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

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
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.

MEMS IMU and GPS/Beidou Integration Navigation System Using Interval Kalman Filter

2014 ◽  
Vol 568-570 ◽  
pp. 970-975 ◽  
Author(s):  
Yang Le ◽  
Xiu Feng He ◽  
Ru Ya Xiao
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Low Cost ◽  
Integrated System ◽  
Integrated Navigation ◽  
Dynamics Modeling ◽  
Integrated Navigation System ◽  
Uncertain Dynamics ◽  
Integration Navigation ◽  
Mems Imu

This paper describes an integrated MEMS IMU and GNSS system for vehicles. The GNSS system is developed to accurately estimate the vehicle azimuth, and the integrated GNSS/IMU provides attitude, position and velocity. This research is aimed at producing a low-cost integrated navigation system for vehicles. Thus, an inexpensive solid-state MEMS IMU is used to smooth the noise and to provide a high bandwidth response. The integration system with uncertain dynamics modeling and uncertain measurement noise is also studied. An interval adaptive Kalman filter is developed for such an uncertain integrated system, since the standard extended Kalman filter (SKF) is no longer applicable, and a method of adaptive factor construction with uncertain parameter is proposed for the nonlinear integrated GNSS/IMU system. The results from the actual GNSS/IMU integrated system indicate that the filtering method proposed is effective.

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

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 4055 ◽  
Author(s):  
Farzan Farhangian ◽  
Rene Landry
Keyword(s):  
Attitude Determination ◽  
Inertial Sensors ◽  
Low Cost ◽  
Pi Controller ◽  
Attitude Estimation ◽  
Error Prediction ◽  
Accuracy Improvement ◽  
Body Tracking ◽  
Human Body Tracking ◽  
Prediction Filter

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.

scholarly journals MEMS Based SINS/OD Filter for Land Vehicles’ Applications

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Huisheng Liu ◽  
Zengcai Wang ◽  
Susu Fang ◽  
Chao Li
Keyword(s):  
Navigation System ◽  
Velocity Measurement ◽  
Microelectromechanical Systems ◽  
Low Cost ◽  
Detection Algorithm ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Land Vehicles ◽  
Mems Imu

A constrained low-cost SINS/OD filter aided with magnetometer is proposed in this paper. The filter is designed to provide a land vehicle navigation solution by fusing the measurements of the microelectromechanical systems based inertial measurement unit (MEMS IMU), the magnetometer (MAG), and the velocity measurement from odometer (OD). First, accelerometer and magnetometer integrated algorithm is studied to stabilize the attitude angle. Next, a SINS/OD/MAG integrated navigation system is designed and simulated, using an adaptive Kalman filter (AKF). It is shown that the accuracy of the integrated navigation system will be implemented to some extent. The field-test shows that the azimuth misalignment angle will diminish to less than 1°. Finally, an outliers detection algorithm is studied to estimate the velocity measurement bias of the odometer. The experimental results show the enhancement in restraining observation outliers that improves the precision of the integrated navigation system.

scholarly journals Vibration Error Correction for the FOGs-Based Measurement in a Drilling System Using an Extended Kalman Filter

2021 ◽  
Vol 11 (14) ◽  
pp. 6514
Author(s):  
Lu Wang ◽  
Yuanbiao Hu ◽  
Tao Wang ◽  
Baolin Liu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Measurement Accuracy ◽  
Original Data ◽  
Calibration Method ◽  
Error Model ◽  
Nonlinear Error ◽  
Measurement While Drilling ◽  
Drilling System ◽  
Vibration Error

Fiber-optic gyroscopes (FOGs)-based Measurement While Drilling system (MWD) is a newly developed instrument to survey the borehole trajectory continuously and in real time. However, because of the strong vibration while drilling, the measurement accuracy of FOG-based MWD deteriorates. It is urgent to improve the measurement accuracy while drilling. Therefore, this paper proposes an innovative scheme for the vibration error of the FOG-based MWD. Firstly, the nonlinear error models for the FOGs and ACCs are established. Secondly, a 36-order Extended Kalman Filter (EKF) combined with a calibration method based on 24-position is designed to identify the coefficients in the error model. Moreover, in order to obtain a higher accurate error model, an iterative calibration method has been suggested to suppress calibration residuals. Finally, vibration experiments simulating the drilling vibration in the laboratory is implemented. Compared to the original data, compensated the linear error items, the error of 3D borehole trajectory can only be reduced by a ratio from 10% to 34%. While compensating for the nonlinear error items of the FOG-based MWD, the error of 3D borehole trajectory can be reduced by a ratio from 44.13% to 97.22%. In conclusion, compensation of the nonlinear error of FOG-based MWD could improve the trajectory survey accuracy under vibration.

A Critical Review of the Biological Processes in the Chemical Absorption-biological Reduction Integrated System for NO Removal

2021 ◽  
Vol 07 ◽  
Author(s):  
Wei Li
Keyword(s):  
Catalytic Reduction ◽  
Low Cost ◽  
Reduction Process ◽  
Integrated System ◽  
Future Research ◽  
Small Scale ◽  
Biological Processes ◽  
Chemical Absorption ◽  
Biological Reduction ◽  
Biological Reaction

: Exploring low-cost, green and safe technologies to provide an alternative to the conventional selective catalytic reduction process is key to the control of NOx emitted from small-scale boilers and other industrial processes. To meet the demand, the chemical absorption-biological reduction integrated system has been developing recently. chemical absorption-biological reduction integrated system applies Fe(II)EDTA for NO absorption and iron-reducing and denitrifying bacteria for absorbent regeneration. Many studies have focused on the enhancements of mass transfer and biological reaction, among which the biological processes were the rate-limiting steps. This review summarizes the current researches on the biological processes in the CABR system, which focuses on the mechanism and enhancement of biochemical reactions, and provides the possible directions of future research.

Non-invasive intelligent monitoring system for fault detection in induction motor based on bio piezoelectric sensor using ANN

2022 ◽  
Author(s):  
Massine GANA ◽  
Hakim ACHOUR ◽  
Kamel BELAID ◽  
Zakia CHELLI ◽  
Mourad LAGHROUCHE ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Induction Motor ◽  
Vibration Analysis ◽  
Low Cost ◽  
Piezoelectric Sensor ◽  
Integrated System ◽  
Easy Access ◽  
Non Invasive ◽  
Artificial Neural

Abstract This paper presents a design of a low-cost integrated system for the preventive detection of unbalance faults in an induction motor. In this regard, two non-invasive measurements have been collected then monitored in real time and transmitted via an ESP32 board. A new bio-flexible piezoelectric sensor developed previously in our laboratory, was used for vibration analysis. Moreover an infrared thermopile was used for non-contact temperature measurement. The data is transmitted via Wi-Fi to a monitoring station that intervenes to detect an anomaly. The diagnosis of the motor condition is realized using an artificial neural network algorithm implemented on the microcontroller. Besides, a Kalman filter is employed to predict the vibrations while eliminating the noise. The combination of vibration analysis, thermal signature analysis and artificial neural network provides a better diagnosis. It ensures efficiency, accuracy, easy access to data and remote control, which significantly reduces human intervention.

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