scholarly journals Polar Transversal Initial Alignment Algorithm for UUV with a Large Misalignment Angle

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3231
Author(s):  
Zheping Yan ◽  
Lu Wang ◽  
Tongda Wang ◽  
Honghan Zhang ◽  
Zewen Yang
Keyword(s):  
Polar Region ◽  
Unscented Kalman Filter ◽  
Equatorial Region ◽  
Alignment Algorithm ◽  
Misalignment Angle ◽  
Initial Alignment ◽  
Unmanned Underwater Vehicles ◽  
Alignment Algorithms ◽  
Simulation And Experiment ◽  
Dynamics Models

The conventional initial alignment algorithms are invalid in the polar region. This is caused by the rapid convergence of the Earth meridians in the high-latitude areas. However, the initial alignment algorithms are important for the accurate navigation of Unmanned Underwater Vehicles. The polar transversal initial alignment algorithm is proposed to overcome this problem. In the polar transversal initial alignment algorithm, the transversal geographic frame is chosen as the navigation frame. The polar region in the conventional frames is equivalent to the equatorial region in the transversal frames. Therefore, the polar transversal initial can be effectively applied in the polar region. According to the complex environment in the polar region, a large misalignment angle is considered in this paper. Based on the large misalignment angle condition, the non-linear dynamics models are established. In addition, the simplified unscented Kalman filter (UKF) is chosen to realize the data fusion. Two comparison simulations and an experiment are performed to verify the performance of the proposed algorithm. The simulation and experiment results indicate the validity of the proposed algorithm, especially when large misalignment angles occur.

scholarly journals Research on an Improved Method for Foot-Mounted Inertial/Magnetometer Pedestrian-Positioning Based on the Adaptive Gradient Descent Algorithm

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4105 ◽  
Author(s):  
Qiuying Wang ◽  
Juan Yin ◽  
Aboelmagd Noureldin ◽  
Umar Iqbal
Keyword(s):  
Kalman Filter ◽  
Gradient Descent ◽  
Rapid Development ◽  
Alignment Algorithm ◽  
Magnetic Disturbance ◽  
Misalignment Angle ◽  
Initial Alignment ◽  
Positioning Systems ◽  
Descent Algorithm ◽  
Gradient Descent Algorithm

Foot-mounted Inertial Pedestrian-Positioning Systems (FIPPSs) based on Micro Inertial Measurement Units (MIMUs), have recently attracted widespread attention with the rapid development of MIMUs. The can be used in challenging environments such as firefighting and the military, even without augmenting with Global Navigation Satellite System (GNSS). Zero Velocity Update (ZUPT) provides a solution for the accumulated positioning errors produced by the low precision and high noise of the MIMU, however, there are some problems using ZUPT for FIPPS, include fast-initial alignment and unobserved heading misalignment angle, which are addressed in this paper. Our first contribution is proposing a fast-initial alignment algorithm for foot-mounted inertial/magnetometer pedestrian positioning based on the Adaptive Gradient Descent Algorithm (AGDA). Considering the characteristics of gravity and Earth’s magnetic field, measured by accelerometers and magnetometers, respectively, when the pedestrian is standing at one place, the AGDA is introduced as the fast-initial alignment. The AGDA is able to estimate the initial attitude and enhance the ability of magnetic disturbance suppression. Our second contribution in this paper is proposing an inertial/magnetometer positioning algorithm based on an adaptive Kalman filter to solve the problem of the unobserved heading misalignment angle. The algorithm utilizes heading misalignment angle as an observation for the Kalman filter and can improve the accuracy of pedestrian position by compensating for magnetic disturbances. In addition, introducing an adaptive parameter in the Kalman filter is able to compensate the varying magnetic disturbance for each ZUPT instant during the walking phase of the pedestrian. The performance of the proposed method is examined by conducting pedestrian test trajectory using MTi-G710 manufacture by XSENS. The experimental results verify the effectiveness and applicability of the proposed method.

scholarly journals Moving State Marine SINS Initial Alignment Based on High Degree CKF

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yong-Gang Zhang ◽  
Yu-Long Huang ◽  
Zhe-Min Wu ◽  
Ning Li
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Alignment Method ◽  
Misalignment Angle ◽  
Initial Alignment ◽  
Central Difference ◽  
Angle Condition ◽  
Cubature Kalman Filter ◽  
High Degree ◽  
Central Difference Kalman Filter

A new moving state marine initial alignment method of strap-down inertial navigation system (SINS) is proposed based on high-degree cubature Kalman filter (CKF), which can capture higher order Taylor expansion terms of nonlinear alignment model than the existing third-degree CKF, unscented Kalman filter and central difference Kalman filter, and improve the accuracy of initial alignment under large heading misalignment angle condition. Simulation results show the efficiency and advantage of the proposed initial alignment method as compared with existing initial alignment methods for the moving state SINS initial alignment with large heading misalignment angle.

scholarly journals An Improved Strong Tracking Kalman Filter Algorithm for the Initial Alignment of the Shearer

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yuming Chen ◽  
Wei Li ◽  
Gaifang Xin ◽  
Hai Yang ◽  
Ting Xia
Keyword(s):  
Kalman Filter ◽  
Adaptive Filtering ◽  
Alignment Algorithm ◽  
Forgetting Factor ◽  
Initial Alignment ◽  
Simulation And Experiment ◽  
Noise Covariance ◽  
Kalman Filter Algorithm ◽  
Fading Factor ◽  
Sequential Filter

The strap-down inertial navigation system (SINS) is a commonly used sensor for autonomous underground navigation, which can be used for shearer positioning under a coal mine. During the process of initial alignment, inaccurate or time-varying noise covariance matrices will significantly degrade the accuracy of the initial alignment of the shearer. To overcome the performance degradation of the existing initial alignment algorithm under complex underground environment, a novel adaptive filtering algorithm is proposed by the integration of the strong tracking Kalman filter and the sequential filter for the initial alignment of the shearer with complex underground environment. Compared with the traditional multiple fading factor strong tracking Kalman filter (MSTKF) method, the proposed MSTSKF algorithm integrates the advantage of strong tracking Kalman filter and sequential filter, and multiple fading factor and forgetting factor for east and north velocity measurement are designed in the algorithm, respectively, which can effectively weaken the coupling relationship between the different states and increase strong robustness against process uncertainties. The simulation and experiment results show that the proposed MSTSKF method has better initial alignment accuracy and robustness than existing strong tracking Kalman filter algorithm.

Study on SRUKF Applied in Initial Alignment with Large Misalignment Angle on Stationary Base of SINS

2011 ◽  
Vol 383-390 ◽  
pp. 5088-5093 ◽  
Author(s):  
Kai Cheng ◽  
Chun Mei Huang ◽  
Yue Yuan Zhao
Keyword(s):  
Kalman Filter ◽  
Nonlinear System ◽  
Inertial Navigation System ◽  
Unscented Kalman Filter ◽  
Estimation Accuracy ◽  
Alignment Error ◽  
Square Root ◽  
Misalignment Angle ◽  
Initial Alignment ◽  
Better Than

The initial alignment error model of SINS (Strap-down Inertial Navigation System) with large misalignment angle is nonlinear. The traditional EKF (Extended Kalman Filter) was used to linearization a nonlinear system, but its performance is limited. In this paper we use the SRUKF (Square Root Unscented Kalman Filter) to process this nonlinear system and the results indicate that SRUKF is better than EKF in convergence speed and estimation accuracy.

A Rapid Initial Alignment Alogrithm Based on Strapdown Gyrocompass

2012 ◽  
Vol 532-533 ◽  
pp. 1563-1567 ◽  
Author(s):  
Si Hai Li ◽  
Gong Min Yan ◽  
Peng Xiang Yang ◽  
Yong Yuan Qin
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Alignment Algorithm ◽  
Initial Alignment ◽  
Basic Principles ◽  
Nonlinear Error ◽  
Alignment Algorithms ◽  
Navigation Computer ◽  
Large Memory

The basic principles for stabilized gyrocompass initial alignment are analyzed in platform inertial navigation system (PINS), then similar principles and initial alignment algorithms suitable for programming are proposed for strapdown inertial navigation system (SINS). The scheme of SINS gyrocompass initial alignment can be divided into four steps, including leveling alignment with header uncertainty, coarse header alignment, leveling realignment and gyrocompass alignment for header. By simplifying SINS nonlinear error model under header uncertainty, the formula of coarse header alignment is deduced. On the assumption of navigation computer having large memory and powerful computing ability, and basing on the ‘multiformity’ of SINS mathematical platform and the ability to attitude reverse control, a specific progress for SINS rapid gyrocompass alignment is introduced and designed in detail. Finally, some tests prove that the proposed alignment algorithm in this paper is effective.

scholarly journals A Polar Initial Alignment Algorithm for Unmanned Underwater Vehicles

Sensors ◽  
2017 ◽  
Vol 17 (12) ◽  
pp. 2709 ◽  
Author(s):  
◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Underwater Vehicles ◽  
Alignment Algorithm ◽  
Initial Alignment ◽  
Unmanned Underwater Vehicles

A new robust quaternion-based initial alignment algorithm for stationary strapdown inertial navigation systems

2020 ◽  
Vol 234 (12) ◽  
pp. 1913-1925
Author(s):  
Habib Ghanbarpourasl
Keyword(s):  
Kalman Filter ◽  
Inertial Navigation ◽  
Bias Error ◽  
Alignment Algorithm ◽  
Navigation Systems ◽  
Initial Alignment ◽  
Unknown Parameters ◽  
Alignment Algorithms ◽  
Alignment Process ◽  
Strapdown Inertial Navigation

A new robust quaternion Kalman filter is developed for accurate alignment of stationary strapdown inertial navigation system. Most fine alignment algorithms have tried to estimate the biases of gyroscopes and accelerometers to reduce the errors of the alignment process. In stationary platforms, due to fixed inputs for sensors, the summation of various errors such as fixed bias, misalignment, scale factor, and nonlinear errors acts like one bias error, and then the identification of each error will be impossible. The observability of gyros and accelerometers’ biases has also been studied. But, nowadays, we know that all of these unknown parameters are not observable. Then this problem can increase the complication of the alignment algorithm. The accelerometers’ errors mainly affect the errors of the roll and pitch angles, but a big portion of the heading’s error results from the gyroscopes’ errors. Modeling of all errors as additional states without considering the observability parameters has no benefits, but will increase the filter’s dimension, so the filter’s performance will decrease. In this study, due to the observability problem, a new robust multiplicative quaternion Kalman filter is designed for the alignment of a stationary platform. The presented algorithm does not estimate the sensors’ errors, but it is robust to uncertainty in the sensors’ errors. In the proposed scheme, the bounds of parameters’ errors are introduced to filter, and the filter tries to remain robust with respect to these uncertainties. The method uses the benefits of quaternions in attitude modeling, and then the robust filter is adapted to work with quaternions. The ability of the new algorithm is evaluated with MATLAB simulations. The outcomes show that the presented algorithm is more accurate than other traditional methods. The extended Kalman filter with accelerometers’ outputs and the horizontal velocities as the measurement equations and additive quaternion Kalman filter are used for comparisons.

An Improved Initial Alignment Method of Strap-Down Inertial Navigation System on a Swaying Base

2013 ◽  
Vol 415 ◽  
pp. 143-148
Author(s):  
Li Hua Zhu ◽  
Xiang Hong Cheng
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Alignment Algorithm ◽  
Alignment Method ◽  
Initial Alignment ◽  
Vehicle Simulation ◽  
Robust Property ◽  
Simulation Results ◽  
Lever Arm Effect ◽  
The Impact

The design of an improved alignment method of SINS on a swaying base is presented in this paper. FIR filter is taken to decrease the impact caused by the lever arm effect. And the system also encompasses the online estimation of gyroscopes’ drift with Kalman filter in order to do the compensation, and the inertial freezing alignment algorithm which helps to resolve the attitude matrix with respect to its fast and robust property to provide the mathematical platform for the vehicle. Simulation results show that the proposed method is efficient for the initial alignment of the swaying base navigation system.

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