Pengyu Gao

Self-calibration method based on navigation in high-precision inertial navigation system with fiber optic gyro

Abstract. A rotary inertial navigation system requires higher calibration accuracy of some error parameters owing to rotation. Conventional multiposition and rotation calibration methods are limited, for they do not consider sensors’ actual operating condition. In order to achieve these parameters’ values as closely as possible to their true values in application, their influence on navigation is analyzed, and a relevant new calibration method based on a system’s velocity output during navigation is designed for the vital error parameters, including inertial sensors’ installation errors and the scale factor error of fiber optic gyro. Most importantly, this approach requires no additional devices compared to the conventional method and costs merely several minutes. Experimental results from a real dual-axis rotary fiber optic gyro inertial navigation system demonstrate the practicability and higher precision of the suggested approach.

A Self-Calibration Method for Non-Orthogonal Angles of Gimbals in Tri-Axis Rotational Inertial Navigation System

Navigation accuracy of the inertial navigation system (INS) could be greatly improved by rotating the inertial measurement unit around gimbals. However, the attitude output accuracy of rotational INS (RINS) would be affected by the non-orthogonal angles of gimbals, which should be accurately calibrated and compensated. In this paper, a novel self-calibration method for non-orthogonal angles of gimbals is proposed in tri-axis RINS. The three non-orthogonal angles could be calibrated using attitude errors and velocity errors as measurements without external equipment. The self-calibration scheme in this method could reduce the coupling of non-orthogonal angles with other errors, and the observability is clear to demonstrate during calibration. The proposed method is verified by both simulations and experiments. The experimental results show that the calibration accuracy of non-orthogonal angles could be less than 2”, and after compensation, the attitude output accuracy is improved from 200” to less than 10”. Therefore, the proposed calibration method could greatly improve the attitude output accuracy and make RINS more effective in some task systems where high attitude output accuracy is urgently required.

Analysis and Improvement of Attitude Output Accuracy in Rotation Inertial Navigation System

Inertial navigation system (INS) measures vehicle’s angular rate and acceleration by orthogonally mounted tri-axis gyroscopes and accelerometers and then calculates the vehicle’s real-time attitude, velocity, and position. Gyroscope drifts and accelerometer biases are the key factors that affect the navigation accuracy. Theoretical analysis and experimental results show that the influence of gyroscope drifts and accelerometer biases can be restrained greatly in rotation INS (RINS) by driving the inertial measurement unit (IMU) rotating regularly, thus improving navigation accuracy significantly. High accuracy in position and velocity should be matched with that in attitude theoretically since INS is based on dead reckoning. However, the marine and vehicle experiments show that short-term attitude output accuracy of RINS is even worse compared with that of nonrotation INS. The loss of attitude accuracy has serious impacts on many task systems where high attitude accuracy is required. This paper researched the principle of attitude output accuracy loss in RINS and then proposed a new attitude output accuracy improvement algorithm for RINS. Experiment results show that the proposed attitude compensation method can improve short-term pitch and roll output accuracy from 20~30 arc seconds to less than 5 arc seconds and azimuth output accuracy improved from 2~3 arc minutes to less than 0.5 arc minutes in RINS.

A self-calibration method for tri-axis rotational inertial navigation system

The navigation accuracy of the rotational inertial navigation system (RINS) could be greatly improved by periodically rotating the inertial measurement unit (IMU) with gimbals. However, error parameters in RINS should be effectively calibrated and compensated. In this paper, a self-calibration method is proposed for tri-axis RINS using attitude errors and velocity errors as measurements. The proposed calibration scheme is designed as three separate steps, and a certain gimbal rotates continuously in each step. All the error parameters in the RINS are calibrated when the whole scheme finishes. The separate calibration steps reduce the correlations between error parameters, and the observability of errors in this method is clear to demonstrate according to the relations between navigation errors and error parameters when gimbals rotate. Each calibration step only lasts 12 min, thus gyro drifts and accelerometers biases could be regarded as constant. The proposed calibration scheme is tested in both simulation and actual tri-axis RINS, and simulation and experimental results show that all 23 error parameters could be well estimated in tri-axis RINS. A long-term vehicle navigation experiment results show that after calibration and compensation, the navigation performance has doubled approximately, and the velocity accuracy is less than 2 m s−1 while the position accuracy is less than 1500 m, fully illustrating the significance of the proposed self-calibration method in improving the navigation performance of RINS.

A Self-Calibration Method for Accelerometer Nonlinearity Errors in Triaxis Rotational Inertial Navigation System

The navigation performance of the rotational inertial navigation system (RINS) could be greatly improved by rotating the inertial measurement unit with gimbals, and self-calibration for error parameters could be achieved in RINS as well. However, accelerometer nonlinearity errors need to be considered and calibrated to further improve the navigation accuracy of RINS, especially in large dynamic applications. In this paper, a self-calibration method is proposed for accelerometer nonlinearity errors in triaxis RINS. Accelerometer nonlinearity errors and other errors are calibrated through optimal estimation with velocity and position error measurements. In order to guarantee that all errors are observable during calibration, some rotation scheme design principles are proposed, which are different from traditional observability analysis methods and could provide instructions for rotation scheme design directly. The effectiveness of the self-calibration method is proved by both simulation and experiment. The accelerometer nonlinearity errors could be accurately calibrated with the proposed method, while other error parameters reach higher calibration accuracy. Furthermore, experiment results from a long-term vehicle navigation show that velocity and position accuracy of the triaxis RINS have improved significantly after compensation with the self-calibration results, fully illustrating the significance of the proposed self-calibration method in improving the navigation performance of RINS.

Research on Parameter Estimation Methods for Alpha Stable Noise in a Laser Gyroscope’s Random Error

Alpha stable noise, determined by four parameters, has been found in the random error of a laser gyroscope. Accurate estimation of the four parameters is the key process for analyzing the properties of alpha stable noise. Three widely used estimation methods—quantile, empirical characteristic function (ECF) and logarithmic moment method—are analyzed in contrast with Monte Carlo simulation in this paper. The estimation accuracy and the application conditions of all methods, as well as the causes of poor estimation accuracy, are illustrated. Finally, the highest precision method, ECF, is applied to 27 groups of experimental data to estimate the parameters of alpha stable noise in a laser gyroscope’s random error. The cumulative probability density curve of the experimental data fitted by an alpha stable distribution is better than that by a Gaussian distribution, which verifies the existence of alpha stable noise in a laser gyroscope’s random error.

An Innovative Wavelet Threshold Denoising Method for Environmental Drift of Fiber Optic Gyro

Fiber optic gyroscope (FOG) is a core component in modern inertial technology. However, the precision and performance of FOG will be degraded by environmental drift, especially in complex temperature environment. As the modeling performance is affected by the noises in the output data of FOG, an improved wavelet threshold value based on Allan variance and Classical variance is proposed for discrete wavelet analysis to decompose the temperature drift trend item and noise items. Firstly, the relationship of Allan variance and Classical variance is introduced by analyzing the drawback of traditional wavelet threshold. Secondly, an improved threshold is put forward based on Allan variance and Classical variance which overcomes the shortcoming of traditional wavelet threshold method. Finally, the innovative threshold algorithm is experimentally evaluated on FOG. The mathematical evaluation results show that the new method can get better signal-to-noise ratio (SNR) and gain the reconstruction signal of the higher correlation coefficient (CC). As an experimental validation, the nonlinear capability of error back propagation neural network (BP neural network) is used to fit the drift trend item and find out the complex relationship between the FOG drift and temperature, and the final processing results indicate that the new denoising method can get better root of mean square error (MSE).

Research on the Rapid and Accurate Positioning and Orientation Approach for Land Missile-Launching Vehicle

Getting a land vehicle’s accurate position, azimuth and attitude rapidly is significant for vehicle based weapons’ combat effectiveness. In this paper, a new approach to acquire vehicle’s accurate position and orientation is proposed. It uses biaxial optical detection platform (BODP) to aim at and lock in no less than three pre-set cooperative targets, whose accurate positions are measured beforehand. Then, it calculates the vehicle’s accurate position, azimuth and attitudes by the rough position and orientation provided by vehicle based navigation systems and no less than three couples of azimuth and pitch angles measured by BODP. The proposed approach does not depend on Global Navigation Satellite System (GNSS), thus it is autonomous and difficult to interfere. Meanwhile, it only needs a rough position and orientation as algorithm’s iterative initial value, consequently, it does not have high performance requirement for Inertial Navigation System (INS), odometer and other vehicle based navigation systems, even in high precise applications. This paper described the system’s working procedure, presented theoretical deviation of the algorithm, and then verified its effectiveness through simulation and vehicle experiments. The simulation and experimental results indicate that the proposed approach can achieve positioning and orientation accuracy of 0.2 m and 20″ respectively in less than 3 min.

Four-position heading effect calibration algorithm for rotation inertial navigation system based on fiber optic gyro

Abstract. Fiber optic gyros (FOGs) are sensitive to the environment fields where they are mounted, and their drifts are easily affected when surrounding temperature field or magnetic field changes. In FOG strapdown inertial navigation system (INS), gyro drifts caused by environmental fields are stable mostly, thus they could be calibrated and compensated beforehand and would not cause obvious alignment and navigation errors. However, in rotation INS (RINS), although navigation errors caused by the constant components of FOG drifts could be well attenuated, the gyro sensing axes are changing relative to the environmental fields in the RINS, which would lead to periodically changing gyro drift components when inertial measurement unit is pointing to different headings, thus producing serious alignment and navigation errors in FOG RINS. To solve this problem, a four-position heading effect calibration algorithm was proposed, and its effectiveness and validity were verified through a dual-axis FOG RINS by turntable experiments. The experimental results show that the azimuth alignment accuracy of the FOG RINS improves from 0.2 deg to about 0.04 deg, increasing five times approximately, which illustrates that the proposed heading effect calibration algorithm could further improve the navigation performance of FOG RINS significantly.

An Innovative Architecture of UTC GPS/INS System with Improved Performance under Severe Jamming

Ultratightly coupled (UTC) architecture is believed to be the best architecture for Global Positioning System (GPS) and Inertial Navigation System (INS) integration system due to the advanced data fusion strategy and effective mutual assistance between the subsystems. However the performance of UTC GPS/INS system will be degraded by severe jamming interference, especially when low-grade inertial measurement unit (IMU) is used. To solve this problem an innovative architecture of UTC GPS/INS system is proposed. Since GPS receiver’s antijamming ability is closely related to tracking loop bandwidth, adaptive tracking loop bandwidth based on the fuzzy logics is proposed to enhance antijamming ability for GPS receiver. The bandwidth will be adapted through a fuzzy logic controller according to the calculated carrier to noise intensity ratio . Moreover, fuzzy adaptive integration Kalman filter (IKF) is developed to improve estimation accuracy of IKF when measurement noises change. A simulation platform is established to evaluate the innovative architecture and results demonstrate that the proposed scheme improves navigation performance significantly under severe jamming conditions.