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.

scholarly journals The Attitude Signal Processing of Four Rotor Aircraft based on a Kalman Filter

2015 ◽  
Vol 11 (8) ◽  
pp. 8
Author(s):  
Zhang Tao ◽  
PEI Jin-xin ◽  
SONG Yu-guang ◽  
LUO Peng
Keyword(s):  
Signal Processing ◽  
Kalman Filter ◽  
Aircraft Control ◽  
Filter Model ◽  
Complementary Filter ◽  
Attitude Angle ◽  
System Optimal ◽  
Kalman Filter Algorithm ◽  
The Stability ◽  
And Control

This paper puts forward Kalman filter algorithm to improve the accuracy and the stability of four rotor aircraft control. The algorithm principle is that making use of covariance to estimate the system optimal output.The flight and control theory of four rotor aircraft is first analysed, furthermore, it is found that the attitude angle has the biggest influence to its control. Then, the Kalman filter model is established for processing attitude angle information from gyro and accelerometer sensors. Finally, the algorithm is compared with complementary filter. The flight conditions is simulated in different flight environment, it is shown that Kalman filter algorithm can improve the precision and real time more greatly in attitude angle acquisition.

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.

scholarly journals Multifactor Systematic Risk Analysis Based on Time-varying Signal Processing Models

2021 ◽  
Author(s):  
Luan Vo
Keyword(s):  
Signal Processing ◽  
Kalman Filter ◽  
Random Walk ◽  
Systematic Risk ◽  
Time Varying ◽  
Random Coefficient Models ◽  
Piecewise Constant ◽  
Kalman Filter Algorithm ◽  
Empirical Tests ◽  
Piecewise Constant Model

This thesis applies the time-varying signal processing models to track the multifactor systematic risk in the Fama-French model. The mean reverting, random walk and random coefficient models are used to analyze the time-varying multifactor beta based on the multivariate Kalman filter algorithm. The sudden changes in the mutifactor beta ar e captured by the piecewise constant model. Our case studies explain the impacts of economic events on the sudden changes in betas for both individual stocks and industrial portfolios. We propose a new time-varying beta model based on a piecewise mean reverting process to express the effects of different types of events on the multifactor beta.The tracking of the piecewise mean reverting beta, using the modified multivariate Kalman filter with the maximum log likelihood estimator, outperforms the traditional piecewise constant and random walk models as demonstrated in our simulations. The empirical tests indicate that the new model effectively captures the different changes in beta depending on the type of event.

A Rugged and Reliable System for Measuring Engine Crankshaft Speed Fluctuations

1998 ◽  
Vol 120 (4) ◽  
pp. 533-536
Author(s):  
Hsi-Yung Feng
Keyword(s):  
Signal Processing ◽  
Gear Tooth ◽  
Current System ◽  
Processing Algorithm ◽  
Computationally Efficient ◽  
Signal Processing Algorithm ◽  
Noise Component ◽  
High Measurement ◽  
Speed Fluctuations ◽  
Engine Crankshaft

A measurement system and the associated signal processing algorithm for accurately determining engine crankshaft speed fluctuations are presented in this work. The main features of the system include ruggedness, simple sensor installation, and high accuracy and resolution. The ruggedness and simplicity of the system are characterized by the use of highly reliable Hall-Effect Transducers (HETs) mounted on the engine flywheel bell housing to sense the passing of flywheel gear teeth. A relatively significant noise component is introduced to the HET measurements by the nonuniform flywheel gear tooth spacings. The high measurement accuracy is achieved by a very computationally efficient signal processing algorithm based on two HET inputs. The advantages of the current system over the single HET systems commonly used in practice are clearly demonstrated through experiments on a Detroit Diesel Corporation 6V-92TA engine.

scholarly journals Multifactor Systematic Risk Analysis Based on Time-varying Signal Processing Models

2021 ◽  
Author(s):  
Luan Vo
Keyword(s):  
Signal Processing ◽  
Kalman Filter ◽  
Random Walk ◽  
Systematic Risk ◽  
Time Varying ◽  
Random Coefficient Models ◽  
Piecewise Constant ◽  
Kalman Filter Algorithm ◽  
Empirical Tests ◽  
Piecewise Constant Model

This thesis applies the time-varying signal processing models to track the multifactor systematic risk in the Fama-French model. The mean reverting, random walk and random coefficient models are used to analyze the time-varying multifactor beta based on the multivariate Kalman filter algorithm. The sudden changes in the mutifactor beta ar e captured by the piecewise constant model. Our case studies explain the impacts of economic events on the sudden changes in betas for both individual stocks and industrial portfolios. We propose a new time-varying beta model based on a piecewise mean reverting process to express the effects of different types of events on the multifactor beta.The tracking of the piecewise mean reverting beta, using the modified multivariate Kalman filter with the maximum log likelihood estimator, outperforms the traditional piecewise constant and random walk models as demonstrated in our simulations. The empirical tests indicate that the new model effectively captures the different changes in beta depending on the type of event.

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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