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A Self-Alignment Algorithm for SINS Based on Gravitational Apparent Motion and Sensor Data Denoising.

Liu Y, Xu X, Liu X, Yao Y, Wu L, Sun J - Sensors (Basel) (2015)

Bottom Line: Initial alignment is always a key topic and difficult to achieve in an inertial navigation system (INS).Aiming to resolve this problem, an online sensor data denoising method based on a Kalman filter is proposed and a novel reconstruction method for apparent motion is designed to avoid the collinearity among vectors participating in the alignment solution.The accuracy can either reach or approach the theoretical values determined by sensor precision under static or swinging conditions.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China,. gcdlyt1985@163.com.

ABSTRACT
Initial alignment is always a key topic and difficult to achieve in an inertial navigation system (INS). In this paper a novel self-initial alignment algorithm is proposed using gravitational apparent motion vectors at three different moments and vector-operation. Simulation and analysis showed that this method easily suffers from the random noise contained in accelerometer measurements which are used to construct apparent motion directly. Aiming to resolve this problem, an online sensor data denoising method based on a Kalman filter is proposed and a novel reconstruction method for apparent motion is designed to avoid the collinearity among vectors participating in the alignment solution. Simulation, turntable tests and vehicle tests indicate that the proposed alignment algorithm can fulfill initial alignment of strapdown INS (SINS) under both static and swinging conditions. The accuracy can either reach or approach the theoretical values determined by sensor precision under static or swinging conditions.

No MeSH data available.


Trajectory of the gravitational apparent motion in inertial space.
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sensors-15-09827-f002: Trajectory of the gravitational apparent motion in inertial space.

Mentions: The concept of apparent motion in INS is initially used to describe the characteristics of gyros. Researchers generally observe that gyros that are stable in the inertial frame, but in the navigation frame, the gyros and the inertial frame revolve around the Earth. The gravitational apparent motion is usually defined as the track of the gravity vector, which is stable in the Earth and rotating in the inertial frame. According to [7,8], the gravitational apparent motion in inertial frame is shown in Figure 2. In inertial frame, gravitational apparent motion is illustrated as a cone with the vertex at the Earth center and the cone axis coinciding with Earth’s rotating axis, the conical bottom radius is determined by the latitude where the vehicle is located. Notably, the formation of this cone is independent of the selection of inertial frame, but the specific mathematical expressions are related to the selection [9,22].


A Self-Alignment Algorithm for SINS Based on Gravitational Apparent Motion and Sensor Data Denoising.

Liu Y, Xu X, Liu X, Yao Y, Wu L, Sun J - Sensors (Basel) (2015)

Trajectory of the gravitational apparent motion in inertial space.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4481961&req=5

sensors-15-09827-f002: Trajectory of the gravitational apparent motion in inertial space.
Mentions: The concept of apparent motion in INS is initially used to describe the characteristics of gyros. Researchers generally observe that gyros that are stable in the inertial frame, but in the navigation frame, the gyros and the inertial frame revolve around the Earth. The gravitational apparent motion is usually defined as the track of the gravity vector, which is stable in the Earth and rotating in the inertial frame. According to [7,8], the gravitational apparent motion in inertial frame is shown in Figure 2. In inertial frame, gravitational apparent motion is illustrated as a cone with the vertex at the Earth center and the cone axis coinciding with Earth’s rotating axis, the conical bottom radius is determined by the latitude where the vehicle is located. Notably, the formation of this cone is independent of the selection of inertial frame, but the specific mathematical expressions are related to the selection [9,22].

Bottom Line: Initial alignment is always a key topic and difficult to achieve in an inertial navigation system (INS).Aiming to resolve this problem, an online sensor data denoising method based on a Kalman filter is proposed and a novel reconstruction method for apparent motion is designed to avoid the collinearity among vectors participating in the alignment solution.The accuracy can either reach or approach the theoretical values determined by sensor precision under static or swinging conditions.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China,. gcdlyt1985@163.com.

ABSTRACT
Initial alignment is always a key topic and difficult to achieve in an inertial navigation system (INS). In this paper a novel self-initial alignment algorithm is proposed using gravitational apparent motion vectors at three different moments and vector-operation. Simulation and analysis showed that this method easily suffers from the random noise contained in accelerometer measurements which are used to construct apparent motion directly. Aiming to resolve this problem, an online sensor data denoising method based on a Kalman filter is proposed and a novel reconstruction method for apparent motion is designed to avoid the collinearity among vectors participating in the alignment solution. Simulation, turntable tests and vehicle tests indicate that the proposed alignment algorithm can fulfill initial alignment of strapdown INS (SINS) under both static and swinging conditions. The accuracy can either reach or approach the theoretical values determined by sensor precision under static or swinging conditions.

No MeSH data available.