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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).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.

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.


Related in: MedlinePlus

Curves of alignment errors.
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sensors-15-09827-f005: Curves of alignment errors.

Mentions: In this paper, the simulation time period is 600 s and the alignment errors are shown in Figure 5 where only constant sensor errors in IMU can be seen, when the self-alignment for SINS based on three vectors of gravitational apparent motion in inertial frame can quickly complete the strapdown inertial navigation initial alignment. Alignment accuracy is equal to the theoretical value in Equation (10). When random sensor errors exist in IMU, the SINS alignment error increases, and an oscillation within 0.01° in the horizontal direction occurs; meanwhile, the yaw error is so severe that it is completely unavailable. Thus, the self-alignment for SINS based on three different vectors of gravitational apparent motion in inertial frame is heavily affected by the random sensor errors. Firstly, we analyze the reasons leading to the failure of the proposed alignment method.


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)

Curves of alignment errors.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-09827-f005: Curves of alignment errors.
Mentions: In this paper, the simulation time period is 600 s and the alignment errors are shown in Figure 5 where only constant sensor errors in IMU can be seen, when the self-alignment for SINS based on three vectors of gravitational apparent motion in inertial frame can quickly complete the strapdown inertial navigation initial alignment. Alignment accuracy is equal to the theoretical value in Equation (10). When random sensor errors exist in IMU, the SINS alignment error increases, and an oscillation within 0.01° in the horizontal direction occurs; meanwhile, the yaw error is so severe that it is completely unavailable. Thus, the self-alignment for SINS based on three different vectors of gravitational apparent motion in inertial frame is heavily affected by the random sensor errors. Firstly, we analyze the reasons leading to the failure of the proposed alignment method.

Bottom Line: Initial alignment is always a key topic and difficult to achieve in an inertial navigation system (INS).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.

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.


Related in: MedlinePlus