Single-axis rotation/azimuth-motion insulation inertial navigation control system with FOGs

Suppression of the G-sensitive drift of laser gyro in dual-axis rotational inertial navigation system

In the fiber optic gyroscope (FOG) single-axis rotation inertial navigation system (SRINS), the Z-axis gyro drift (ϵz) dramatically limits its navigation precision and the Z-axis gyro scale factor error (δKGz) can cause greater navigation error because of the rotation process compared with the strap-down inertial navigation system. Hence, identification and compensation for the ϵz and δKGz are important in SRINS. An approach based on the azimuth error model of open-loop algorithm is proposed. This approach considers azimuth angle as a measurement and uses a least recursive square algorithm for identifying the ϵz and δKGz. Compared with the traditional method, which takes velocity and position errors as measurements, the time required for identifying ϵz with the proposed method is only approximately 10 min, while the traditional method requires 6 h to 12 h. Experimental results from an SRINS with FOGs demonstrate that the accuracy of identifying the ϵz reaches 0.002°/h and that of the δKGz reaches 8 ppm in 10 min. The positioning accuracy of the SRINS improves greatly after the identification and compensation.

Aiming at the problem that the error modulation effects are affected by the angular motions of the vehicle in the dual-axis rotational laser gyro inertial navigation system (INS), a vehicle angular motion isolation method based on attitude feedback is proposed. Based on the error equations of INS, the principles of error modulation in rotation process are analyzed. Then the relationships between the navigation results and the controlled angular velocity imposed on the rotating mechanism are established. Finally, simulation models of angular motions with various forms are established. Simulation results show that this method can effectively isolate the angular motions of the vehicle and reduce the influences of the angular motions on the rotation modulation. The navigation accuracy is significantly improved.

Navigation of Hypersonic vehicles in the radio frequency (RF) blackout area during atmospheric reentry is challenging as the vehicles can only use the inertial navigation system (INS) as autonomous navigation method in this area. In this paper, strapdown inertial navigation system (SINS) based on the Fiber Optic Gyroscope (FOG) is used for navigation in blackout area. However, without external navigation measurement, the errors of SINS caused by the FOG drift and accelerometer bias would cumulate with time and degrade navigation accuracy. To solve this problem, single axis rotation modulation along with the azimuth axis of the body frame is adopted. The Generic Hypersonic Vehicle (GHV) model designed by NASA Langley Research Center is used to build the reentry fight model which can generate navigation information for simulation. Through derivation the error equations of FOG SINS in the North-East-Down (NED) navigation frame, the principle of error compensation by rotation modulation can be well understood. The simulation results show that rotation modulation can effectively decrease the impact of inertial sensor drift and improve the navigation accuracy in blackout area.

Rotation modulation (RM) technology can effectively improve the navigation accuracy of strapdown inertial navigation system (SINS) by the periodic rotation of the rotation mechanism. But the original error characteristics of SINS will be changed after introducing the rotation modulation technology. Aiming at the practical micro-electro-mechanical system RMSINS, the error characteristics are comprehensively analyzed, and then a complete error model is established. At the same time, a reasonable parameter calibration scheme and the compensation method are designed to compensate the error in this paper. On this basis, static and dynamic experiments are carried out. The position accuracies of the rotation modulation state in the static and dynamic experiments are about ten times and about three times than that of the non-rotation state. The experimental results show that the proposed error analysis and compensation method are feasible which have been applied to the practical engineering.

To improve the accuracy and robustness of the inertial navigation systems (INS) for wheeled robots without adding additional component cost, we propose Wheel-INS, a complete dead reckoning (DR) solution based on a wheel-mounted microelectromechanical system (MEMS) inertial measurement unit (IMU) in this article. There are two major advantages of mounting IMU to the center of a non-steering wheel. Firstly, the gyroscope outputs can be used to calculate the wheel speed, so as to replace the traditional odometer to mitigate the error drift of INS. Secondly, with the rotation of the wheel, the constant bias error of the inertial sensor can be canceled to some extent. The installation scheme of the wheel-mounted MEMS IMU (Wheel-IMU), the system characteristics, and the DR error analysis are all discussed in this paper. Experimental results show that the maximum position drift of Wheel-INS in the horizontal plane is less than 1.8% of the total traveled distance, reduced by 23% compared to the conventional odometer-aided INS (ODO/INS). In addition, Wheel-INS outperforms ODO/INS because of its inherent immunity to constant bias error of gyroscopes. The source code and experimental datasets have been made available to the community ( <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/i2Nav-WHU/Wheel-INS</uri> ).

A new dynamic calibration method for IMU deterministic errors of the INS on the Hypersonic Cruise Vehicles

For its obvious high accuracy in a long term, rotary inertial navigation system becomes popular. However, as errors increasing with time are changed into periodic errors with zero mean, the accuracy of its azimuth angle decreases in view of short term and can’t fully satisfy the need of users such as flight control system. The precision of azimuth angle is studied by analyzing its influence factor and establishing the scale factor error model and drift models of fiber optic gyro and the measurement error model of photoelectric encoder. Correction for fiber optic gyro and photoelectric encoder based on their mutual assistance is designed to improve the short-term accuracy of rotary inertial navigation system’s azimuth angle. Experimental results in a fiber optic gyro based rotary inertial navigation system verify the proposed method.

In the dual-axis rotation inertial navigation system (INS), besides the gyro error, accelerometer error, rolling misalignment angle error, and the gimbal angle error, the shaft swing angle and the axis non-orthogonal angle also affect the attitude accuracy. Through the analysis of the structure, we can see that the shaft swing angle and axis non-orthogonal angle will produce coning errors which cause the fluctuation of the attitude. According to the analysis of the rotation vector, it can be seen that the coning error will generate additional drift velocity along the rotating shaft, which can reduce the navigation precision of the system. In this paper, based on the establishment of the modulation average frame, the vector projection is carried out, and then the attitude conversion matrix and the attitude error matrix mainly including the shaft swing angle and axis non-orthogonal are obtained. Because the attitude angles are given under the static condition, the shaft swing angle and the axis non-orthogonal angle are estimated by the static Kalman filter (KF). This kind of KF method has been widely recognized as the standard optimal estimation tool for estimating the parameters such as coning angles (α1 , α2), initial phase angles (ϕ1,ϕ2), and the non-perpendicular angle (η). In order to carry out the system level verification, a dual axis rotation INS is designed. Through simulation and experiments, the results show that the amplitudes of the attitude angles’ variation are reduced by about 20%–30% when the shaft rotates. The attitude error equation is reasonably simplified and the calibration method is accurate enough. The attitude accuracy is further improved.

In long endurance underwater vehicle rotational inertial navigation system, the rotation strategy significantly impacts navigation accuracy. To address the limitations of the dual-axis 16-position rotation modulation scheme, specifically, its large rotation angles and inability to modulate errors arising from the coupling of scale factors and Earth’s rotation rate. This paper proposes a geocentric frame based triaxial 32-position rotation modulation method. This method suppresses navigation errors by altering the direction of the rotation axis and increasing the rotation sequence. It simultaneously isolates the Earth’s rotation rate, thereby eliminating the coupling error between inertial sensor scale factors and Earth rotation, and enhancing system navigation accuracy. Experimental results demonstrate that the system achieves a navigation positioning accuracy of approximately 2.2 nautical miles (n mile) over 72 hours. Compared to a single-axis RINS, positioning accuracy is improved by a factor of 8. Compared to a dual-axis RINS, positioning accuracy is enhanced by 0.75 n mile. These results validate the effectiveness of the proposed method in improving navigation accuracy.

In a deeply-coupled GPS/INS integrated system, the use of the inertial aiding information can improve the tracking loop performance and make the system more robust. To meet this requirement, the inertial aiding information should have sufficient accuracy in short-term (such as the sampling interval of GPS, e.g. 1sec). The MEMS (Micro-Electro Mechanical System) IMU (Inertial Measurement Unit) can be a promising candidate due to its small size and low cost. There should be no doubt that MEMS INS (Inertial Navigation System) can aid the GPS receiver tracking loop by eliminating the dominant part of the motion dynamic stress, considering that the INS errors induced by the receiver motion dynamics is much less than the motion dynamic itself, when the receiver manoeuvres. So the only concern the side effect caused by MEMS INS, which determine whether MEMS IMU is qualified for deep integration, is its navigation error independent with the motion dynamics (i.e. manoeuvre-independent error). This paper assesses this side effect of MEMS INS in terms of providing Doppler aiding data in to the GPS carrier tracking loop through a thorough error propagation analysis. The Laplace transform analysis is applied to the simplified INS error dynamic equations under stationary condition and find out the transfer relation between the error sources and the velocity estimation errors. Then the velocity error is converted to Doppler aiding error and substitute into the GPS tracking loop to analyze the corresponding carrier phase error. Results show that the largest velocity error caused by maneuver-independent errors is less than 0.1m/s during the typical GPS update interval (e.g. 1 sec), which meets the real road test results. The consequent carrier phase tracking error caused by the maneuver-independent error of MEMS INS is below 1.2 degree, which is much less than receiver inherent errors (e.g. the oscillator error and thermal noise). Conclusion can be reached that even the low-end MEMS IMUs have the ability of aiding the GPS receiver signal tracking although it induces some additional errors.

The inertial navigation system (INS) is widely used in spacecraft, missiles, and airplanes for its full autonomy and complete information. Skewed inertial measurement unit (IMU) is a typical device for INS. This IMU is of great significance for key projects such as manned spacecraft or heavy-lift rockets. Calibration of the IMU parameters has always been the key technology for INS. However, the calibration for skewed IMU is difficult to conduct for the skewed placement of the sensors and the complicated model. An all-parameter calibration for six-axis skewed IMU is proposed in this article. The error model is established. The measurement equations for Kalman filter are deduced. The calculation can be accomplished in two steps: The calibration for three gyros+ three accelerometers and the parameter compensation and iteration calculation. Simulations and experiments results indicate that: The complexity of calibration data and procedure is the same level as that of traditional IMU; all the parameters can be precisely estimated with the designed 12 positions sequence.

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.

With the rotation modulation, a strapdown inertial navigation system (INS) can keep high accuracy navigation without high accuracy gyro and high accuracy accelerometer. Firstly we give the error equation of strapdown INS, and then deduct the navigation error propagation of system. From partial analysis way, the modulation process of gyro constant error is obtained. Form global analysis way, the navigation error roused by constant error and the modulation effects are deducted here.

Inertial navigation provides a unique ability to know where one has been, where one is currently, and where one is going, given only a starting position. The laws of physics permit the sensing of dynamic motion without external information, making inertial systems impervious to jamming, masking, or spoofing. Measurements of six degrees of freedom are required - three linear accelerations, and three angular rates - to fully propagate the velocity, position, and orientation of the system. The first inertial sensors are traced to the early 19th century and specialized inertial guidance systems appeared in the 1940s, yet inertial navigation systems did not become commonplace until the 1960s. This is largely due to the fact that requirements for navigation accuracy inertial sensors - accelerometers and gyroscopes - are very challenging. In the past fifty years, significant evolutionary and revolutionary changes have taken place in the designs of inertial sensors and systems. These include the progression from fluid-filled to dry instruments and the transition from mechanically complex stabilized inertial platforms to computationally intensive strapdown systems. Gyroscopes have evolved from large mechanical devices to highly refined precision mechanical sensors. Optical rotation sensors such as the ring laser gyro and the fiber optic gyro have enabled new system designs and capabilities. Coriolis vibratory gyroscopes such as the hemispherical resonator gyro are capable of extreme accuracy and reliability; new opportunities for miniaturizing these types of sensors will lead to new classes of accuracy for inertial navigation systems. Advanced gyroscope technologies such as the nuclear magnetic resonance gyroscope which uses atomic spin to detect rotation have already been demonstrated to achieve navigation accuracy requirements. Cold atom technologies may also provide the opportunity for very high accuracy accelerometers and gyroscopes in the future. Inertial navigation technologies and applications of the past, present, and future are discussed.