In-Flight Alignment Method of SINS/GPS Integrated Navigation System Based on IUKF

There are the scale factor error of LDV (laser Doppler velocimeter) and the misalignment between the SINS (Strapdown inertial navigation system) and the vehicle in a SINS/LDV integrated navigation system. In this paper, the effects of these errors on the attitude, velocity and position of dead reckoning are derived, and a new online calibration method aiming to calibrate the scale factor of LDV and the misalignment between the SINS and the vehicle for the integrated system is put forward. This method, which is utilize the velocity and position of the Global Position System (GPS) as references, use the velocity error and position error of dead reckoning to estimate these errors. Through simulation and experiment, the validity and feasibility of the method are verified. The results show that the scale factor and the misalignment can be calibrated with satisfying accuracy, and the related research can provide technical support for high precision navigation of SINS/LDV integrated navigation systems.

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.

The constraints of weight, volume and power for Small unmanned air vehicle (UAV) restrict the application of sensors with heavy and good performance and powerful processors. This paper presents a real-time solution of autonomous flight navigation and its results for small UAV by applying small, cheap, low precision and low-power integrated navigation system, which includes Strap-down Inertial Navigation System (SINS) based on Micro-electro-mechanical system (MEMS) inertial sensors, Global Positioning System (GPS) receiver and magnetometer. The Square-Root Unscented Kalman filter (SR-UKF) for data fusion using in this MEMS-SINS/GPS/ magnetometer integrated navigation system provides continuous and reliable navigation results for the loops of guidance and control for the small UAV with autonomous flight. The whole integrated navigation system algorithm is implemented within low-power embedded microprocessors. The real-time flight test results show that the MEMS-SINS/GPS/magnetometer integrated navigation system is effective and accurate.

Celestial navigation system (CNS) is a completely autonomous navigation technology with strong anti-interference ability and high navigation accuracy, which has a great application prospect. Aim at defect of traditional integrated navigation system using attitude angle measurement celestial navigation system and strapdown inertial navigation system (SINS), which can only correct of strapdown inertial navigation system’s gyro drift. This paper proposes an improved integrated navigation system that add double-star positioning celestial navigation to traditional SINS/CNS integrated navigation system, meanwhile, introduces the fault detection technology based on statistical distance to solve the problem that inaccuracy of measurement information leads to the integrated navigation system’s performance degradation. By analyzing the simulation results, the improved navigation system can inhibit divergence of system and obtain high accurate and stable navigation information.

For the guidance, control and navigation of autonomous vehicles, both positioning and orientation are important for mission success and surveillance. Global positioning system (GPS) and strapdown inertial navigation system (SINS) have complementary characteristics to overcome the demerits of each other and can well perform as integrated navigation system. The observability analysis of low-cost SINS/GPS from many researches shown that the heading and yaw rate gyro bias is unobservable in straight motion and with constant acceleration. This paper presents integration of low-cost SINS/GPS aided with odometer and heading from two antennae GPS receiver to overcome this problem and to control the accuracy degradation during GPS outage. The carrier phase measurements are processed to obtain the heading from two antennae position measurement. The obtained heading and position from GPS and velocity measurement from odometer are used as observation input to extended Kalman filter (EKF) for the heading and positioning accuracy improvement of SINS/odometer/GPS integrated system for land vehicle navigation. When GPS signal is unavailable, the filter switches to SINS/odometer integration to control the position and velocity errors growth during this period. The observation model for heading measurement from two antennae is derived for the designed system. A field test was conducted to check the effectiveness using proposed method for heading and positioning accuracy improvement. The test results showed that Low-cost IMU/Odometer/ Two Antennae GPS integrated systems' accuracy both in position and heading is improved significantly.

High dynamic aircraft is a very attractive new generation vehicles, in which provides near space aviation with large flight envelope both speed and altitude, for example the hypersonic vehicles. The complex flight environments for high dynamic vehicles require high accuracy and stability navigation scheme. Since the conventional Strapdown Inertial Navigation System (SINS) and Global Position System (GPS) federal integrated scheme based on EKF (Extended Kalman Filter) is invalidation in GPS single blackout situation because of high speed flight, a new high precision and stability integrated navigation approach is presented in this paper, in which the SINS, GPS and Celestial Navigation System (CNS) is combined as a federal information fusion configuration based on nonlinear Unscented Kalman Filter (UKF) algorithm. Firstly, the new integrated system state error is modeled. According to this error model, the SINS system is used as the navigation solution mathematic platform. The SINS combine with GPS constitute one error estimation filter subsystem based on UKF to obtain local optimal estimation, and the SINS combine with CNS constitute another error estimation subsystem. A non-reset federated configuration filter based on partial information is proposed to fuse two local optimal estimations to get global optimal error estimation, and the global optimal estimation is used to correct the SINS navigation solution. The χ 2 fault detection method is used to detect the subsystem fault, and the fault subsystem is isolation through fault interval to protect system away from the divergence. The integrated system takes advantages of SINS, GPS and CNS to an immense improvement for high accuracy and reliably high dynamic navigation application. Simulation result shows that federated fusion of using GPS and CNS to revise SINS solution is reasonable and availably with good estimation performance, which are satisfied with the demands of high dynamic flight navigation. The UKF is superior than EKF based integrated scheme, in which has smaller estimation error and quickly convergence rate.

The extended Kalman filter (EKF) has been used widely in Global Position System (GPS) and Strapdown Inertial Navigation Systems (SINS) integrated navigation systems,which simply linearizes all nonlinear models.However, careful treatment of the nonlinearity of the system models is particularly critical when the integrated systems use low cost micro-electro-mechanical (MEMS) inertial sensors. Hence application of the EKF in MEMS inertial sensor-based integrated navigation systems will likely lead to inaccurate results. To overcome the shortcomings of the EKF, the unscented Kalman filter (UKF) has been proposed.The UKF does not require the linearization of the system models. Alternatively it uses a set of deterministically selected "sigma-points",which completely capture the true mean and covariance of the original random vector. Then these sigma-points are propagated through the nonlinear models.This captures the mean and covariance to second order accuracy for arbitrary nonlinear functions. In this paper the EKF and UKF are applied in GPS/SINS integrated systems respectively. Simulation result demonstrates that the performance of the UKF is better than the EKF in MEMS inertial sensor-based SINS/GPS integrated systems.

To improve the navigation accuracy of AUV (autonomous underwater vehicle), SINS (strapdown inertial navigation system), GPS (global positioning system), DVL (Doppler velocity log) and TAN (terrain aided navigation) are adopted in the AUV integrated navigation system. The mathematic model of the AUV integrated navigation system and the observation model of the chosen navigation sensors are built according to the system simulation experiments data. An improved filter based on RBF neural network for adjusting the information sharing factors is designed and implemented in the AUV integrated navigation system. Simulation experiments are carried out according to the mathematic model. It can be concluded from the simulation experiments that the navigation accuracy is improved substantially with the multiple sensors and federated filter in case that colored noise is engaged. The novel integrated navigation system is effective in prohibiting the divergence of the filter and improving fault tolerance ability and it greatly raises the precision of the navigation accuracy for the AUV integrated navigation system.

The fusion of multi-sensor data can provide more accurate and reliable navigation performance than single-sensor methods. However, the general Federated Kalman Filter (FKF) is not suitable for large changes of complex nonlinear systems parameters and is not optimized for effective information sharing coefficients to estimate navigation preferences. This study concerns research on the FKF method for a nonlinear adaptive model based on an improved Genetic Algorithm (GA) for the Strapdown Inertial Navigation System (SINS) / Celestial Navigation System (CNS) / Global Positioning System (GPS) integrated multi-sensor navigation system. An improved fitness function avoids the premature convergence problem of a general GA and decimal coding improves its performance. The improved GA is used to build the adaptive FKF model and to select the optimized information sharing coefficients of the FKF. An Unscented Kalman Filter (UKF) is used to deal with the nonlinearity of integrated navigation system. Finally, a solution and implementation of the system is proposed and verified experimentally.

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.

To solve the problem of information fusion in the strapdown inertial navigation system (SINS)/celestial navigation system (CNS)/global positioning system (GPS) integrated navigation system described by the nonlinear/non-Gaussian error models, a new algorithm called the federated unscented particle filtering (FUPF) algorithm was introduced. In this algorithm, the unscented particle filter (UPF) served as the local filter, the federated filter was used to fuse outputs of all local filters, and the global filter result was obtained. Because the algorithm was not confined to the assumption of Gaussian noise, it was of great significance to integrated navigation systems described by the non-Gaussian noise. The proposed algorithm was tested in a vehicle’s maneuvering trajectory, which included six flight phases: climbing, level flight, left turning, level flight, right turning and level flight. Simulation results are presented to demonstrate the improved performance of the FUPF over conventional federated unscented Kalman filter (FUKF). For instance, the mean of position-error decreases from (0.640×10−6 rad, 0.667×10−6 rad, 4.25 m) of FUKF to (0.403×10−6 rad, 0.251×10−6 rad, 1.36 m) of FUPF. In comparison of the FUKF, the FUPF performs more accurate in the SINS/CNS/GPS system described by the nonlinear/non-Gaussian error models.

The integrated navigation system of strapdown inertial navigation system (SINS) and odometer (OD) is a commonly used vehicle positioning system. The installation errors caused by the difference between the installation positions of SINS and OD mainly include the installation deflection angle and lever arm, which will reduce the positioning accuracy. The traditional calibration method is to expand the installation deflection angle error and lever arm error into state variables, the velocity difference under nonholonomic constraint (NHC) is taken as the measurement to establish the Kalman filter model for online calibration, but this method seriously depends on the sensor accuracy. In order to calibrate the installation errors of low-precision SINS, this paper improves NHC, adds the difference between the centripetal acceleration measured by odometer and calculated by SINS as measurement on the basis of the original measurement, and introduces a low-pass filter to reduce the interference of vehicle vibration on the accelerometer.The experimental results show that the proposed algorithm has better convergence of the installation deflection angle, and the error between the estimation results of the forward lever arm and the actual measurement results is less than 0.1m; After compensating the installation errors of the improved NHC, the maximum positioning errors in the east and north directions are reduced by 63.19% and 43.51% respectively, the height error is also greatly reduced, and the positioning accuracy of the integrated navigation system is significantly improved.

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.

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

In this paper, two new methods are developed to improve the sensitivity of the failure detection system, where the chi-square test is used to monitor the state estimates of the Kalman filter: (1) Using two state propagators, which are reset regularly with the data of the Kalman filter to increase their accuracy and so the detection sensitivity of the system, and used as the failure detection reference alternatively to avoid the risk of using the contaminated state propagator as the reference system for failure detection. (2) Monitoring the state estimates of the Kalman filter individually, so that the test statistics most seriously affected by the failures are utilized to enhance detection sensitivity. The effectiveness of the proposed approaches is demonstrated in the failure detection problem of a SDINS(Strapdown Inertial Navigation System)/GPS(Global Positioning System) integrated navigation system. The simulation results show that the failure sensitivity is remarkably increased. The system functions quite well to detect the soft failures in the gyros and accelerometers.