Comparison of the Kalman filter with classical digital filter

AIChE Journal ◽  
1974 ◽  
Vol 20 (3) ◽  
pp. 598-600 ◽  
Author(s):  
Robert G. Kneile ◽  
Richard H. Luecke
Keyword(s):  
Kalman Filter ◽  
Digital Filter

scholarly journals PREDIKSI ARAH DATANG BOLA MENGGUNAKAN KALMAN FILTER PADA ROBOT KIPER SEPAKBOLA

Transmisi ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 56-61
Author(s):  
Daniyah Daniyah ◽  
Bustanul Arifin ◽  
Imam Much Ibnu Subroto
Keyword(s):  
Kalman Filter ◽  
Digital Filter

Robot kiper merupakan robot yang bertugas menjaga gawang dari masuknya bola oleh robot tim lawan. Permasalahan yang dihadapi dalam merancang robot kiper adalah bagaimana meningkatkan respon robot kiper terhadap bola sehingga kemungkinan terjadinya goal oleh robot lawan lebih sedikit. Robot kiper ini dirancang dengan menerapkan Kalman Filter. Kalman Filter merupakan suatu digital filter yang menggunakan algoritma dalam proses sinyal. Fungsi Kalman Filter sendiri adalah sebagai estimator stokastik untuk memprediksi arah bola terhadap robot kiper sepak bola beroda. Proses prediksi dapat dilakukan dengan mendeteksi bola terlebih dahulu sebagai acuan. Untuk pendeteksian bola sendiri menggunakan metode HSV yaitu Hue, Saturation, Value kemudian akan diolah menggunakan Kalman Filter sehingga mendapatkan nilai-nilai yang diperlukan dalam memprediksi arah datang bola. Penelitian ini menggunakan robot yang berdimensi 52 × 52 × 80 cm sesuai dengan aturan pada Kontes Robot Sepak Bola Indonesia Beroda dengan pemrograman Python dan menggunakan OpenCV untuk pengolahan citra-nya juga Filterpy untuk menerapkan fungsi Kalman Filter. Hasil dari penelitian ini adalah robot kiper yang menggunakan Kalman Filter dapat mengenali bola dari sudut pengujian yang sudah dilakukan dan dapat mengenali prediksi dari arah bola yang datang dari Kalman Filter yang digunakan pada robot kiper.

THE EXTENDED KALMAN FILTER AUGMENTED BY AN ADAPTIVE DIGITAL FILTER FOR DATA FUSION OF A MOBILE ROBOT CONTROL SYSTEM

2020 ◽  
Vol 2 ◽  
pp. 85-94
Author(s):  
P.A. BEZMEN
Keyword(s):  
Kalman Filter ◽  
Control System ◽  
Data Fusion ◽  
Mobile Robot ◽  
Extended Kalman Filter ◽  
Digital Filter ◽  
Robot Control ◽  
Mobile Robot Control ◽  
Operational Error ◽  
Robot Control System

The paper proposes the combination of the extended Kalman filter and an adaptive digital filter to compensate an operational error of the extended Kalman filter during data fusion of a mobile robot control system. The paper describes the structure and operation of such combination, shows the buffer memory configuration of an adaptive digital filter.

scholarly journals Filtering a Signal in a Fuzzy Filter Based on the Area Ratio Method

2020 ◽  
Vol 24 (3) ◽  
pp. 183-195
Author(s):  
M. V. Bobyr ◽  
M. Yu. Luneva
Keyword(s):  
Kalman Filter ◽  
Digital Filter ◽  
Experimental Studies ◽  
Area Ratio ◽  
Center Of Gravity ◽  
Ratio Method ◽  
Fuzzy Filter ◽  
Signal Filtering ◽  
Gravity Method ◽  
Control Coefficients

Purpose of reseach. Digital signal filtering allows real-time noise reduction in electronic devices. Currently, there are many different digital filters, differing in speed, computing power, algorithms and restrictions on the conditions of use. One of these filters is the Kalman filter, but adjusting the gains of this filter is very complicated by the process of additional experiments and collection of statistical information. Therefore, in this paper, the authors consider a simplified algorithm for finding the control coefficients of a fuzzy digital filter with defuzzifier based on the area ratio method and investigate the influence of the area ratio method parameters on signal filtering, thereby achieving the goal of improving the accuracy of the fuzzy digital filter. Methods. For the algorithm for finding the control coefficients of the digital filter, a fuzzy logic apparatus was used. The control factors are determined using a defuzzifier based on the area ratio method. Results. In the course of experimental studies, the mean square error RMSE was calculated for a fuzzy digital filter using the area ratio method, the center of gravity method and the Kalman filter. Based on the results obtained, it was concluded that the fuzzy filter based on the area ratio RMSE method is 5.43 times less than for the Kalman filter and 2.77 times less than for the defuzzifier based on the center of gravity method. The results obtained prove the effectiveness of using a fuzzy digital filter with the area ratio method. Conclusion: This article considers an algorithm for the operation of a fuzzy digital filter, simulates a fuzzy digital filter and a Kalman filter in the Simulink system and calculates the RMSE values for a fuzzy digital filter with the area ratio method and the center of gravity method, as well as the Kalman filter.

scholarly journals Accuracy Improvement of RSSI-based Distance Localization using Unscented Kalman Filter (UKF) Algorithm for Wi-Fi Tracking Application

2020 ◽  
Vol 14 (16) ◽  
pp. 225
Author(s):  
Syifaul Fuada ◽  
Trio Adiono ◽  
Prasetiyo Prasetiyo
Keyword(s):  
Kalman Filter ◽  
Digital Filter ◽  
Unscented Kalman Filter ◽  
Path Loss ◽  
Performance Comparison ◽  
Accuracy Improvement ◽  
Loss Model ◽  
Localization System ◽  
Path Loss Model ◽  
Server System

<p class="0abstract">In this report, we perform the digital filter computation using Matlab for Wi-Fi tracking application. This work motivates to improve the accuracy of filter algorithm in the RSSI-based distance localization system. There are several aspects that we can improve, e.g., in the Filter part and Path-loss model. But, in this work, we focus on filter part; Unscented Kalman Filter (UKF) is implemented to replace linear Kalman Filter (KF), which is used in previous work. Based on the performance comparison, UKF has 90% hit ratio while linear KF has only 81.15 % hit ratio. We found that UKF can handle the noise in RSSI. Further work, the UKF algorithm is then embedded on the server system.</p>

scholarly journals Examination of Analysis and Forecast Errors of High-Resolution Assimilation, Bias Removal, and Digital Filter Initialization with an Ensemble Kalman Filter

2012 ◽  
Vol 140 (12) ◽  
pp. 3992-4004 ◽  
Author(s):  
Brian C. Ancell
Keyword(s):  
Kalman Filter ◽  
High Resolution ◽  
Digital Filter ◽  
Ensemble Kalman Filter ◽  
Weather Prediction ◽  
Surface Wind ◽  
Forecast Errors ◽  
Temperature Bias ◽  
High Resolution Analysis ◽  
Bias Removal

Abstract Mesoscale atmospheric data assimilation is becoming an integral part of numerical weather prediction. Modern computational resources now allow assimilation and subsequent forecasting experiments ranging from resolutions of tens of kilometers over regional domains to smaller grids that employ storm-scale assimilation. To assess the value of the high-resolution capabilities involved with assimilation and forecasting at different scales, analyses and forecasts must be carefully evaluated to understand 1) whether analysis benefits gained at finer scales persist into the forecast relative to downscaled runs begun from lower-resolution analyses, 2) how the positive analysis effects of bias removal evolve into the forecast, and 3) how digital filter initialization affects analyses and forecasts. This study applies a 36- and 4-km ensemble Kalman filter over 112 assimilation cycles to address these important issues, which could all be relevant to a variety of short-term, high-resolution, real-time forecasting applications. It is found that with regard to surface wind and temperature, analysis improvements gained at higher resolution persist throughout the 12-h forecast window relative to downscaled, high-resolution forecasts begun from analyses on the coarser grid. Aloft, however, no forecast improvements were found with the high-resolution analysis/forecast runs. Surface wind and temperature bias removal, while clearly improving surface analyses, degraded surface forecasts and showed little forecast influence aloft. Digital filter initialization degraded temperature analyses with or without bias removal, degraded wind analyses when bias removal was used, but improved wind analyses when bias removal was absent. No forecast improvements were found with digital filter initialization. The consequences of these results with regard to operational assimilation/forecasting systems on nested grids are discussed.

Sign in / Sign up

Export Citation Format

Share Document