Jiancheng Fang

Predictive Iterated Kalman Filter for INS/GPS Integration and Its Application to SAR Motion Compensation

This paper deals with the problem of state estimation for the integration of an inertial navigation system (INS) and Global Positioning System (GPS). For a nonlinear system that has the model error and white Gaussian noise, a predictive filter (PF) is used to estimate the model error, and based on this, a modified iterated extended Kalman filter (IEKF) is proposed and is called predictive iterated Kalman filter (PIKF). The basic idea of the PIKF is to compensate the state estimate by the estimated model error. An INS/GPS integration system is implemented using the PIKF and applied to synthetic aperture radar (SAR) motion compensation. Through flight tests, it is shown that the PIKF has an obvious accuracy advantage over the IEKF and unscented Kalman filter (UKF) in velocity.

A Novel Calibration Method of Magnetic Compass Based on Ellipsoid Fitting

Magnetic compass is widely used to indicate the heading of vehicle by measuring the Earths magnetic field. However, it suffers from local magnetic interferences; thus, the calibration of the magnetic compass is very essential before it is used. The traditional calibration methods require reference information and special requirements such as keeping the magnetic compass level during calibration, which is very difficult to manage outdoor. This paper presents an efficient method for calibrating the magnetic compass without the aforementioned traditional requirements. This method is based on the fact that the error model of magnetic compass is an ellipsoid, and a constraint least-square method is adopted to estimate the parameters of an ellipsoid by rotating the magnetic compass in various (random) orientations. This method can estimate all the parameters of the error model and compensate errors caused by sensor defects, hard-iron interferences, and soft-iron interferences. Although the calibration parameters are relative values, it does not have any influence on the heading calculated. The experimental results show that this method is effective in calibrating the magnetic compass, and the heading precision of the magnetic compass acquired after calibration is better than 0.4°.

Not Fully Overlapping Allan Variance and Total Variance for Inertial Sensor Stochastic Error Analysis

Stochastic errors characterize the performance of inertial sensors and indicate the potential improvements of the device. Accurate modeling of these errors can enhance the performance of inertial navigation system. The simplest and generally adopted method to model stochastic errors of inertial sensor is the normal nonoverlapped Allan variance, but its estimation accuracy decreases in long cluster time. The fully overlapping Allan variance improves the estimation accuracy in long cluster time very much, while the traditional total variance based on maximal-overlapping cluster samples further increases the estimation accuracy in long cluster time greatly. However, with these two methods, better estimation is achieved at the expense of much longer computation time. Besides, the computation burden for large dataset and many variance data points is extremely large, while the inertial sensors need large dataset with many variance data points to fully characterize their stochastic errors. This is because the correlation time for some stochastic error is very long (e.g., the correlation time is 3 h for rate random walk), and the Allan variance curve of some stochastic error is very complicated (e.g., sinusoidal noise). Consequently, the fully overlapping Allan variance and the traditional total variance are not suitable for inertial sensor stochastic error modeling. This paper proposes a not fully overlapping Allan variance which has similar estimation accuracy to fully overlapping Allan variance, and whose calculation time is greatly reduced relative to fully overlapping Allan variance. Then, the not fully overlapping concept is integrated to that of total variance to further improve the estimation accuracy in long cluster time with respect to Allan variance, and the calculation time of total variance is greatly reduced as well. This method enables high accuracy and high computational efficiency of Allan variance analysis at the same time, especially for large dataset and many variance data points analysis. Finally, the proposed methods are applied to 12-hour static data of gyroscopes and accelerometers from a position and orientation system, and their advantages are demonstrated.

A Hybrid Prediction Method for Bridging GPS Outages in High-Precision POS Application

Position and orientation system (POS) is a key technology widely used in remote sensing applications, which integrates inertial navigation system (INS) and GPS using a Kalman filter (KF) to provide high-accuracy position, velocity, and attitude information for remote sensing motion compensation. However, when GPS signal is blocked, the POS accuracy will decrease owing to the unbounded INS error accumulation. To improve the reliability and accuracy of POS, this paper proposes a hybrid prediction method for bridging GPS outages. This method uses radial basis function (RBF) neural network coupled with time series analysis to forecast the measurement update of KF, resulting in reliable performance during GPS outages. In verifying the proposed hybrid prediction method, a flight experiment was conducted in 2011, based on a high-precision Laser POS. Experimental results show that the proposed hybrid prediction method is more effective than two other methods (KF and RBF neural network).

Sliding Average Allan Variance for Inertial Sensor Stochastic Error Analysis

Inertial sensor errors include deterministic errors and stochastic errors. Deterministic errors can be calibrated in laboratory by simple computation technique. Stochastic errors can be determined during calibration by adopting special methods because of their random character. The simplest method to determine the stochastic errors for inertial sensors is the Allan variance. This kind of method needs large data to fully characterize the stochastic errors. The normal nonoverlapped Allan variance has quite poor estimation accuracy in long cluster time. The fully overlapping Allan variance and traditional total variance have better estimation accuracy in long cluster time but are quite time consuming for large data set. The not fully overlapping Allan variance and nonoverlapped total variance are suitable for large data set to improve the estimation accuracy in long cluster time with much less time, but their accuracy is still relatively poor in comparison with not fully overlapping total variance. Whereas the not fully overlapping total variance is relatively time consuming and, compared with Allan variance, there is a bias which is not easy to be corrected. This paper proposes a sliding average Allan variance that has comparable estimation accuracy with total variance. The data are not required to extend as the total variance; thus the calculation burden could be reduced greatly. Therefore, it is more suitable for large data set. In addition this method has no bias in comparison with Allan variance, which means no bias correction is required. This method is applied to 12-h static data of three gyroscopes from a position and orientation system with good performance.

Dynamics Modeling and Measurement of the Microvibrations for a Magnetically Suspended Flywheel

Microvibrations produced by high-speed flywheels can highly degrade the performance of precision instruments on satellites. Magnetically suspended flywheel (MSFW) is a promising attitude actuator for the ultraprecision satellites due to the convenient and effective microvibration suppression algorithm. The comprehensive microvibration characteristics of an MSFW are important for designing the microvibration suppression algorithm. This paper focuses on studying the microvibration characteristics of an MSFW with a specific magnetic bearing structure. A complete microvibration dynamical model of the MSFW, including the rotor dynamics, the magnetic bearing control system, and the microvibration sources, is developed. Simulations are performed to predict the microvibration characteristics according to the proposed theoretical model and experimental tests are conducted to measure the comprehensive microvibrations of the practical MSFW. Simulation and experimental results are compared to prove the validity of the proposed microvibration dynamical model. Thus, the proposed theoretical model can predict the microvibration characteristics of the MSFW and can be used to design the microvibration suppression algorithm in the future work.

A New Noncontact Flatness Measuring System of Large 2-D Flat Workpiece

The flatness measurement of a large 2D flat workpiece such as a satellite solar panel substrate is a key problem in its manufacturing process. Based on the current measuring methods and the actual engineering project, a new noncontact high-precision, low-cost large 2-D flat workpiece flatness measuring system is developed, in which the techniques of oblique optical triangle measuring structure, virtual measurement datum plane, and error self-correction are adopted. By means of the oblique optical triangle measuring structure that is first proposed, the measuring area and resolution of the measuring system can be greatly increased. Meanwhile, on the basis of the techniques of virtual measurement datum plane and error self-correction, the noncontact high-precision, low-cost flatness measurement can be realized on a nonprecise large metallic platform. Experimental results on the satellite solar panel substrate indicate the correctness and effectiveness of the proposed noncontact flatness-measuring system of the large 2-D flat workpiece. Although the specific application of the satellite solar panel substrate is presented in this paper, the proposed measurement techniques and measuring system could be applied independent of the nature or use of the large 2-D flat workpieces.

A New Inclination Error Calibration Method of Motion Table Based on Accelerometers

Motion table is a kind of test equipment, which is designed to provide precise angular and rate motions for the testing of measurement sensors and systems. To improve the application precision of the motion table, a new inclination error calibration method of the motion table is proposed in this paper. The error model of the motion table that considered the inclination error is derived, and ellipsoid fitting is employed to calibrate the inclination error of the motion table based on the output of accelerometer. To validate the effectiveness of the new calibration method, position and orientation system (POS) is used to test the motion table. Through establishment of the relationship between inclination error and output of the motion table, the inclination error of the motion table has been compensated in the test process of POS, so that the inclination error can be indicated by the output of POS on the motion table. According to the output of POS before and after inclination error compensation of the motion table, the feasibility of calibration method in this paper is verified, and the experiment results show that the proposed method can improve the application precision of the motion table greatly.

Research on fast algorithm of small UAV navigation in non-linear matrix reductionism method

The low Reynolds numbers of small UAV will result in unfavorable aerodynamic conditions to support controlled flight. And as operated near ground, the small UAV will be affected seriously by low-frequency interference caused by atmospheric disturbance. Therefore, the GNC system needs high frequency of attitude estimation and control to realize the steady of the UAV. In company with the dimensional of small UAV dwindling away, its GNC system is more and more taken embedded designing technology to reach the purpose of compactness, light weight and low power consumption. At the same time, the operational capability of GNC system also gets limit in a certain extent. Therefore, a kind of high speed navigation algorithm design becomes the imminence demand of GNC system. Aiming at such requirement, a kind of non-linearity matrix reduction approach is adopted in this paper to create a new high speed navigation algorithm which holds the radius of meridian circle and prime vertical circle as constant and linearizes the position matrix calculation formulae of navigation equation. Compared with normal navigation algorithm, this high speed navigation algorithm decreases 17.3% operand. Within small UAVs mission radius (20km), the accuracy of position error is less than 0.13m. The results of semi-physical experiments and small UAVs auto pilot testing proved that this algorithm can realize high frequency attitude estimation and control. It will avoid low-frequency interference caused by atmospheric disturbance properly.

Solar panel substrate planeness measuring system by an optical triangulation method

The planeness measurement of solar panel substrate is a key problem in the manufacture procedure of satellites. The current measuring methods have the problems such as low precision and low efficiency. Based on the actual engineering project of the solar panel substrate planeness measuring system, a solar panel substrate planeness non-contact measuring system is presented, which employs an optical triangulation method and bases on virtual precise datum plane. By means of a declinate optical triangulation measuring instrument structure which is firstly proposed, the measured area and resolution of this measuring system are greatly increased, and the high accuracy non-contact measurement of the planeness of a large area plane is realized. On the basis of a new modeling method of virtual precise datum plane and measurement error compensation technique, the measuring system can accurately measure the solar panel substrate planeness on a non-precision plate. The actual measurement results show that the measurement accuracy 0.02mm (RMS) can be obtained when a solar panel substrate (2581mmx1755mm) planeness is measured by using of this measuring system.