Research of misalignment between dithered ring laser gyro angle rate input axis and dither axis

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

SINS/ANS integration for augmented performance navigation solution using unscented Kalman filtering

With the advantages of high velocity measurement accuracy and fast dynamic response, the laser Doppler velocimeter (LDV) is expected to replace the odometer to be combined with a strapdown inertial navigation system (SINS) to form a higher precision integrated navigation system. However, LDV scale factor error and misalignment angles between LDV and inertial measurement unit will affect the accuracy of navigation. Considering that not all global navigation satellite system (GNSS) receivers can directly provide velocity information and current mainstream calibration methods are sensitive to the measurement noise and outliers of velocity and position information, a robust calibration method aided by GNSS is proposed in this paper, which is based on position observation. Different from current popular calibration methods, the attitude information of the GNSS/SINS integrated navigation system obtained by an adaptive Kalman filter is used to construct the observation vector together with LDV velocity outputs and GNSS position outputs in this method. The LDV scale factor error and the misalignment angle are determined by the ratio of two observation vector modulus and the Davenport's q-method method, respectively. The accuracy and robustness of the calibration method are verified by one vehicle test with normal GNSS signals and one vehicle test with GNSS signals with outliers. And the horizontal position error of dead reckoning of the calibrated LDV/SINS integrated system are less than 0.0314% and 0.1033% of the mileage, respectively.

In order to solve the strapdown inertial navigation system (INS) alignment problem under the marine mooring condition, the rapid strapdown INS fine alignment method is proposed. This method uses the gravity in the inertial frame to deal with the lineal and angular disturbances. Also the forward and backward processes for strapdown INS calculation and filter estimation are designed. Making use of the forward and backward processes to repeatedly process the saved inertial measurement unit (IMU) data sequence could quickly obtain the initial strapdown attitude matrix. Simulation and trial test validate the performance of the proposed rapid fine strapdown INS alignment.

Self-alignment of full skewed RSINS: Observability analysis and full-observable Kalman filter

A zero-velocity detection method with transformation on generalized likelihood ratio statistical curve

The article presents a method of determining gyro and accelerometer delays during calibration of strapdown inertial navigation systems (SINS) of navigation accuracy. The method uses the Kalman filter in SINS navigation mode and estimates errors in gyro and accelerometer delays and scale factors, sensitivity axes misalignment and drift, coordinates of accelerometer proof masses in the body-fixed frame. Application of SINS navigation mode during calibration reduces the requirements to accuracy of the rate table. The obtained estimates of calibration parameters are self-consistent, minimizing the resulting SINS error. The method was tested during calibration of FOG-based SINS.

Alignment accuracy and alignment time are two important indexes of the initial alignment procedure of a strapdown inertial navigation system (SINS). However, large misalignment angles will bring nonlinear problems of SINS error models and prolong the convergence time of the initial alignment procedure. In this paper, an innovative control scheme is proposed for the initial alignment of SINS on stationary base, which is implemented by active disturbance rejection control (ADRC). Compared with the conventional compass method, the proposed method has a lower requirement on high accuracy of the coarse alignment. Therefore, the total alignment time is decreased, meanwhile the interferences caused by the system errors are decreased and the stability of the closed-loop system is enhanced. Simulations and experiments show that the alignment procedure has been greatly hastened and the accuracy has been improved.

Strapdown inertial navigation systems (SINS) are widely used in modern air transport. The service life of such systems is decades. During this time, the characteristics of such systems may deteriorate due to the degradation of inertial sensors - gyroscopes and accelerometers. Under these conditions, in order to maintain the required accuracy of the SINS during the entire period of operation, it is necessary either to periodically carry out routine maintenance with the onboard system, or to continuously adapt the used sensor measurement model in terms of compensating for errors that arise. The second direction, which we will call calibration, has the advantage over the first one that it is carried out automatically by the system itself and does not require any additional equipment or additional maintenance work. The known method of SINS recalibration is based on the integration of information from the SINS and from the receiver of satellite radio navigation signals (SRNS). Such calibration, firstly, significantly depends on the availability and quality of satellite information, and secondly, it is possible only for integrated navigation systems in which the inertial subsystem is supplemented by a SRNS receiver. In contrast to the known method, this article solves the problem of autonomous SINS calibration, which is carried out after each flight. The solution is based on a linearized inertial navigation error model based on the boundary conditions of the state vector (at the beginning and end of the flight). The external information used consists only of the initial values of the coordinates and the current measurements of the baroaltimeter. As a result of the decision at the end of the flight, the errors of gyroscopes and accelerometers are determined, as well as the error in determining the final values of latitude and longitude in the SINS. Sensor errors can be used in subsequent system activations, which improves the accuracy of its operation as a whole. The analysis of the suitability and effectiveness of the developed methodology was carried out on a typical flight simulation program.

The purpose of this research is to prove the eventuality of using a novel adaptive surrogate model for optimization problems. The adaptive surrogate model is based on iteration sampling and extended radial basis function (ERBF). This method improves the precision by a means that new sample points is placed in the blank area and all the sample points is uniformly distributed in the design region. The precision of the surrogate model is checked using standard error measure to determine whether updating the surrogate model or not. Since the prediction of modal frequencies require structure modal simulations. In order to decrease the number of computer simulations, a Multi-Island GA approach is combined with the adaptive surrogate model to find the optimum modal frequencies of a strapdown inertial navigation system for electric helicopters. The strapdown inertial navigation system is comprised of damping material, counterweight material and inertial navigation sensor. This is a multi-objective functions optimization problem since the modal frequencies are considered from mode 1 to mode 6 in this paper. Several weights of multi-objective functions are utilized to research the modal frequencies. The whole number of 15 sampling points is picked to build the primary surrogate model using Latin hypercube sampling (LHS). The results of adaptive surrogate model show that two new sampling points are needed to reform the precision of the surrogate model under the condition of 2 % confidence bounds. The structure modal optimization results show that the choice of the weights for the multi-objective functions has a major effect on the final optimum modal frequencies. Time- and frequency-domain analysis indicated that the optimum modal frequencies are far away from the excitation frequencies to avoid strapdown inertial navigation system resonance as far as possible.

With the rapid development of networking of vehicles, vehicle positioning problem gets more and more attention. GPS (Global Positioning System) and SINS (strapdown inertial navigation system) are the two technologies in the field of vehicle location. Although the GPS has a relatively high positioning accuracy, it can not get the positioning where satellites are blocked, which requires auxiliary of the other positioning means, to achieve AGPS (Assited Global Positioning System) positioning. SINS can get the vehicle driving parameters by IMU (Inertial Measurement Unit) in the case of no GPS signal and then make use of kinematics law to realize self-positioning. But due to error having a time cumulative, and the external device in the installation process, there is mounting error, and thus can not achieve a higher accuracy location. In order to solve this problem, this paper proposes a Dead reckoning system based on inertial devices to realize the autonomous positioning of the vehicle. The acceleration sensor is used to measure the acceleration of the carrier, and the gyro sensor is used to measure the yaw angle of the vehicle. DR algorithm is used to calculate the approximate position of the vehicle, and the error factor of GPS acquisition is used to correct the vehicle trajectory. In this paper, through the simulation analysis, it is verified that the real-time correction eliminates the error, effectively improves the positioning accuracy, and meets the positioning and navigation requirements of the blind area of GPS signal.

To realize the effective estimation of the inertial measurement unit (IMU) error parameters of a hypersonic vehicle and satisfy the high-precision navigation requirements, a hybrid neural network (HYNN) method that consists of a quasi-Newton semi-determined weight neural network (QNSWNN) and a semi-determined weight recurrent neural network (SWRNN) is proposed. In contrast to conventional neural networks, the weights of the HYNN model, which are determined by the IMU error parameters, have physical meanings. First, the IMU error model and strap-down inertial navigation system (SINS) navigation model are established. Second, the QNSWNN model is constructed based on the SINS/celestial navigation system (CNS) integrated navigation system information. The quasi-Newton is used to adjust the weights, which include the gyroscope error. The rest weights of QNSWNN are fixed based on a SINS attitude calculation model. The gyroscope error parameters can be estimated during the QNSWNN training process. Lastly, the SWRNN model is constructed based on the SINS/GPS integrated navigation system information. The BP algorithm is used to adjust the weights, which include the accelerometer error. The rest weights of SWRNN are fixed based on the SINS navigation calculation model. The accelerometer error parameters can be estimated during the SWRNN training process. The simulation results show that the IMU error parameter can be effectively estimated by the HYNN, and the relative errors are below 25%. Moreover, when the signal of the auxiliary navigation system is interrupted, the HYNN method still has high prediction accuracy for the SINS navigation parameters.

INS/GNSS integration is a well known technique in navigation and, in general, in time-Position-Velocity-Attitude (tPVA) trajectory determination. It is also known, that INS and GNSS are complementary and that the key to their integration is the correct calibration of the Inertial Measurement Unit (IMU) sensors, of the IMU's Inertial Sensor Assembly (ISA) system parameters (axes misalignments and center of navigation), and of the IMU-to-GNSS system parameters (eccentricity vectors). Calibration is performed both at the manufacturer's facilities prior to shipping and "on-the-job," i.e., together with navigation. Classical INS calibration has two parts: INS error modeling and actual INS calibration once an INS error model is available (also called INS calibration model) either at the manufaturer's or on-the-job. INS error modeling is about understanding the error features - systematic and random - of an IMU and its result uses to be dynamic models - Stochastic Differential Equations (SDE) and white noise amplitude - of the main error sources like biases, scale factors and misalignment angles. INS calibration is about solving for the unknowns (states) of the SDE calibration model together with the unknown states of the SDE of motion. The quality of INS calibration is dependent on the mission dynamics. In classical INS/GNSS navigation, the INS calibration model is dynamics independent; i.e., SDEs formulas and white noise figures are derived following a number of established procedures and then kept fixed. However, it can be shown, that the error features and the INS error calibration model of an IMU change from mission to mission or, more to the point, from one to another type of dynamics. It is therefore natural, to investigate the relationship between IMU errors and motion dynamics and, once this is understood, use this knowledge to improve the performance of INS/GNSS tPVA trajectory determination. In this paper we present a method for IMU error analysis and its first results. It consists of using a reference IMU (REF) together with the IMU under test (IUT) for the simultaneous measurement of REF and ITU signals. First, we show how the sensor random behavior changes with dynamics for tactical and low-cost grade IMUs with respect a navigation grade one. Second, we introduce a procedure to analyze the error of the dynamic dat. The procedure is based on inertial data analysis tools as the Allan Variance applied to the errors of the target IMU with respect to the reference IMU.

Focusing on the determining attitude of land vehicles which equipped with antenna platform for communication satellite, the mathematical model of the integrated navigation system of Strapdown Inertial Navigation System(SINS) and Global Positioning System(GPS) has been built at first. Then the observability of the attitude errors of the integrated navigation system has been concluded to be very weak, and the observability of the heading error is weaker than the horizontal attitude's. Due to this characteristic and the requirement of miniaturization and low-cost, a new inertial measurement unit (IMU) structure has been designed, which comprises three gyros and three accelerometers. Two MEMS gyros were installed on the horizontal plane, and a high degree of accuracy fibre optical gyro was installed in the vertical direction. During navigation period, the Kalman filter is used for calculating the navigation information. Both the theoretical analysis and simulation results show that the new unsymmetrical design of IMU can improve the precision of heading significantly, and when this IMU is integrated with GPS, the attitude precision can meet the requirement of the antenna platform control.

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.