scholarly journals Designing a Mouse for Disabilities Using the MPU-6050 Sensor with the Kalman Filter Method as a Noise Reducer

2021 ◽  
Vol 11 (4) ◽  
pp. 188-194
Author(s):  
Putri Ayu Zartika ◽  
Mila Kusumawardani ◽  
Koesmarijanto Koesmarijanto
Keyword(s):  
Kalman Filter ◽  
Data Transmission ◽  
Physical Disabilities ◽  
Head Movements ◽  
Average Error ◽  
Filter Method ◽  
Noise Interference ◽  
Kalman Filter Method ◽  
People With Physical Disabilities ◽  
Error Percentage

Problems that are often faced by people with physical disabilities are those who have limited hands, one of which is when they will use the computer. His inability to grip and use the mouse is often a barrier in using the computer. The purpose of the design of the tool is to provide facilities for people with disabilities to be able to use a mouse that will be moved based on head movements without noise interference caused by the MPU-6050 sensor. The results of the tests carried out show that designing a mouse with the MPU-6050 sensor has been successfully carried out, the MPU-6050 sensor by implementing a kalman filter as a noise reducer on the X axis has an accuracy value with an average error percentage of 0.09% and at Y angle is 0.12%. Data transmission from the mouse to the computer is done wirelessly using bluetooth HC-05 can receive data well as far as 12.5 meters with an error percentage of 0%. The button on the mouse that functions to perform the left click function when the button is bitten 1x, right click when the button is bitten 2x and click and hold to do a left click 2x or double click can run according to the command, has a 100% success rate.

scholarly journals Multiscale KF Algorithm for Strong Fractional Noise Interference Suppression in Discrete-Time UWB Systems

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Liyun Su ◽  
Yuli Zhang ◽  
Yanju Ma ◽  
Jiaojun Li ◽  
Fenglan Li
Keyword(s):  
Kalman Filter ◽  
Discrete Time ◽  
Interference Suppression ◽  
Sampling Rate ◽  
Noise Signal ◽  
Filter Method ◽  
Noise Interference ◽  
Fractional Noise ◽  
Sampled Signal ◽  
Kalman Filter Method

In order to suppress the interference of the strong fractional noise signal in discrete-time ultrawideband (UWB) systems, this paper presents a new UWB multi-scale Kalman filter (KF) algorithm for the interference suppression. This approach solves the problem of the narrowband interference (NBI) as nonstationary fractional signal in UWB communication, which does not need to estimate any channel parameter. In this paper, the received sampled signal is transformed through multiscale wavelet to obtain a state transition equation and an observation equation based on the stationarity theory of wavelet coefficients in time domain. Then through the Kalman filter method, fractional signal of arbitrary scale is easily figured out. Finally, fractional noise interference is subtracted from the received signal. Performance analysis and computer simulations reveal that this algorithm is effective to reduce the strong fractional noise when the sampling rate is low.

scholarly journals Performance evaluation of GPS / BDS combined single-point positioning based on RTKLIB

2020 ◽  
Vol 165 ◽  
pp. 03009
Author(s):  
Li Yan-yi ◽  
Huang Jin ◽  
Tang Ming-xiu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Single Point ◽  
Least Square Method ◽  
Least Square ◽  
Filter Method ◽  
Single System ◽  
Positioning Error ◽  
Dual Mode ◽  
Kalman Filter Method

In order to evaluate the performance of GPS / BDS, RTKLIB, an open-source software of GNSS, is used in this paper. In this paper, the least square method, the weighted least square method and the extended Kalman filter method are respectively applied to BDS / GPS single system for data solution. Then, the BDS system and GPS system are used for fusion positioning and the positioning results of the two systems are compared with that of the single system. Through the comparison of experiments, on the premise of using the extended Kalman filter method for positioning, when the GPS signal is not good, BDS data is introduced for dual-mode positioning, the positioning error in e direction is reduced by 36.97%, the positioning error in U direction is reduced by 22.95%, and the spatial positioning error is reduced by 16.01%, which further reflects the advantages of dual-mode positioning in improving a system robustness and reducing the error.

Fault diagnose of the rolling bearings vibration signals based on Kalman filter method

2021 ◽  
Author(s):  
Qingpeng Han ◽  
Xinhang Shen ◽  
Bin Wu ◽  
Rui Zhu ◽  
Daolei Wang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Filter Method ◽  
Fault Diagnose ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Kalman Filter Method

An Implicit Algorithm of Solving Nonlinear Filtering Problems

2014 ◽  
Vol 16 (2) ◽  
pp. 382-402
Author(s):  
Feng Bao ◽  
Yanzhao Cao ◽  
Xiaoying Han
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Nonlinear Filtering ◽  
State Equation ◽  
Nonlinear Filter ◽  
Filter Method ◽  
Simulation Methods ◽  
Current State ◽  
Numerical Simulation Methods ◽  
Kalman Filter Method

AbstractNonlinear filter problems arise in many applications such as communications and signal processing. Commonly used numerical simulation methods include Kalman filter method, particle filter method, etc. In this paper a novel numerical algorithm is constructed based on samples of the current state obtained by solving the state equation implicitly. Numerical experiments demonstrate that our algorithm is more accurate than the Kalman filter and more stable than the particle filter.

An Improved Adaptive Kalman Filter for Gravity Anomaly Distortion Correction

2011 ◽  
Vol 121-126 ◽  
pp. 1421-1425
Author(s):  
Li Ye Zhao ◽  
Hong Sheng Li
Keyword(s):  
Kalman Filter ◽  
Gravity Anomaly ◽  
Distortion Correction ◽  
The Self ◽  
Filter Method ◽  
Current Time ◽  
Gain Matrix ◽  
Gravity Signal ◽  
Kalman Filter Method ◽  
Correlation Sequences

Combined with the system state equation and the measurement equation, a new method of cascade Kalman filter is proposed and applied to the correction of gravity anomaly distortion. In the signal processing procedure, according to the self-correlation sequences of the measurement gravity signal, the relation of the gain matrix K and the self-correlation sequences could be obtain, and the gravity signal at current time can be calculated by the gain matrix K. Emulations and experiments indicate that both the cascade Kalman filter method and the single inverse Kalman filter method are effective in alleviating the distortion of the gravity anomaly signal, but the performance of the cascade Kalman filter method is better than that of single inverse Kalman filter method.

The Dangerous Rock Mass Deformation Analysis with Application of Kalman Filter Method Based on Material Properties and AR Model

2013 ◽  
Vol 738 ◽  
pp. 109-112
Author(s):  
Fu Min Lu ◽  
Ting Yao Jiang
Keyword(s):  
Kalman Filter ◽  
Rock Mass ◽  
Ar Model ◽  
Deformation Analysis ◽  
Filter Method ◽  
The Status ◽  
Rock Mass Deformation ◽  
Kalman Filter Method ◽  
Mass Deformation ◽  
Dangerous Rock Mass

Considering the material property of the rock to the dangerous rock mass,the paper Looks the model parameter of AR( 1) model as the status vector, and uses Kalman filter method to analysis the deformation of the dangerous rock mass. The result shows that the method can improve the accuracy of fitting and forecasting of the model.

Optimization of Stock Portfolios Using Goal Programming Based on the Kalman-Filter Method

2021 ◽  
Vol 7 (1) ◽  
pp. 20-30
Author(s):  
Fauziyah ◽  
Evita Purnaningrum
Keyword(s):  
Kalman Filter ◽  
Goal Programming ◽  
Stock Prices ◽  
Stock Price ◽  
Filter Method ◽  
Stock Portfolios ◽  
Stock Investment ◽  
Kalman Filter Method ◽  
Return Difference ◽  
Stock Portfolio

Long-term stock investment development is carried out by means of portfolio optimization. Selection of stocks for portfolios is not only based on high-value stock prices but also takes into account their fluctuations. Estimation of future stock price fluctuations has an indirect impact on future portfolio formation. This research has implemented the Kalman filter method to obtain the best estimation results from various stock prices with a high degree of accuracy. The results are then used to form a stock portfolio on the basis of Goal Programming. This study has compared the optimization results with the real value of stock prices. The results obtained, Kalman filter-based Goal Programming is more effective for predicting future portfolios compared to the Goal Programming method with a return difference of Rp. 178,039,848. This suggests that optimization with the Kalman Filter-based Objective Programming can be used as a tool to determine future stock portfolios.

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