Abstract
For a running freely land-vehicle strapdown inertial navigation system (SINS), the problems of self-calibration and attitude alignment need to be solved simultaneously. This paper proposes a complete alignment algorithm for the land vehicle navigation using Inertial Measurement Units (IMUs) and an odometer. A self-calibration algorithm is proposed based on the global observability analysis to calibrate the odometer scale factor and IMU misalignment angle, and the initial alignment and calibration method based on optimal algorithm is established to estimate the attitude and other system parameters. This new algorithm has the capability of self-initialization and calibration without any prior attitude and sensor noise information. Computer simulation results show that the performance of the proposed algorithm is superior to the extended Kalman filter (EKF) method during the oscillating attitude motions, and the vehicle test validates its advantages.
Highlights
The strapdown inertial navigation system (SINS) is an autonomous navigation system that uses inertial measurement units (IMUs) and initial navigation information to determine the attitude, position and velocity [1,2]
Unlike the alignment on a static base, the alignment on a moving base usually requires the carrier motion information provided by some external device, for example, global positioning system (GPS), cameras, odometers and Doppler laser radars [9,10,11]
According to the results of observability analysis in the previous section, it is feasible to construct an ideal observer to estimate the odometer scale factor and IMU misalignment angle based on Equations (19) and (20)
Summary
The strapdown inertial navigation system (SINS) is an autonomous navigation system that uses inertial measurement units (IMUs) and initial navigation information to determine the attitude, position and velocity [1,2]. In [18,19], Kalman filter-based initial alignment for SINS/Doppler velocity log (DVL) integration is studied. DVL aided SINS initial alignment based on when thefiltering vehicle would running freely. The coarse alignment algorithm based on optimal estimation odometer aided SINS is studied in [22,23], in which the integration form of the velocity update for odometer [22,23], in which integration form of the velocity update equation in theaided bodySINS frameisisstudied used toingive a rough initialthe attitude. An optimization-based based initialprovided alignment calibrationisalgorithm of INS/odometer is proposed, in which the initial alignment and calibration algorithm of INS/odometer system is proposed, in which attitude attitude and the associated parameters including the odometer scale factor, lever the arm, and the associated the odometer scaleThe factor, lever arm, misalignment misalignment angleparameters and inertialincluding sensor biases are estimated.
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