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.

Research on Adaptive Kalman Filter for INS

2017 ◽  
Vol 872 ◽  
pp. 316-320
Author(s):  
Kai Xia Wei
Keyword(s):  
Kalman Filter ◽  
Response Speed ◽  
Measured Data ◽  
Specific Method ◽  
Alignment Error ◽  
Initial Alignment ◽  
Adaptive Kalman Filter ◽  
Noise Interference ◽  
Kalman Filter Algorithm ◽  
The Stability

Due to sensor accuracy and noise interference and other reasons, the measured data may be inaccurate or even wrong. This will reduce the accuracy of the filter and the reliability and response speed of the Kalman filter, and even make the Kalman filter lose the stability. In this paper, a new INS initial alignment error model and observation model are derived for the errors in INS initial alignment. The adaptive Kalman filter is built to improve the stability and the accuracy of filter. The specific method is to make the adaptive Kalman filter manage to correct the filter online by getting the observed data. The simulation results show the proposed algorithm improves the accuracy of initial alignment in SINS, and prove the adaptive Kalman filter is effective. The main innovation in this paper is to manage to build the adaptive Kalman filter to modify the filter online by using the observed data. The adaptive Kalman filter algorithm improves the accuracy of the filter.

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 Online Estimation of Li-ion Battery SOC for Electric Vehicles Based on An Improved AEKF

2019 ◽  
Vol 118 ◽  
pp. 02025 ◽  
Author(s):  
Kaihui Feng ◽  
Bibin Huang ◽  
Qionghui Li ◽  
Hu Yan
Keyword(s):  
Kalman Filter ◽  
Electric Vehicles ◽  
Equivalent Circuit Model ◽  
Estimation Accuracy ◽  
Extend Kalman Filter ◽  
Online Estimation ◽  
Soc Estimation ◽  
Kalman Filter Algorithm ◽  
Li Ion ◽  
Influence Of Noise

The purpose of this paper is to discuss how to eliminate the influence of noise time -varying characteristics on the accuracy of SOC estimation. Based on the matlab/simulink platform, the Thevenin equivalent circuit model of the battery is built, and an improved Adaptive Extend Kalman Filter (AEKF) is designed, which is compared with the Extend Kalman filter algorithm (EKF).The simulation results are shown that the improved AEKF algorithm results in effective online estimation SOC and the estimation accuracy is higher than the EKF algorithm.

scholarly journals Underwater Bearing-Only Multitarget Tracking in Dense Clutter Environment Based on PMHT

2020 ◽  
Vol 38 (2) ◽  
pp. 359-365
Author(s):  
Xiaohua Li ◽  
Ya'an Li ◽  
Xiaofeng Lu ◽  
Chenxu Zhao ◽  
Jing Yu
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Data Association ◽  
Estimation Accuracy ◽  
Multitarget Tracking ◽  
Single Observation ◽  
Multiple Observations ◽  
Multiple Hypothesis Tracking ◽  
Kalman Filter Algorithm ◽  
Present Algorithm

Underwater bearing-only multitarget tracking in clutter environment is challenging because of the measurement nonlinearity, range unobservability, and data association uncertainty. In terms of the principle of expectation maximization, combining the extended Kalman filter (EKF) and unscented Kalman filter algorithm(UKF), a new bearing-only multi-sensor multitarget tracking via probabilistic multiple hypothesis tracking(PMHT) algorithm is proposed. The PMHT algorithm introduces an association variable to deal with the data association uncertainty problem between the measurements and the targets. Furthermore, the EKF-based PMHT for multi-sensor multitarget system is simplified, which obviate the need to "stack" the synthetic measurements and can reduce the computation cost. The estimation accuracy of the EKF based on PMHT approach and UKF based on PMHT approach in simulation experiments for underwater bearing-only cross-moving targets and closely spaced targets for the case of stationary multiple observations and maneuvering single observation under dense clutter environment is analyzed. The experimental results demonstrate that the present algorithm is very well in a highly clutter environment and its computational load is low, which confirms the effectiveness of the algorithm to the bearing-only multitarget tracking in dense clutter.

Kalman Filter Based Sensor Fusion for a Mobile Manipulator

2019 ◽  
Author(s):  
Barnaba Ubezio ◽  
Shashank Sharma ◽  
Guglielmo Van der Meer ◽  
Michele Taragna
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Inertial Sensor ◽  
Noise Model ◽  
Estimation Accuracy ◽  
Mobile Manipulator ◽  
End Effector ◽  
Kalman Filter Algorithm ◽  
Working Frequency ◽  
Mems Imu

Abstract End-effector tracking for a mobile manipulator is achieved through Sensor Fusion techniques, implemented with a particular visual-inertial sensor suite and an Extended Kalman Filter algorithm. The suite is composed of an Optitrack motion capture system and a Honeywell HG4930 MEMS IMU, for which a further analysis on the mathematical noise model is reported. The filter is constructed in such a way that its complexity remains constant and independent of the visual algorithm, with the possibility of inserting additional sensors, to further improve the estimation accuracy. Experiments in real-time have been performed with the 12-DOF KUKA VALERI robot, extracting the position and the orientation of the end-effector and comparing their estimates with pure sensor measurements. Along with the physical results, issues related to calibration, working frequency and physical mounting are described.

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.

Sign in / Sign up

Export Citation Format

Share Document