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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

Projections of gravitational apparent motion in the inertial frame.
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sensors-15-09827-f010: Projections of gravitational apparent motion in the inertial frame.

Mentions: The introduction of online parameters-adjusted Kalman filter to the self-alignment of the SINS based on the three different vectors of gravitational apparent motion in the inertial frame and the projections of the gravity vectors measured by accelerometers are shown in Figure 10. From Figure 10, the projections of the gravity in the inertial frame, which are measured by accelerometers, are smoother and most of the random noise disturbance is eliminated. Compared with the theoretical gravity in the inertial frame without any IMU measurement errors, constant values exist because of the constant errors of the accelerometers.


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)

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

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

sensors-15-09827-f010: Projections of gravitational apparent motion in the inertial frame.
Mentions: The introduction of online parameters-adjusted Kalman filter to the self-alignment of the SINS based on the three different vectors of gravitational apparent motion in the inertial frame and the projections of the gravity vectors measured by accelerometers are shown in Figure 10. From Figure 10, the projections of the gravity in the inertial frame, which are measured by accelerometers, are smoother and most of the random noise disturbance is eliminated. Compared with the theoretical gravity in the inertial frame without any IMU measurement errors, constant values exist because of the constant errors of the accelerometers.

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