scholarly journals Kalman Filter untuk Mengurangi Derau Sensor Accelerometer pada IMU Guna Estimasi Jarak

AVITEC ◽  
2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Muhammad Ari Roma Wicaksono ◽  
Freddy Kurniawan ◽  
Lasmadi Lasmadi
Keyword(s):  
Kalman Filter ◽  
Measurement Errors ◽  
System Modeling ◽  
Measurement Unit ◽  
Accelerometer Sensor ◽  
Matrix Operations ◽  
Threshold Algorithm ◽  
Average Accuracy ◽  
Measurement Results ◽  
Kalman Filter Algorithm

This study aims to develop a Kalman filter algorithm in order to reduce the accelerometer sensor noise as effectively as possible. The accelerometer sensor is one part of the Inertial Measurement Unit (IMU) used to find the displacement distance of an object. The method used is modeling the system to model the accelerometer system to form mathematical equations. Then the state space method is used to change the system modeling to the form of matrix operations so that the process of the data calculating to the Kalman Filter algorithm is not too difficult. It also uses the threshold algorithm to detect the sensor's condition at rest. The present study had good results, which of the four experiments obtained with an average accuracy of 93%. The threshold algorithm successfully reduces measurement errors when the sensor is at rest or static so that the measurement results more accurate. The developed algorithm can also detect the sensor to move forward or backward.

The Two-Wheeled Self-Balancing Mobile Robot Based on MEMS

2012 ◽  
Vol 468-471 ◽  
pp. 2678-2681
Author(s):  
Hu Sun ◽  
Yun Guo Li ◽  
Xin Biao Li ◽  
Pei Cheng
Keyword(s):  
Kalman Filter ◽  
Inertial Measurement Unit ◽  
Optimal Estimation ◽  
Measurement Unit ◽  
Robot System ◽  
Multiple Sensors ◽  
Attitude Angle ◽  
Wheeled Robot ◽  
Kalman Filter Algorithm ◽  
Sensors Fusion

In this paper, the MIMU( MEMS Inertial Measurement Unit) was used to detect the attitude angle of the two-wheeled robot. By Kalman filter, the optimal estimation of attitude angle was gotten, and which was applied to the balance controlling. In this system, FPGA is chosen as processor, and the embedded kernel was built up based on SOPC. Furthermore, the software of multiple sensors fusion has been developed. The experiment indicates that the design of this robot system is reasonable, and the Kalman filter algorithm can improve the precision of controlling effectively.

scholarly journals Robust Inertial Measurement Unit-Based Attitude Determination Kalman Filter for Kinematically Constrained Links

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 768 ◽  
Author(s):  
Jung Lee ◽  
Mi Choi
Keyword(s):  
Kalman Filter ◽  
Inertial Measurement Unit ◽  
Attitude Determination ◽  
Measurement Unit ◽  
Kinematic Constraint ◽  
Inertial Measurement ◽  
Acceleration Level ◽  
Average Accuracy ◽  
Moving Body ◽  
External Acceleration

The external acceleration of a fast-moving body induces uncertainty in attitude determination based on inertial measurement unit (IMU) signals and thus, frequently degrades the determination accuracy. Although previous works adopt acceleration-compensating mechanisms to deal with this problem, they cannot completely eliminate the uncertainty as they are, inherently, approaches to an underdetermined problem. This paper presents a novel constraint-augmented Kalman filter (KF) that eliminates the acceleration-induced uncertainty for a robust IMU-based attitude determination when IMU is attached to a constrained link. Particularly, this research deals with an acceleration-level kinematic constraint derived on the basis of a ball joint. Experimental results demonstrate the superiority of the proposed constrained KF over the conventional unconstrained KF: The average accuracy improved by 1.88° with a maximum of 4.18°. More importantly, whereas the accuracy of conventional KF is dependent to some extent on test acceleration conditions, that of the proposed KF is independent of these conditions. Due to the robustness of the proposed KF, it may be applied when accurate attitude estimation is needed regardless of dynamic conditions.

scholarly journals Real-Time Estimation of Gas Turbine Engine Damage Using a Control Based Kalman Filter Algorithm

1991 ◽  
Author(s):  
L. J. Kerr ◽  
T. S. Nemec ◽  
G. W. Gallops
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Measurement Errors ◽  
Time Estimation ◽  
Gas Turbine Engine ◽  
Control Measurement ◽  
Turbine Engine ◽  
Kalman Filter Algorithm

A second generation Kalman filter algorithm is described that has sufficient accuracy and response for real-time detection and estimation of gas turbine engine gas path damage caused by normal wear, mechanical failures and ingestion of foreign objects. The algorithm was developed for in-flight operation of aircraft engines but also has application for marine and industrial gas turbines. The control measurement and microcomputer requirements are described. The performance and sensitivity to engine transients and measurement errors is evaluated. The algorithm is demonstrated with actual engine data of ice and bird ingestion tests.

Enhanced Kalman filter algorithm using fuzzy inference for improving position estimation in indoor navigation

2021 ◽  
Vol 40 (5) ◽  
pp. 8991-9005
Author(s):  
Faisal Jamil ◽  
DoHyeun Kim
Keyword(s):  
Kalman Filter ◽  
Indoor Environment ◽  
Fuzzy Inference ◽  
Position Estimation ◽  
Indoor Navigation ◽  
Measurement Unit ◽  
Fusion Algorithm ◽  
Positioning Systems ◽  
Sensory Data ◽  
Kalman Filter Algorithm

In recent few years, the widespread applications of indoor navigation have compelled the research community to propose novel solutions for detecting objects position in the Indoor environment. Various approaches have been proposed and implemented concerning the indoor positioning systems. This study propose an fuzzy inference based Kalman filter to improve the position estimation in indoor navigation. The presented system is based on FIS based Kalman filter aiming at predicting the actual sensor readings from the available noisy sensor measurements. The proposed approach has two main components, i.e., multi sensor fusion algorithm for positioning estimation and FIS based Kalman filter algorithm. The position estimation module is used to determine the object location in an indoor environment in an accurate way. Similarly, the FIS based Kalman filter is used to control and tune the Kalman filter by considering the previous output as a feedback. The Kalman filter predicts the actual sensor readings from the available noisy readings. To evaluate the proposed approach, the next-generation inertial measurement unit is used to acquire a three-axis gyroscope and accelerometer sensory data. Lastly, the proposed approach’s performance has been investigated considering the MAD, RMSE, and MSE metrics. The obtained results illustrate that the FIS based Kalman filter improve the prediction accuracy against the traditional Kalman filter approach.

Real-Time Estimation of Gas Turbine Engine Damage Using a Control-Based Kalman Filter Algorithm

1992 ◽  
Vol 114 (2) ◽  
pp. 187-195 ◽  
Author(s):  
L. J. Kerr ◽  
T. S. Nemec ◽  
G. W. Gallops
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Measurement Errors ◽  
Time Estimation ◽  
Gas Turbine Engine ◽  
Control Measurement ◽  
Turbine Engine ◽  
Kalman Filter Algorithm

A second-generation Kalman filter algorithm is described that has sufficient accuracy and response for real-time detection and estimation of gas turbine engine gas path damage caused by normal wear, mechanical failures, and ingestion of foreign objects. The algorithm was developed for in-flight operation of aircraft engines but also has application for marine and industrial gas turbines. The control measurement and microcomputer requirements are described. The performance and sensitivity to engine transients and measurement errors is evaluated. The algorithm is demonstrated with actual engine data of ice and bird ingestion tests.

IP Core Based on the Kalman Filter Algorithm in the FPGA Implementation

2013 ◽  
Vol 694-697 ◽  
pp. 1093-1097
Author(s):  
Zhao Xue ◽  
Liu Quan ◽  
Xiao Fei Wang
Keyword(s):  
Kalman Filter ◽  
High Speed ◽  
Implementation Process ◽  
Floating Point ◽  
Programming Algorithm ◽  
Double Precision ◽  
Ip Core ◽  
Matrix Operations ◽  
Kalman Filter Algorithm ◽  
Point Data

This article discusses one-dimensional Kalman filter algorithm using FPGA hardware IP core implementation process. First of all, to program the FPGA matrix operations, implementation of double precision floating point. Then the Kalman filter algorithm programmed in MATLAB, to verify the correctness of the algorithm thinking. Finally the MATLAB language programming algorithm is converted into VHDL language. And call 64 a double precision floating point data algorithm realizes the design of 1-D Kalman filtration algorithm IP core, which make the Kalman filter meet the high precision as well as high speed to complete complex algorithm.

scholarly journals Observer design for a single mast stacker crane

2021 ◽  
Vol 69 (9) ◽  
pp. 806-816
Author(s):  
Lukas Ecker ◽  
Tobias Malzer ◽  
Arne Wahrburg ◽  
Markus Schöberl
Keyword(s):  
Kalman Filter ◽  
Ritz Method ◽  
Observer Design ◽  
Measurement Unit ◽  
Dimensional System ◽  
Observer Error ◽  
Infinite Dimensional ◽  
Measurement Results ◽  
Automated Warehouses ◽  
Error System

Abstract This contribution is concerned with the design of observers for a single mast stacker crane, which is used, e. g., for storage and removal of loads in automated warehouses. As the mast of such stacker cranes is typically a lightweight construction, the system under consideration is described by ordinary as well as partial differential equations, i. e., the system exhibits a mixed finite-/infinite-dimensional character. We will present two different observer designs, an Extended Kalman Filter based on a finite-dimensional system approximation, using the Rayleigh-Ritz method and an approach exploiting the port-Hamiltonian system representation for the mixed finite-/infinite-dimensional scenario where in particular the observer-error system should be formulated in the port-Hamiltonian framework. The mixed-dimensional observer and the Kalman Filter are employed to estimate the deflection of the beam based on signals acquired by an inertial measurement unit at the beam tip. Such an approach considerably simplifies mechatronic integration as it renders strain-gauges at the base of the mast obsolete. Finally, measurement results demonstrate the capability of these approaches for monitoring and vibration-rejection purposes.

Development of reference materials with the composition of propyl and isopropyl alcohol solutions

2020 ◽  
pp. 66-72
Author(s):  
Irina A. Piterskikh ◽  
Svetlana V. Vikhrova ◽  
Nina G. Kovaleva ◽  
Tatyana O. Barynskaya
Keyword(s):  
Reference Materials ◽  
Measurement Errors ◽  
Isopropyl Alcohol ◽  
Certified Reference Materials ◽  
Toxic Substances ◽  
Biological Objects ◽  
Margin Of Error ◽  
Measurement Results ◽  
And Control ◽  
Characteristic Mass

Certified reference materials (CRM) composed of propyl (11383-2019) and isopropyl (11384-2019) alcohols solutions were created for validation of measurement procedures and control of measurement errors of measurement results of mass concentrations of toxic substances (alcohol) in biological objects (urine, blood) and water. Two ways of establishing the value of the certified characteristic – mass consentration of propanol-1 or propanol-2 have been studied. The results obtained by the preparation procedure and comparison with the standard are the same within the margin of error.

scholarly journals Acceleration Feature Extraction of Human Body Based on Wearable Devices

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 924
Author(s):  
Zhenzhen Huang ◽  
Qiang Niu ◽  
Ilsun You ◽  
Giovanni Pau
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Kalman Filter ◽  
Human Body ◽  
Wearable Devices ◽  
Human Motion ◽  
Data Set ◽  
Feature Extraction Method ◽  
Motion Data ◽  
Kalman Filter Algorithm

Wearable devices used for human body monitoring has broad applications in smart home, sports, security and other fields. Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human body acceleration feature extraction method based on wearable devices is studied. Firstly, Butterworth filter is used to filter the data. Then, in order to ensure the extracted feature value more accurately, it is necessary to remove the abnormal data in the source. This paper combines Kalman filter algorithm with a genetic algorithm and use the genetic algorithm to code the parameters of the Kalman filter algorithm. We use Standard Deviation (SD), Interval of Peaks (IoP) and Difference between Adjacent Peaks and Troughs (DAPT) to analyze seven kinds of acceleration. At last, SisFall data set, which is a globally available data set for study and experiments, is used for experiments to verify the effectiveness of our method. Based on simulation results, we can conclude that our method can distinguish different activity clearly.

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