Localized fluid flow measurements with an He-Ne laser spectrometer

This paper proposed three methods to compensate the temperature energy influence drift of the MEMS vibration gyroscope, including radial basis function neural network (RBF NN), RBF NN based on genetic algorithm (GA), and RBF NN based on GA with Kalman filter (KF). Three-axis MEMS vibration gyroscope (Gyro X, Gyro Y, and Gyro Z) output data are compensated and analyzed in this paper. The experimental results proved the correctness of these three methods, and MEMS vibration gyroscope temperature energy influence drift is compensated effectively. The results indicate that, after RBF NN-GA-KF method compensation, the bias instability of Gyros X, Y, and Z improves from 139°/h, 154°/h, and 178°/h to 2.9°/h, 3.9°/h, and 1.6°/h, respectively. And the angle random walk of Gyros X, Y, and Z was improved from 3.03°/h1/2, 4.55°/h1/2, and 5.89°/h1/2 to 1.58°/h1/2, 2.58°/h1/2, and 0.71°/h1/2, respectively, and the drift trend and noise characteristic are optimized obviously.

https://downloads.hindawi.com/journals/sv/2018/2830686.pdf

Temperature Energy Influence Compensation for MEMS Vibration Gyroscope Based on RBF NN-GA-KF Method

Since the conventional strapdown inertial navigation system (SINS)- and odometer (OD)-integrated navigation has certain limitations, such as suffering from scale factor variation and being affected by wheel slipping and skidding, we present a high-accuracy integrated navigation system that uses both the SINS and laser Doppler velocimeter (LDV) for land vehicles to overcome these problems. We took the lever-arm residual and boresight errors into consideration and formed a complete 21-state Kalman filter to accomplish integrated navigation and precise sensor-to-sensor calibration. The observability analysis of the SINS/LDV system provides valuable insights into the conditions under which the states can be estimated. In addition, the summed measurement model and sequential Kalman filter calculation are deduced to make full use of the nonholonomic constraints (NHC) and LDV measurements. Finally, a long-distance experiment (230 km) with a navigation-grade IMU demonstrated that positioning accuracies of better than 0.02% of the distance travelled can be achieved in both the horizontal and vertical directions.

High-Accuracy SINS/LDV Integration for Long-Distance Land Navigation

This paper proposes a new quaternion calibration algorithm to calibrate large misalignment angles between Strapdown Inertial Navigation System (SINS) and Doppler Velocity Log (DVL) in SINS/DVL integrated navigation system. A new SINS/DVL/GPS integrated navigation system is derived to complete the SINS alignment and SINS/DVL integrated navigation system calibration. Different from the traditional calibration model, the misalignment angles are described by quaternion, according to the physical properties of misalignment angle and scale factor error, the new measurement equation is derived: zero observation vector is used to estimate the misalignment angles, and velocity error scalar is used to estimate the scale factor error. A switching measurement information Kalman filter is designed to switch between different processes. The performance of the proposed quaternion calibration algorithm is evaluated through simulation comparisons with nonlinear model approaches and experiment. The simulation results demonstrate that the proposed quaternion calibration algorithm has better accuracy and robustness than the nonlinear model approaches. The experiment results show that the proposed quaternion calibration algorithm is effective, the SINS/DVL integrated navigation system positioning error after calibration is less than 1.65% mileage in more than 30km travel.

A Novel Calibration Method of SINS/DVL Integration Navigation System Based on Quaternion

Land vehicle navigation based on inertial navigation system (INS) and odometers is a classical autonomous navigation application and has been extensively studied over the past several decades. In this work, we seriously analyze the error characteristics of the odometer (OD) pulses and investigate three types of odometer measurement models in the INS/OD integrated system. Specifically, in the pulse velocity model, a preliminary Kalman filter is designed to obtain accurate vehicle velocity from the accumulated pulses; the pulse increment model is accordingly obtained by integrating the pulse velocity; a new pulse accumulation model is proposed by augmenting the travelled distance into the system state. The three types of measurements, along with the nonhonolomic constraint (NHC), are implemented in the standard extended Kalman filter. In view of the motion-related pulse error characteristics, the multiple model adaptive estimation (MMAE) approach is exploited to further enhance the performance. Simulations and long-distance experiments are conducted to verify the feasibility and effectiveness of the proposed methods. It is shown that the standard pulse velocity measurement achieves the superior performance, whereas the accumulated pulse measurement is most favorable with the MMAE enhancement.

INS/Odometer Land Navigation by Accurate Measurement Modeling and Multiple-Model Adaptive Estimation

In order to obtain the accurate velocity of a vehicle, calibration of a laser Doppler velocimeter (LDV) is particularly important after installation of the inertial navigation system (INS) and LDV. The calibration accuracy determines the navigational accuracy of the INS/LDV integrated navigation system. In this paper, a highly accurate calibration method based on the error equations of the INS/LDV integrated system is presented. The basic principles of analytic calibration and filtering calibration methods are discussed in detail. The test results show that the scale factor and the installation error of the LDV can be automatically calibrated by our novel calibration method with sufficient accuracy. For a high-precision INS and a 2-D LDV integrated navigation system, after calibration, the position error of dead reckoning (DR) decreased from 1180 m in 2 h to 8 m in 2 h. For a medium precision INS and a 1-D LDV integrated navigation system, after calibration, the position error of DR decreased from 1300 m in 1 h to 25 m in 1 h. The excellent parameter stability of the LDV is also illustrated.

A Highly Accurate Calibration Method for Terrestrial Laser Doppler Velocimeter

Traditional compensation methods using temperature-related parameters have little effect when the ring laser gyroscope (RLG) bias changes rapidly. To solve this problem, a novel RLG bias temperature compensation method using readout signals is proposed in this paper. Combined with the least squares support vector machine (LS-SVM) algorithm, the novel method can improve the precision of the RLG bias. Experiments show that by utilizing the readout signals in the LS-SVM model, the RLG bias stability can be significantly raised compared to the original data. The novel method proposed in this paper is shown to be feasible, even when the RLG bias changes rapidly.

Temperature compensation method using readout signals of ring laser gyroscope

To further improve ring laser gyroscope (RLG) bias stability, a multiple-point temperature gradient algorithm is proposed for RLG bias compensation in this paper. Based on the multiple-point temperature measurement system, a complete thermo-image of the RLG block is developed. Combined with the multiple-point temperature gradients between different points of the RLG block, the particle swarm optimization algorithm is used to tune the support vector machine (SVM) parameters, and an optimized design for selecting the thermometer locations is also discussed. The experimental results validate the superiority of the introduced method and enhance the precision and generalizability in the RLG bias compensation model.

/pdf/multiple-point-temperature-gradient-algorithm-for-ring-laser-i6ix0j61tl.pdf

Multiple-Point Temperature Gradient Algorithm for Ring Laser Gyroscope Bias Compensation.

Temperature is an important factor for affecting the accuracy of quartz flexible accelerometer. The relationship of quartz flexible accelerometers drift to temperature should be established accurately which can improve the precision of inertial navigation system. In order to reduce the temperature sensitivity and improve the sensor performance, temperature drift compensation method based on artificial fish swarm (AFS) algorithm is established and the steps and methods are given. The traditional modeling method of stepwise regression is also investigated to provide a comparison with the AFS algorithm. The result shows that the temperature compensation model by AFS algorithm is accurate. The drift instability of accelerometer output is reduced from 160.2ug to 18.0ug over the temperature range from-20°Cto +50°C. The results of the stochastic temperature tests show that this method has reduced the influence of temperature variation effectively and improved the accelerometer accuracy.

Temperature Drift Compensation Based on Artificial Fish Swarm Algorithm for Quartz Flexible Accelerometer

The calibration of error coefficients for accelerometers and laser gyros is an effective way to improve the navigation precision of strapdown inertial navigation systems. The calibration parameters often change with temperature. This paper proposes a system-level calibration method including temperature-related error coefficients. The method includes an improved 18-step calibration scheme with temperature being changed by using a thermal chamber. A 42-dimensional Kalman filter is applied to estimate the error parameters including the bias, scale factor errors, installation errors and temperature-related error coefficients of accelerometers. This method has the great advantage of simplifying the calibration procedure and is widely applicable to all temperature-related error coefficients. Compared with the traditional calibration method at different temperature points, the calibration time of the proposed method is shortened by 24 h. The feasibility of this method is verified by simulations and navigation experiments. The results of navigation experiments show that the maximum positioning errors in pure inertial navigation decrease by approximately 30% after temperature compensation.

https://iopscience.iop.org/article/10.1088/1361-6501/ac0acd/pdf

Guo Wei

Papers

A Highly Accurate Calibration Method for Terrestrial Laser Doppler Velocimeter

Temperature compensation method using readout signals of ring laser gyroscope

Multiple-Point Temperature Gradient Algorithm for Ring Laser Gyroscope Bias Compensation.

Temperature Drift Compensation Based on Artificial Fish Swarm Algorithm for Quartz Flexible Accelerometer

A system-level calibration method including temperature-related error coefficients for a strapdown inertial navigation system