An Improve Hybrid Calibration Scheme for Strapdown Inertial Navigation System

Abstract

This paper develops an improved hybrid calibration scheme for the strapdown inertial navigation system (SINS) that combines the advantages of an optimal rotational norm calibration method and an improved system-level calibration method. To accurately determine the scale factors and misalignment error of gyros triad, the optimal rotation norm calibration method is applied. Similarly, the improved system-level calibration method based on the 24-dimensional error state Kalman filter (ESKF) plays an important role in accelerometer calibration and gyro biases calibration. The clockwise and counterclockwise multi-cycles rotation scheme is designed to eliminate the effects of the Earth's rate and gyros biases, and then provide accurate rotational norm to completely determine the gyros triad scale factors and misalignment. In addition, the improved ESKF system-level calibration method is employed to estimate accelerometer parameters and gyros biases. Simulation test verify the validity of the proposed method, and the calibration experiences and the verification tests of the two high-precision optical gyros inertial measurement units (IMU) are carried out. For comparison, the traditional 30-dimensional system-level calibration is also performed. The attitude error of high maneuvering swing test indicate that the gyros calibration accuracy of hybrid calibration scheme is better than that of traditional system-level calibration scheme. Furthermore, the positioning error of pure inertial navigation solution in static and dynamic condition for two calibration methods indicate that the hybrid calibration scheme significantly improves the positioning accuracy, especially in the dynamic experiment, that is, the proposed scheme provides a more accurate calibration of the IMU.

Latitude Determination and Error Analysis for Stationary SINS in Unknow-Position Condition

“…Similar to the derivation process of latitude error in analytic 1 method, it is easy to obtain the sine and cosine expressions between azimuth ψ and IMU output from Equation (19). Associating Equations (15), (19) and (20), the LD equation of analytical 2 method can be rewritten as follows:…”

“…For the purpose of the test, a navigation grade INS with three gyros and accelerometers was taken as a candidate simulation SINS. The accelerometer data sets were corrupted by 100 µg of constant biases and 10 µg/ √ h of random walk, and the gyros data sets were added 0.01 • /h of constant biases and 0.001 • / √ h of random walk, respectively [19,20]. The initial position of the simulation SINS was located in Beijing of China with a latitude of 39.97 • (N), longitude of 116.34 • (E) and altitude of 50 m. Then, the 5 min data sets sampled with 100 Hz were generated.…”

A New Norm-Observed Calibration Method Based on Improved Differential Evolution Algorithm for SINS

“…b: orthogonal body frame with right-front-up axis N g : three-axis gyroscope output in the nonorthogonal frame N a : three-axis accelerometer output in the nonorthogonal frame ω b : three-axis angular velocities input in the b frame f b : three-axis specific forces input in the b frame K g : diagonal matrix of the gyros' scale factors K a : diagonal matrix of the accelerometers' scale factors ε: three-axis gyroscope constant drift vector ∇: three-axis accelerometer constant drift vector E g : installation error matrix from the b frame to the gyro frame E a : installation error matrix from the b frame to the accelerometer frame e ideal error model of the gyros can be defined as follows [23]:…”

Geometric Positioning Error Mitigation of SAR Image in Ocean Utilizing AIS Information

“…eometric positioning accuracy is crucially influenced by the acquisition geometry of remote sensing apparatus; the discrete location, velocity and motion of the sensor are required to be measured precisely [1]. Due to its demand of enhancing positioning accuracy, advanced geo-location sensors such as Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU), respectively estimating the location with velocity and geometric motion, were introduced and implemented to the sensor [2]. In order to enhance the acquisition of location, velocity and motion, suppression of intrinsic measurement error terms was generally applied through Kalman Filter, utilizing the covariance matrix of the maneuvering target sensor [3].…”