scholarly journals Novel SINS Initial Alignment Method under Large Misalignment Angles and Uncertain Noise Based on Nonlinear Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Bo Yang ◽  
Xiaosu Xu ◽  
Tao Zhang ◽  
Jin Sun ◽  
Xinyu Liu
Keyword(s):  
Kalman Filter ◽  
Nonlinear Filter ◽  
Nonlinear Filters ◽  
Alignment Method ◽  
Initial Alignment ◽  
Numerical Accuracy ◽  
Alignment Problem ◽  
Sampling Problem ◽  
Simulation Results ◽  
Better Than

For the SINS initial alignment problem under large misalignment angles and uncertain noise, two novel nonlinear filters, referred to as transformed unscented quadrature Kalman filter (TUQKF) and robust transformed unscented quadrature Kalman filter (RTUQKF), are proposed in this paper, respectively. The TUQKF sets new deterministic sigma points to address the nonlocal sampling problem and improve the numerical accuracy. The RTUQKF is the combination ofH∞technique and TUQKF. It improves the accuracy and robustness of state estimation. Simulation results indicate that TUQKF performs better than traditional filters when misalignment angles are large. Turntable and vehicle experiments results indicate that, under the condition of uncertain noise, the performances of RTUQKF are better than other filters and more robust. These two methods can effectively further increase precision and convergence speed of SINS initial alignment.

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.

scholarly journals Discrete EKF with pairwise Time Correlated Measurement Noise for Image-Aided Inertial Integrated Navigation

2014 ◽  
Vol II-2 ◽  
pp. 61-66 ◽  
Author(s):  
N. S. Gopaul ◽  
J. G. Wang ◽  
B. Hu
Keyword(s):  
Kalman Filter ◽  
Inertial Navigation ◽  
Measurement Noise ◽  
Integrated Navigation ◽  
Process Noise ◽  
Shaping Filter ◽  
Egomotion Estimation ◽  
Simulation Results ◽  
The One ◽  
Better Than

An image-aided inertial navigation implies that the errors of an inertial navigator are estimated via the Kalman filter using the aiding measurements derived from images. The standard Kalman filter runs under the assumption that the process noise vector and measurement noise vector are white, i.e. independent and normally distributed with zero means. However, this does not hold in the image-aided inertial navigation. In the image-aided inertial integrated navigation, the relative positions from optic-flow egomotion estimation or visual odometry are <i>pairwise</i> correlated in terms of time. It is well-known that the solution of the standard Kalman filter becomes suboptimal if the measurements are colored or time-correlated. Usually, a shaping filter is used to model timecorrelated errors. However, the commonly used shaping filter assume that the measurement noise vector at epoch <i>k</i> is not only correlated with the one from epoch <i>k</i> &ndash; 1 but also with the ones before epoch <i>k</i> &ndash; 1 . The shaping filter presented in this paper uses Cholesky factors under the assumption that the measurement noise vector is pairwise time-correlated i.e. the measurement noise are only correlated with the ones from previous epoch. Simulation results show that the new algorithm performs better than the existing algorithms and is optimal.

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.

Application of Adaptive Kalman Filter Technique in Initial Alignment of Single-Axial Rotation Strap-Down Inertial Navigation System

2012 ◽  
Vol 466-467 ◽  
pp. 617-621
Author(s):  
Song Tian Shang ◽  
Wen Shao Gao
Keyword(s):  
Kalman Filter ◽  
Axial Rotation ◽  
Estimation Accuracy ◽  
Initial Alignment ◽  
Adaptive Kalman Filter ◽  
Rotation System ◽  
Kalman Filter Algorithm ◽  
Axis Rotation ◽  
Single Axis Rotation ◽  
Better Than

In order to improve the accuracy of initial alignment which determines the accuracy of navigation, a Sage-Husa adaptive kalman filter algorithm is applied to SINS initial alignment of single-axis rotation system. The simulation result further shows that in the case of inaccurate statistical property of noise, the estimation accuracy of Sage-Husa adaptive kalman filter is better than the conventional kalman filter.

scholarly journals A Method for SINS Alignment with Large Initial Misalignment Angles Based on Kalman Filter with Parameters Resetting

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Xixiang Liu ◽  
Xiaosu Xu ◽  
Yiting Liu ◽  
Lihui Wang
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Nonlinear Problem ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Error Model ◽  
The State ◽  
Alignment Method ◽  
Initial Alignment ◽  
Alignment Process

In the initial alignment process of strapdown inertial navigation system (SINS), large initial misalignment angles always bring nonlinear problem, which causes alignment failure when the classical linear error model and standard Kalman filter are used. In this paper, the problem of large misalignment angles in SINS initial alignment is investigated, and the key reason for alignment failure is given as the state covariance from Kalman filter cannot represent the true one during the steady filtering process. According to the analysis, an alignment method for SINS based on multiresetting the state covariance matrix of Kalman filter is designed to deal with large initial misalignment angles, in which classical linear error model and standard Kalman filter are used, but the state covariance matrix should be multireset before the steady process until large misalignment angles are decreased to small ones. The performance of the proposed method is evaluated by simulation and car test, and the results indicate that the proposed method can fulfill initial alignment with large misalignment angles effectively and the alignment accuracy of the proposed method is as precise as that of alignment with small misalignment angles.

scholarly journals An Improved SINS Alignment Method Based on Adaptive Cubature Kalman Filter

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5509 ◽  
Author(s):  
Yonggang Zhang ◽  
Geng Xu ◽  
Xin Liu
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Real Life ◽  
Covariance Matrices ◽  
The Body ◽  
Alignment Method ◽  
Initial Alignment ◽  
Noise Covariance Matrix ◽  
Cubature Kalman Filter ◽  
Noise Covariance

Initial alignment is critical and indispensable for the inertial navigation system (INS), which determines the initial attitude matrix between the reference navigation frame and the body frame. The conventional initial alignment methods based on the Kalman-like filter require an accurate noise covariance matrix of state and measurement to guarantee the high estimation accuracy. However, in a real-life practical environment, the uncertain noise covariance matrices are often induced by the motion of the carrier and external disturbance. To solve the problem of initial alignment with uncertain noise covariance matrices and a large initial misalignment angle in practical environment, an improved initial alignment method based on an adaptive cubature Kalman filter (ACKF) is proposed in this paper. By virtue of the idea of the variational Bayesian (VB) method, the system state, one step predicted error covariance matrix, and measurement noise covariance matrix of initial alignment are adaptively estimated together. Simulation and vehicle experiment results demonstrate that the proposed method can improve the accuracy of initial alignment compared with existing methods.

scholarly journals An Improved Initial Alignment Method for Rocket Navigation Systems

2013 ◽  
Vol 66 (5) ◽  
pp. 737-749 ◽  
Author(s):  
Lihua Zhu ◽  
Xianghong Cheng
Keyword(s):  
Kalman Filter ◽  
Impulse Response ◽  
Finite Impulse Response ◽  
Wind Effect ◽  
Navigation Systems ◽  
Alignment Method ◽  
Initial Alignment ◽  
Online Estimation ◽  
Sensor Errors ◽  
Lever Arm Effect

This paper proposes an algorithm for the initial alignment method for rocket navigation systems. It uses the inertial freezing alignment to resolve the attitude matrix with respect to its fast and robust characteristics. Due to disturbances from the swaying base environment, such as people walking and wind effect, which would consequently result in a great lever arm effect, a Finite Impulse Response (FIR) filter is utilized to decrease the noise in the accelerometers' measurement. In addition, there are sensor errors in the system; the online estimation of gyroscopes' drift with a Kalman filter is adopted to achieve compensation. Numerical results from a simulated rocket initial alignment experiment are reported to demonstrate the effectiveness of the method.

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.

scholarly journals Indoor Intruder Tracking Using Visible Light Communications

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4578 ◽  
Author(s):  
Farah Alsalami ◽  
Zahir Ahmad ◽  
Stanislav Zvanovec ◽  
Paul Haigh ◽  
Olivier Haas ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Visible Light ◽  
State Estimation ◽  
Ambient Noise ◽  
Estimation Error ◽  
Visible Light Communication ◽  
Visible Light Communications ◽  
Simulation Results ◽  
Better Than ◽  
Comprehensive Study

This paper proposes a comprehensive study of indoor intruder tracking using visible light communication (VLC). A realistic indoor VLC channel was developed, taking into consideration reflections, shadowing, and ambient noise. The intruder was considered smart and aiming to escape tracking. This was modelled by adding noise and disturbance to the intruder’s trajectory. We propose to extend the application of minimax filtering from state estimation in the radio frequency (RF) domain to intruder tracking using VLC. The performance of the proposed method was examined and compared with Kalman filter for both VLC and RF. The simulation results showed that the minimax filter provided marginally better tracking and was more robust to the adversary behavior of the intruder than Kalman filter, with less than 0.5 cm estimation error. In addition, minimax was significantly better than Kalman filter for RF tracking applications.

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