Accuracy Improvement of Attitude Determination Systems Using EKF-Based Error Prediction Filter and PI Controller

Accurate attitude and heading reference system (AHRS) play an essential role in navigation applications and human body tracking systems. Using low-cost microelectromechanical system (MEMS) inertial sensors and having accurate orientation estimation, simultaneously, needs optimum orientation methods and algorithms. The error of attitude estimation may lead to imprecise navigation and motion capture results. This paper proposed a novel intermittent calibration technique for MEMS-based AHRS using error prediction and compensation filter. The method, inspired from the recognition of gyroscope’s error and by a proportional integral (PI) controller, can be regulated to increase the accuracy of the prediction. The experimentation of this study for the AHRS algorithm, aided by the proposed prediction filter, was tested with real low-cost MEMS sensors consists of accelerometer, gyroscope, and magnetometer. Eventually, the error compensation was performed by post-processing the measurements of static and dynamic tests. The experimental results present about 35% accuracy improvement in attitude estimation and demonstrate the explicit performance of proposed method.

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Least square estimation-based adaptive complimentary filter for attitude estimation

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331218755234 ◽

2018 ◽

Vol 41 (1) ◽

pp. 235-245 ◽

Cited By ~ 3

Author(s):

Parag Narkhede ◽

Alex Noel Joseph Raj ◽

Vipan Kumar ◽

Vinod Karar ◽

Shashi Poddar

Keyword(s):

Inertial Sensors ◽

Low Cost ◽

Attitude Estimation ◽

Unmanned Vehicles ◽

Least Square ◽

Least Square Estimation ◽

Micro Electro Mechanical Systems ◽

Moving Vehicle ◽

Complementary Filter ◽

Filter Structure

Attitude estimation is one of the core fundamentals for navigation of unmanned vehicles and other robotic systems. With the advent of low cost and low accuracy micro-electro-mechanical systems (MEMS) based inertial sensors, these devices are used ubiquitously for all such commercial grade systems that need motion information. However, these sensors suffer from time-varying bias and noise parameters, which need to be compensated during system state estimation. Complementary filtering is one of such techniques that is used here for estimating attitude of a moving vehicle. However, the complementary filter structure is dependent on user fed gain parameters, KP and KI and needs a mechanism by which they can be obtained automatically. In this paper, an attempt has been made towards addressing this issue by applying least square estimation technique on the error obtained between estimated and measured attitude angles. The proposed algorithm simplifies the design of nonlinear complementary filter by computing the filter gains automatically. The experimental investigation has been carried out over several datasets, confirming the advantage of obtaining gain parameters automatically for the complementary filtering structure.

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Performance Trades for Multiantenna GNSS Multisensor Attitude Determination Systems

International Journal of Aerospace Engineering ◽

10.1155/2018/4871239 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12

Author(s):

Nathan A. Tehrani ◽

Jason N. Gross

Keyword(s):

Attitude Determination ◽

Inertial Sensors ◽

Satellite System ◽

Attitude Estimation ◽

Multiple Antenna ◽

Estimation Performance ◽

Trade Offs ◽

Sensing Platforms ◽

Global Navigation Satellite ◽

System Configurations

We present various performance trades for multiantenna global navigation satellite system (GNSS) multisensor attitude estimation systems. In particular, attitude estimation performance sensitivity to various error sources and system configurations is assessed. This study is motivated by the need for system designers, scientists, and engineers of airborne astronomical and remote sensing platforms to better determine which system configuration is most suitable for their specific application. In order to assess performance trade-offs, the attitude estimation performance of various approaches is tested using a simulation that is based on a stratospheric balloon platform. For GNSS errors, attention is focused on multipath, receiver measurement noise, and carrier-phase breaks. For the remaining attitude sensors, different performance grades of sensors are assessed. Through a Monte Carlo simulation, it is shown that, under typical conditions, sub-0.1-degree attitude accuracy is available when using multiple antenna GNSS data only, but that this accuracy can degrade to degree level in some environments warranting the inclusion of additional attitude sensors to maintain the desired level of accuracy. Further, we show that integrating inertial sensors is more valuable whenever accurate pitch and roll estimates are critical.

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A Robust Nonlinear Observer for Real-Time Attitude Estimation Using Low-Cost MEMS Inertial Sensors

Sensors ◽

10.3390/s131115138 ◽

2013 ◽

Vol 13 (11) ◽

pp. 15138-15158 ◽

Cited By ~ 19

Author(s):

José Guerrero-Castellanos ◽

Heberto Madrigal-Sastre ◽

Sylvain Durand ◽

Lizeth Torres ◽

German Muñoz-Hernández

Keyword(s):

Real Time ◽

Inertial Sensors ◽

Low Cost ◽

Attitude Estimation ◽

Nonlinear Observer ◽

Robust Nonlinear Observer

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Nonlinear Complementary Filter for Attitude Estimation by Fusing Inertial Sensors and a Camera

Sensors ◽

10.3390/s20236752 ◽

2020 ◽

Vol 20 (23) ◽

pp. 6752

Author(s):

Lingxiao Zheng ◽

Xingqun Zhan ◽

Xin Zhang

Keyword(s):

Inertial Sensors ◽

Filter Design ◽

Low Cost ◽

Measurement Model ◽

Attitude Estimation ◽

System Stability ◽

Feature Points ◽

Complementary Filter ◽

Geometry Measurement ◽

Implicit Geometry

Using a standalone camera for pose estimation has been quite a standard task. However, the point correspondence-based algorithms require at least four feature points in the field of view. This paper considers the situation that there are only two feature points. Focusing on the attitude estimation, we propose to fuse a camera with low-cost inertial sensors based on a nonlinear complementary filter design. An implicit geometry measurement model is derived using two feature points in an image. This geometry measurement is fused with the angle rate measurement and vector measurement from inertial sensors using the proposed nonlinear complementary filter with only two parameters to be adjusted. The proposed nonlinear complementary filter is posed directly on the special orthogonal group SO(3). Based on the theory of nonlinear system stability analysis, the proposed filter ensures locally asymptotic stability. A quaternion-based discrete implementation of the filter is also given in this paper for computational efficiency. The proposed algorithm is validated using a smartphone with built-in inertial sensors and a rear camera. The experimental results indicate that the proposed algorithm outperforms all the compared counterparts in estimated accuracy and provides competitive computational complexity.

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Research on Data Fusion Technology of Body Posture Detection Based on Kalman Filter

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.668-669.1003 ◽

2014 ◽

Vol 668-669 ◽

pp. 1003-1006 ◽

Cited By ~ 1

Author(s):

Xian Wei Wang ◽

Fu Cheng Cao

Keyword(s):

Kalman Filter ◽

Attitude Determination ◽

Inertial Sensors ◽

Low Cost ◽

Micro Electro Mechanical System ◽

Angle Measurement ◽

The Body ◽

Body Posture ◽

Posture Estimation ◽

The Impact

This paper discusses the body posture detection problem using low cost Micro-Electro-Mechanical System (MEMS) inertial sensors, for which a complementary sensor fusion solution is proposed. Considering the impact from the noise and bias drifts, through Kalman filter to complete the multi-sensor information fusion, achieved an accurate attitude determination. The experimental results show that, after using Kalman filtering algorithm to fuse acceleration sensor and signal gyroscope, it can effectively eliminate the accumulative error and significantly better dynamic characteristics of attitude angle measurement, Improving the reliability and accuracy of body posture estimation.

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Attitude Estimation with Complementary Filter

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.44-47.3781 ◽

2010 ◽

Vol 44-47 ◽

pp. 3781-3784

Author(s):

Rui Hua Chang ◽

Xiao Dong Mu ◽

Xiao Wei Shen

Keyword(s):

Solid State ◽

Real Time ◽

Inertial Sensors ◽

Low Cost ◽

Estimation Error ◽

Estimation Method ◽

Attitude Estimation ◽

Complementary Filter

An attitude estimation method is presented for a robot using low-cost solid-state inertial sensors. The attitude estimates are obtained from a complementary filter by combining the measurements from the integration of a tri-axis gyro and an aiding system mechanized using a tri-axis accelerometer and a tri-axis magnetometer. The results show that the estimation error is less than 1 degree compare to the reference attitude. It is a simple, yet effective method for attitude estimation, suitable for real-time implementation on a robot.

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Pyroelectric Sensor Integrated Positioning System Design and Implementation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.608-609.903 ◽

2014 ◽

Vol 608-609 ◽

pp. 903-907

Author(s):

Wei Tang ◽

Ji Xiong ◽

Deng Li ◽

J.Y. Zhang ◽

Na Jiang

Keyword(s):

Tracking System ◽

Low Cost ◽

Research Focus ◽

Experiment Data ◽

Positioning Accuracy ◽

Experiment System ◽

Body Tracking ◽

Pyroelectric Detectors ◽

Human Body Tracking ◽

Very High

With the enhancement of society's concern for public safety, human locate system based on pyroelectric detectors is becoming a research focus. We design a low cost human body tracking system based on pyroelectric detectors scattered in the room. According to a huge number of experiment data, we design a high sensitive pyroelectric infrared node with a very high positioning accuracy. We also build an experiment system to collect muti-node experimental data to achieve the purpose of real time positioning with the positioning accuracy of 0.4 meters, which achieves the purpose of indoor positioning.

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Calibration of Low Cost IMU’s Inertial Sensors for Improved Attitude Estimation

Journal of Intelligent & Robotic Systems ◽

10.1007/s10846-020-01259-0 ◽

2020 ◽

Vol 100 (3-4) ◽

pp. 1015-1029

Author(s):

Mingjie Dong ◽

Guodong Yao ◽

Jianfeng Li ◽

Leiyu Zhang

Keyword(s):

Inertial Sensors ◽

Low Cost ◽

Attitude Estimation

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Error Prediction for SINS/GPS after GPS Outage Based on Hybrid KF-UKF

Mathematical Problems in Engineering ◽

10.1155/2015/239426 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 5

Author(s):

Baiqiang Zhang ◽

Hairong Chu ◽

Tingting Sun ◽

Hongguang Jia ◽

Lihong Guo ◽

...

Keyword(s):

Low Cost ◽

Error Model ◽

Integrated System ◽

Error Prediction ◽

Simulation Test ◽

Accuracy Improvement ◽

Unscented Transform ◽

Nonlinear Error ◽

Error Covariance ◽

Mems Imu

The performance of MEMS-SINS/GPS integrated system degrades evidently during GPS outage due to the poor error characteristics of low-cost IMU sensors. The normal EKF is unable to estimate SINS error accurately after GPS outage owing to the large nonlinear error caused by MEMS-IMU. Aiming to solve this problem, a hybrid KF-UKF algorithm for real-time SINS/GPS integration is presented in this paper. The linear and nonlinear SINS error models are discussed, respectively. When GPS works well, we fuse SINS and GPS with KF with linear SINS error model using normal EKF. In the case of GPS outage, we implement Unscented Transform to predict SINS error covariance with nonlinear SINS error model until GPS signal recovers. In the simulation test that we designed, an evident accuracy improvement in attitude and velocity could be noticed compared to the normal EKF method after the GPS signal recovered.

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