A correcting filter for a mechanically dithered single-axis ring laser gyro

The ring laser gyro (RLG) dither axis will bend and exhibit errors due to the specific forces acting on the instrument, which are known as g-sensitive misalignments of the gyros. The g-sensitive misalignments of the RLG triad will cause severe attitude error in vibration or maneuver environments where large-amplitude specific forces and angular rates coexist. However, g-sensitive misalignments are usually ignored when calibrating the strapdown inertial navigation system (SINS). This paper proposes a novel method to calibrate the g-sensitive misalignments of an RLG triad in linear vibration environments. With the SINS is attached to a linear vibration bench through outer rubber dampers, rocking of the SINS can occur when the linear vibration is performed on the SINS. Therefore, linear vibration environments can be created to simulate the harsh environment during aircraft flight. By analyzing the mathematical model of g-sensitive misalignments, the relationship between attitude errors and specific forces as well as angular rates is established, whereby a calibration scheme with approximately optimal observations is designed. Vibration experiments are conducted to calibrate g-sensitive misalignments of the RLG triad. Vibration tests also show that SINS velocity error decreases significantly after g-sensitive misalignments compensation.

In modern aerospace technology, strapdown inertial navigation systems (SINS) are widely used, using fiber-optic (FOG) or ring laser (CLG) gyroscopes. During the operation of such systems, the sensitivity axes are rotated relative to the basic coordinate system. The resulting angles between the axes of the base coordinate system and the axes of sensitivity of the navigation system (non-orthogonality) are one of the factors leading to an increase in the measurement errors of the device, which affects the measurement accuracy. During operation, the system is affected by vibrations of various nature, the impact of which can contribute to the appearance of non-orthogonality. The purpose of this work is to determine the maximum permissible vibration amplitudes affecting the SINS body according to the permissible values ​​of the deviation of the FOG sensitivity axes for two variants of the SINS layout. An approach to determining the permissible amplitudes of an external harmonic impact on the unit of a strapdown inertial navigation system based on fiber-optic or ring laser gyroscopes is considered. A design scheme, mathematical and finite element models for calculating natural frequencies and forced oscillations of a strapdown inertial navigation system unit have been developed. In various frequency ranges, numerical calculations have determined the boundary values ​​of the amplitudes of the external harmonic impact on the base of specific configurations of the SINS assembly. It has been established that dangerous states take place in the region of the 1st natural frequency of the system, as well as near higher frequencies. Comparison of the results for design options 1 and 2 allows us to conclude that in order to weaken the effect of vibrations on the accuracy of the SINS unit, it is advisable that the lowest natural vibration frequencies for the SINS assembly be as high as possible (more than 1000 Hz).
 Key words: vibration; fiber optic gyroscope; strapdown inertial navigation system; finite element method; natural frequencies and modes of vibration.

Several static tests indicate that the Ring Laser Gyro (RLG) bias inside of the Strap-down Inertial Navigation System (SINS) varies remarkably as long time working. Further experiments and analyzing results show that the SINS external metal shell could insure the inside temperature rising gently and evenly, and the RLG drifts could be viewed mainly affected by RLG inside temperature field. In order to ac hieve better RLG stability character istic within full temperature range, investigated the BP and RBF Artificial Neural Networks (ANN) nonlinear modeling and compensation technology. Firstly, introduced two typical structures for BP an d RBF neural networks, and then, take a set of static tests data from 25 $ C to 55 $ C as training samples, separately built up four-l ayer BP and two-layer RBF neural networks for RLG drifts. In order to compare the compensation effects, first-order and second-order piecewise Least Square (LS) fitting technologies are also implemented here. Four new expe rimental data were adopted to check the modeling validity. The compensation results show that the RLG drifts stability could be improved by 20%-40%; the precision of BP network modeling method is better than th at of first-order linear piecewise LS f itting, and the precision of RBF is better than that of second-order piecewise LS fitting. Keywords: Strap-down Inertial Navigation System (SINS), Ring Laser Gyro (RLG), drifts, Artificial Neural Network (ANN), Least Square (LS)

Unscented Kalman filter for SINS alignment

The strap-down inertial navigation system (SINS), especially the SINS composed by dithered ring laser gyroscope (DRLG) is a kind of equipment, which providing high reliability and performance for moving vehicles. However, the mechanical dither which is used to eliminate the “Lock-In” effect can cause vibration disturbance to the INS and lead to dithering coupling problem in the inertial measurement unit (IMU) gyroscope triad, so its further application is limited. Among DRLG errors between the true gyro rotation rate and the measured rotation rate, the frequently considered one is the input axis misalignment between input reference axis which is perpendicular to the mounting surface and gyro angular rate input axis. But the misalignment angle between DRLG dither axis and gyro angular rate input axis is often ignored by researchers, which is amplified by dither coupling problem and that would lead to negative effects especially in high accuracy SINS. In order to study the problem more clearly, the concept of misalignment between DRLG dither axis and gyro angle rate input axis is researched. Considering the error of misalignment is of the order of 10 -3 rad. or even smaller, the best way to measure it is using DRLG itself by means of an angle exciter as an auxiliary. In this paper, the concept of dither axis misalignment is explained explicitly firstly, based on this, the frequency of angle exciter is induced as reference parameter, when DRLG is mounted on the angle exciter in a certain angle, the projections of angle exciter rotation rate and mechanical oscillation rate on the gyro input axis are both sensed by DRLG. If the dither axis has misalignment error with the gyro input axis, there will be four major frequencies detected: the frequency of angle exciter, the dither mechanical frequency, sum and difference frequencies of the former two frequencies. Then the amplitude spectrum of DRLG output signal obtained by the using LabVIEW program. if there are only angle exciter and the dither mechanical frequencies, the misalignment may be too small to be detected, otherwise, the amplitude of the sum and difference frequencies will show the misalignment angle between the gyro angle rate input axis and the dither axis. Finally, some related parameters such as frequency and amplitude of the angle exciter and sample rate are calculated and the results are analyzed. The simulation and experiment result prove the effectiveness of the proposed method..

Flight trials began this month on the Ferranti FIN3000 “Laser Gyro” Strapdown Inertial Navigation System (INS). Initial trials of the prototype system, designed and developed by the Edinburgh‐based Navigation Systems Department, are being carried out in the Company's HS125 aircraft.

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.

Initial alignment is of vital importance to inertial navigation systems (INS). The two main methods of self-alignment, the traditional gyro-compassing alignment method and the Kalman filter method, are discussed. We apply and compare the two methods via simulation and test in use of a strapdown inertial navigation system (SINS) based fiber optic gyroscope (FOG), respectively. The simulation and test show that Kalman filter method is better in convergent time and accuracy of attitude and azimuth, however, gyro-compassing alignment method is better in robustness. This provides a useful reference for designing alignment algorithm for FOG SINS.

In this paper, we propose a systematic framework for the autonomous navigation system based on distributed filtering for an Unmanned Aerial Vehicle (UAV). The proposed framework consists of the design and algorithm of the autonomous navigation. Therein, the camera mounted on the UAV functions as a navigation sensor targeted for navigation and positioning. In order to reduce the computational complexity and exclude the risk caused by Global Positioning System (GPS) outage, an autonomous navigation system based on distributed filtering is designed and realized. When GPS is available by monitoring the GPS integrity, sensor information from Strapdown Inertial Navigation System (SINS) and GPS is fused using a 7-state Conventional Kalman Filter (CKF) to estimate the full UAV state (position, velocity and attitude); when GPS is unavailable, sensor information from gyroscopes, accelerometers and magnetometer is fused using a 4-state Extended Kalman Filter (EKF) to estimate the attitude and heading of the UAV, and sensor information from SINS and vision positioning system is fused using a 7-state Incoordinate Interval Kalman Filter (IIKF) to estimate the position and velocity of the UAV. In addition, the second-order vertical channel damping loop is adopted to fuse measurements from a barometer with those of SINS, which suppresses the divergence of the vertical channel error and makes the altitude information calculated by SINS trustable. Both ground and flight experiments of the autonomous navigation system have been carried out. The test results show that the system can provide stabilized attitude information in long durations, and can realize the automatic flight control of UAV.

An improved method of gyrocompass alignment for strap‐down inertial navigation system (SINS) on moving base assisted with Doppler velocity log (DVL) is proposed in this paper. After analyzing the classical gyrocompass alignment principle on static base, implementation of compass alignment on moving base is given in detail. Furthermore, based on analysis of velocity error, latitude error, and acceleration error on moving base, two improvements are introduced to ensure alignment accuracy and speed: (1) the system parameters are redesigned to decrease the acceleration interference and (2) a data repeated calculation algorithm is used in order to shorten the prolonged alignment time caused by changes in parameters. Simulation and test results indicate that the improved method can realize the alignment on moving base quickly and effectively.

For the high-speed and light Autonomous Underwater Vehicle (AUV), it is moving freely, and the speed and attitude are in dynamic movement. And for the micro electromechanical system (MEMS) technology based low-cost strapdown inertial navigation system (SINS), over too large bias drift errors not only made the system functions nonlinear, but also made the estimation for misalignment angle converge to a false value. Therefore, this paper concerns the MEMS-based low-cost SINS initial alignment under the dynamic movement with large initial error. A nonlinear mathematics model of SINS initial alignment has been derived. The nonlinear filter unscented Kalman filter (UKF) is investigated in the nonlinear system to estimate the initial attitude and the MEMS sensor biases simultaneously. The simulation indicated that the algorithm is suitable for initial alignment.

In this paper, A laser gyroscope strap-down inertial navigation system (SDINS) error model with 12 system state parameters is derived, and the observability of SDINS is discussed by observability analysis of piece-wise constant system (PWCS) and observable degree analysis method based on the singular value decomposition (SVD). It is demonstrated that four-position alignment can make the system completely observable, and all the system state parameters can be estimated by Kalman filter. The simulation indicates the efficiency of the analysis results.

It is difficult to rapidly and accurately achieve alignment under complicated interferential environmental conditions with a marine strapdown inertial navigation system (SINS) subject to angular swaying interference and linear motion interference. To solve this problem, an SINS self-alignment algorithm in a complex interference environment is presented in this article. For the angular motion disturbance problem, we used the attitude-updating algorithm in the coagulation inertial coordinate system to reflect the carrier’s attitude change in real time under the swaying interference so as to eliminate the influence of angular motion disturbance. To solve the problem of linear motion interference, we first use Fourier transform analysis (FFT) to analyze the frequency characteristics of linear motion interference signals. We then adaptively decompose the original signal into a series of signal components with different time scales and local characteristics according to the amplitude and frequency using the modified ensemble empirical mode decomposition (MEEMD) algorithm. Finally, we remove and reconstruct the disturbing signal components according to the frequency characteristics of the disturbing signal to filter the line motion disturbing signal. We use the MEEMD algorithm to conduct a ship alignment test. After pre-filtering by MEEMD, high accuracy can be achieved after 100 s of alignment, and the mean square error of the heading angle is 2.85′. The test results show that the MEEMD method has a good noise removal effect. The anti-jamming initial alignment algorithm based on the MEEMD algorithm has the ability to suppress the interference of angular motion and linear motion and achieve high-precision anti-jamming initial alignment under the complicated interferential environmental conditions with angular swaying interference and linear motion interference.

A novel nonlinear attitude error model based on square root cubature information filter (SR-CIF) is proposed aiming to speed up the convergence rate of DVL-aided strapdown inertial navigation system (SINS) in-motion initial alignment for autonomous underwater vehicle (AUV) when both large initial heading error and large initial level attitude errors exist The new nonlinear attitude error model and measurement model are applicable for initial alignment after a short-time coarse alignment with large attitude errors exist. The SR-CIF embeds the whole iterative process into the data structure framework of the information filtering, replacing the covariance matrix in the square root cubature Kalman filter (SR-CKF) with an information matrix which simplifies the filter initialization. The square root technology ensures the symmetry and positive definiteness of the information matrix with enhanced stability of the filter. The simulation and experimental results indicate that the proposed DVL-aided alignment filter is effective with large initial attitude errors. The rate of convergence and estimation accuracy of the SR-CIF is higher than that of the conventional SR-CKF with large attitude misalignments.

Recently, the Strapdown Inertial Navigation System (SDINS) has been successfully integrated with Global Positioning System (GPS) to obtain reliable navigation solutions. SDINS is based on Micro-Electro Mechanical System (MEMS) sensors such as accelerometer and gyroscope sensors. To improve the overall performance of the integrated navigation system, the Inertial Measurement Unit (IMU) readings is treated through effectively band-limiting the high frequency noise using the Discrete Wavelet Multi-resolution Algorithm (DWMRA) as a first step, while the second step is to enhance the navigation position and velocity through utilizing the Nonlinear Autoregressive model with eXogenous inputs (NARX) to fuse GPS and INS systems. The performance of the proposed integrated navigation system is validated through comparison with other systems. Finally, the obtained results suggest a promising and superior prospect for NARX in the field of navigation for low-cost IMU’s during GPS denied signals, since it outperforms the Conventional Neural Network (CNN) and Extended Kalman Filter (EKF) by 84% and 92%, respectively.