scholarly journals Novel In-flight Coarse Alignment of Low-cost Strapdown Inertial Navigation System for Unmanned Aerial Vehicle Applications

2016 ◽  
Vol 59 (1) ◽  
pp. 10-17 ◽  
Author(s):  
Dingjie WANG ◽  
Liang CHEN ◽  
Jie WU
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Low Cost ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Aerial Vehicle ◽  
Strapdown Inertial Navigation

An Augmented Strapdown Inertial Navigation System using Jerk and Jounce of Motion for a Flying Robot

2017 ◽  
Vol 70 (4) ◽  
pp. 907-926 ◽  
Author(s):  
Milad Bayat ◽  
MA Amiri Atashgah
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Low Cost ◽  
Inertial Navigation ◽  
Satellite System ◽  
Strapdown Inertial Navigation System ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Flying Robot ◽  
Strapdown Inertial Navigation

This paper offers an algorithm for enhancement of positioning accuracy of a quad-rotor flying robot, based on jerk and jounce of motion. The suggested method utilises the first and second numerical derivatives of the vehicle's acceleration and augments the mathematical model in the estimation process. For this purpose, the Kalman Filter (KF) is implemented for integration of a Strapdown Inertial Navigation System (SINS) and Global Navigation Satellite System (GNSS). The required data are collected from a low-cost/quality Micro Electromechanical Sensors (MEMS) during an assisted flight. For increasing the precision and accuracy of the collected data, all instruments including accelerometers, gyroscopes and magnetometers are calibrated before the experiments. Moreover, to reduce and limit the measurement noises of the MEMS sensor, a low-pass filter is applied; this is while sensors in the autopilot are affected by high levels of noise and drift, which makes them inappropriate for accurate positioning. The experimental results exhibit an improvement in positioning and altitude sensing through augmentation of the loosely coupled SINS/GNSS navigation method.

Observability and Estimation Error Analysis of the Initial Fine Alignment Filter for Nonleveling Strapdown Inertial Navigation System

2012 ◽  
Vol 135 (2) ◽  
Author(s):  
Seong Yun Cho ◽  
Hyung Keun Lee ◽  
Hung Kyu Lee
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
The State ◽  
State Model ◽  
Low Grade ◽  
State Variables ◽  
Estimation Errors ◽  
Strapdown Inertial Navigation

In this paper, performance of the initial fine alignment for the stationary nonleveling strapdown inertial navigation system (SDINS) containing low-grade gyros is analyzed. First, the observability is analyzed by conducting a rank test of an observability matrix and by investigating the normalized error covariance of the extended Kalman filter based on the ten-state model. The results show that the accelerometer biases on horizontal axes are unobservable. Second, the steady-state estimation errors of the state variables are derived using the observability equation. It is verified that the estimates of the state variables have errors due to the unobservable state variables and nonleveling attitude angles of a vehicle containing the SDINS. Especially, this paper shows that the larger the attitude angles of the vehicle are, the greater the estimation errors are. Finally, it is shown that the performance of the eight-state model excluding the two unobservable state variables is better than that of the ten-state model in the fine alignment by a Monte Carlo simulation.

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