scholarly journals New Method of Spatial Extrapolation of Meteorological Fields on the Mesoscale Level Using a Kalman Filter Algorithm for a Four-Dimensional Dynamic–Stochastic Model

2007 ◽  
Vol 24 (2) ◽  
pp. 182-193 ◽  
Author(s):  
V. S. Komarov ◽  
A. V. Lavrinenko ◽  
A. V. Kreminskii ◽  
N. Ya Lomakina ◽  
Yu B. Popov ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Stochastic Model ◽  
Statistical Estimation ◽  
Velocity Fields ◽  
New Method ◽  
Atmospheric Parameters ◽  
Time Variations ◽  
Dynamic Stochastic ◽  
Kalman Filter Algorithm

Abstract A new method and an algorithm of spatial extrapolation of mesometeorological fields to a territory uncovered with observations are suggested. The algorithm uses a linear Kalman filter for a four-dimensional dynamic–stochastic model of space–time variations of the atmospheric parameters. The results of statistical estimation of the quality of the algorithm used for spatial extrapolation of mesoscale temperature and wind velocity fields are discussed.

Evaluating the Observability in the Combination Process of the Height Measurement Signals

2020 ◽  
Vol 10 (4) ◽  
pp. 24-36
Author(s):  
Nguyen Quang Vinh
Keyword(s):  
Signal Processing ◽  
Kalman Filter ◽  
Height Measurement ◽  
Signal Processing Algorithm ◽  
Combination Process ◽  
High Measurement ◽  
Measurement Signal ◽  
Kalman Filter Algorithm ◽  
Channel Combination

In the modern navigation system, the height channel is always the most unstable channel. Combination processing the height measurement signals by using the Kalman filter algorithm can improve the precision of the high measurement. However, in the process of performing the signal processing algorithm by using the Kalman filter, the transition time to obtain the set status is long. Moreover, within different flight conditions, the inertia height meter will be combined with the supporting height meter to get the structure of the combination height meter in order to process the height measurement signals more precisely. In this article, the authors proposed using the criterion for evaluating the observable level to improve the quality of height measurement signal processing. The research results were simulated on three combined high measurements, in which the inertia height meter (IHM) (the basic meter) was combined with one or two supporting height meters (the radio height meter [RHM] and the barometer [AHM]) to show the correctness of the proposed algorithm.

A New Method of Fault Phase Identification for High Voltage Transmission Lines Based on the Fault Component of Phase Voltage

2013 ◽  
Vol 732-733 ◽  
pp. 1056-1064
Author(s):  
Yang Chen ◽  
Yan Hu ◽  
Neng Ling Tai
Keyword(s):  
Kalman Filter ◽  
High Voltage ◽  
Transmission Lines ◽  
High Sensitivity ◽  
New Method ◽  
Phase Identification ◽  
Phase Voltage ◽  
Two Phase ◽  
Kalman Filter Algorithm ◽  
High Voltage Transmission

Since the existing fault phase identification methods can not identify all fault types quickly and accurately for high voltage transmission lines, this article proposed a new method of fault phase identification based on the fault component of phase voltage difference and the kalman filter algorithm. The method defined the fault components ratio of one phase voltage to the difference of the other two phase voltages as a fault phase identification factor. By analyzing the characteristics of fault phase identification factors in each fault type, the fault phase can be identified. Simulation results show that using the kalman filter algorithm to extract fundamental component is faster and more accurate. Meanwhile, the method can identify fault phases within half a cycle and is scarcely influenced by fault resistances, fault locations and fault initial phase angles. It also has a high sensitivity when the fault is on the side of strong source.

UGR CALCULATION BASED ON THE LUMINANCE SPATIAL-ANGULAR DISTRIBUTION

2020 ◽  
Vol 2020 (4) ◽  
pp. 25-32
Author(s):  
Viktor Zheltov ◽  
Viktor Chembaev
Keyword(s):  
Monte Carlo ◽  
Angular Distribution ◽  
Monte Carlo Method ◽  
Visual Task ◽  
New Method ◽  
Lighting System ◽  
Preliminary Assessment ◽  
System A ◽  
The Monte Carlo Method

The article has considered the calculation of the unified glare rating (UGR) based on the luminance spatial-angular distribution (LSAD). The method of local estimations of the Monte Carlo method is proposed as a method for modeling LSAD. On the basis of LSAD, it becomes possible to evaluate the quality of lighting by many criteria, including the generally accepted UGR. UGR allows preliminary assessment of the level of comfort for performing a visual task in a lighting system. A new method of "pixel-by-pixel" calculation of UGR based on LSAD is proposed.

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.

Flight performance monitoring with optimal filtering applications

2019 ◽  
Vol 124 (1272) ◽  
pp. 170-188
Author(s):  
V. A. Deo ◽  
F. Silvestre ◽  
M. Morales
Keyword(s):  
Kalman Filter ◽  
Performance Monitoring ◽  
The Body ◽  
Flight Performance ◽  
Performance Parameters ◽  
Sideslip Angle ◽  
Fuel Flow ◽  
Aircraft Performance ◽  
Lift And Drag

ABSTRACTThis work presents an alternative methodology for monitoring flight performance during airline operations using the available inboard instrumentation system. This method tries to reduce the disadvantages of the traditional specific range monitoring technique where instrumentation noise and cruise stabilisation conditions affect the quality of the performance monitoring results. The proposed method consists of using an unscented Kalman filter for aircraft performance identification using Newton’s flight dynamic equations in the body X, Y and Z axis. The use of the filtering technique reduces the effect of instrumentation and process noise, enhancing the reliability of the performance results. Besides the better quality of the monitoring process, using the proposed technique, additional results that are not possible to predict with the specific range method are identified during the filtering process. An example of these possible filtered results that show the advantages of this proposed methodology are the aircraft fuel flow offsets, as predicted in the specific range method, but also other important aircraft performance parameters as the aircraft lift and drag coefficients (CL and CD), sideslip angle (β) and wind speeds, giving the operator a deeper understanding of its aircraft operational status and the possibility to link the operational monitoring results to aircraft maintenance scheduling. This work brings a cruise stabilisation example where the selected performance monitoring parameters such as fuel flow factors, lift and drag bias, winds and sideslip angle are identified using only the inboard instrumentation such as the GPS/inertial sensors, a calibrated anemometric system and the angle-of-attack vanes relating each flight condition to a specific aircraft performance monitoring result. The results show that the proposed method captures the performance parameters by the use of the Kalman filter without the need of a strict stabilisation phase as it is recommended in the traditional specific range method, giving operators better flexibility when analysing and monitoring fleet performance.

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