown inertial navigation system /Doppler radar

2018 ◽  
Vol 51 (9-10) ◽  
pp. 431-442 ◽  
Author(s):  
Yang Bo ◽  
Wang Yue-gang ◽  
Xue Liang ◽  
Shan Bin ◽  
Wang Bao-cheng
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Doppler Radar ◽  
Inertial Navigation ◽  
Dead Reckoning ◽  
Strapdown Inertial Navigation System ◽  
Orientation Method ◽  
Integration Mode ◽  
Position And Orientation ◽  
Strapdown Inertial Navigation

In order to realize maneuver combat in the modern warfare, some special military vehicles require the ability of determining their position and orientation rapidly and accurately, and the position and orientation system should be highly autonomous and have strong anti-jamming capability. So a high-accuracy independent position and orientation method for vehicles that utilizes strapdown inertial navigation system/Doppler radar is presented in this article. Laser gyroscopes in strapdown inertial navigation system and Doppler radar are adopted to develop a dead-reckoning system for vehicles. Subsequently, the attitude, velocity,and position-updating algorithms of dead-reckoning system are designed. The error sources of dead-reckoning system are analyzed to establish the system error model, including the attitude error equations of the mathematical platform, velocity error equations, and position error equations. The errors of strapdown inertial navigation system and deadreckoning system are selected as system states of the integrated position and orientation method. The difference between the attitude output of strapdown inertial navigation system and that of dead-reckoning system, and the difference between the position output of strapdown inertial navigation system and that of dead-reckoning system are chosen as the measurements of integrated position and orientation. Then, Kalman filter is adopted to design the filtering algorithm of integrated position and orientation. In the end, the integrated position and orientation method is validated by simulation experiment and vehicular experiment. The experimental results show that strapdown inertial navigation system/Doppler radar integration can realize accurate positioning and orientation for a long time, and the accuracy of attitude/position integration mode is significantly higher than that of velocity/position integration mode. Therefore, the former integration mode is more suitable for accurate position and orientation for vehicles.

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