Multiple Iterative Kalman Filter SINS Initial Alignment Algorithm

2021 ◽  
Author(s):  
Wence Shi ◽  
Jiangning Xu ◽  
Hongyang He ◽  
Ding Li ◽  
Hongqiong Tang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Alignment Algorithm ◽  
Initial Alignment

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.

Initial Alignment of Shipborne SINS under Ship Motion

2020 ◽  
Vol 28 (3) ◽  
pp. 3-17 ◽  
Author(s):  
G.I. Emel’yantsev ◽  
◽  
A.P. Stepanov ◽  
B.A. Blazhnov ◽  
◽  
...  
Keyword(s):  
Kalman Filter ◽  
Ship Motion ◽  
Second Step ◽  
Alignment Algorithm ◽  
Initial Alignment ◽  
Log Data ◽  
Current Attitude ◽  
Alignment Errors ◽  
Filter Algorithms ◽  
Water Speed

The paper focuses on improving the accuracy and shortening the time of shipborne SINS initial alignment under the ship yaw, roll and pitch. This is achieved by implementing a two-step SINS alignment algorithm. At the first step, the ship current attitude parameters are approximately autonomously estimated by data from gyros and accelerometers with account for its dynamics and using water speed log data. At the second step, the system fine alignment is performed with account for alignment errors after the completion of the first step. Speed and position measurements from external aids are additionally applied during the fine alignment. Kalman filter algorithms are used in the first and second steps. Results from bench and sea tests for SINS on navigation grade FOGs under the ship yaw, roll and pitch motion are provided.

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.

A Novel Initial Alignment Based on the HMM/Steady State KALMAN Filter

2013 ◽  
Vol 562-565 ◽  
pp. 426-430
Author(s):  
Xing Ma ◽  
Lun Chao Zhong ◽  
Jun Li Han ◽  
Chang Shun Liu
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
Alignment Algorithm ◽  
Acceleration Sensor ◽  
Initial Alignment ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Acceleration Data ◽  
Low Pass ◽  
Poor Stability

In allusion to low accurary and poor stability of the initial alginment based on the acceleration sensor, the HMM (Hidden Marco Model) / steady state Kalman filter is designed to solve the problem above. C8051F340 is used to sample the acceleration data of the MEMS acceleration sensor Model1221, then the digital low-pass filter is used to filter the acceleration data. The steady state Kalman filter based on the second order HMM model. The initial alignment algorithm outputs the angle value. The experiments demonstrate that the the HMM / steady state Kalman filter is more stable and more precise than the traditional algorithm.

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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