Real time tracking of TV satellites on moving vehicles using Kalman filter

Kocadag, Fatih; Demirkol, Aşkın

doi:10.1109/spin.2015.7095286

Cited by 5 publications

(5 citation statements)

References 2 publications

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“…On the contrary, if the noise expectation is not 0, then the estimated value is still deviated from the actual value. Therefore, it is very suitable for solving real-time problems and embedded systems [16].…”

Section: Kalman Filtering Algorithmmentioning

confidence: 99%

Thermal Load Model of a Proportional Solenoid Valve Based on Random Load Conditions

Liu,

Wang,

et al. 2023

Sensors

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Drastic changes in the random load of an electromechanical system bring about a reliability problem for the proportional solenoid valve based on a thermal effect. In order to accurately and effectively express the thermal load of a proportional solenoid valve under random load conditions and to meet the requirements of online acquisition, adaptive anomaly detection, and the missing substitution of thermal load data, a thermal load prediction model based on the Kalman filter algorithm is proposed. Taking the compound operation process of an excavator as the object and based on the field testing of an excavator and the independent testing experiment of a proportional solenoid valve in a non-installed state, a method of obtaining historical samples of the proportional solenoid valve’s power and thermal load is given. The k-means clustering algorithm is used to cluster the historical samples of the power and thermal load corresponding to the working posture of a multi-tool excavator. The Grubbs criterion is used to eliminate the outliers in the clustering samples, and unbiased estimation is performed on the clustering samples to obtain the prediction model. The results show that the cross-validation of the sample data under the specific sample characteristics of the thermal load model was carried out. Compared with other methods, the prediction accuracy of the thermal load model based on the Kalman filter is higher, the adaptability is strong, and the maximum prediction deviation percentage is stable within 5%. This study has value as a reference for random cycle thermal load analyses of low-frequency electromechanical products.

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Section: Kalman Filtering Algorithmmentioning

confidence: 99%

Thermal Load Model of a Proportional Solenoid Valve Based on Random Load Conditions

Liu,

Wang,

et al. 2023

Sensors

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“…The input section consists of several sensors such as GPS, digital compass, and accelerometer, which detect and track the orbital position of the satellite which provides information on the angle of the satellite position to the microcontroller. This microcontroller will translate the angular position of the sensor through the help of motor windows 1 and 2 to move the parabolic antenna to a certain signal strength point according to the position of the detected satellite orbit [7]- [12]. Smartphones receive notification information from the microcontroller which can set and monitor realtime with special specifications [13]- [16].…”

Section: A Block Diagrammentioning

confidence: 99%

Automatic Detection System Satellite Orbit Position with a Method of Control Azimuth and Elevation Angles Parabolic Antenna

Herdiana

Gunawan

2021

jrc

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The automatic system satellite signal strength tracking model through the positioning of the receiving antenna is one way to reduce the problem of attenuation and signal strength instability which is quite influential in tracking the satellite orbit position. The method of applying the antenna movement technique by adjusting the azimuth and elevation angles is possible to detect the position of the satellite orbit trajectory accurately. Therefore, the expected goal is to know the existence of the satellite's orbital trajectory and ensure that the signal strength it transmits will always be stable in all conditions. The empirical quantitative method is used to support the achievement of these objectives where the technique is carried out through field observations and device design involving calculations and applied mathematical equations based on information on satellite position data as a tracking object as well as a source of signal strength. Based on the test results, it shows that all the positions of the tracked satellite orbital trajectories produce a minimum and maximum azimuth-elevation angle of 0.52% -4.02% and 0.25% -3.99% when the system detects NSS6. Furthermore, when the system detects Palapa D, the minimum values for the percentage of maximum deviation error are 0.6% and 4.67% with a tolerance of the alignment angle to the satellite of < 20. Overall, the designed system is able to track the position of the satellite's orbit path based on the level of signal strength stability on the parabolic receiver antenna which is designed with small percentage of angle error.

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“…Noisy measurements were produced by sensor and these data sets are sent to the discrete Kalman filter part in order to estimate the attitude and it's rate. A real time tracking has been done to calculate azimuth and elevation angle of the satellite by Kocadag and Demirkol (2015) for uninterrupted broadcasting. Pham et al, (2015) proposed a gain-scheduled Extended kalman filter (EKF) to reduce the computational requirement in the nano-satellite attitude determination process.…”

Section: Extended Kalman Filtermentioning

confidence: 99%

“…( 6) can be recasted as (24) The succeeding empirical speculations are made also here and ∂N are known for a set γ. Designate a switching function as (25) (26) (27) where e = ω − ω d is defined as the trajectory tracking error vector, ω d is the desired trajectory to be tracked by ω and k 1 being the positive diagonal feedback matrix. Say if p coincides to a small value or zero, then e will converge to a small value or zero.…”

Section: Advance Adaptive Robust Control Lawmentioning

confidence: 99%

Extended Kalman Filter Based Estimations for Satellite Attitude Control System

Dube

Sharma

Patel

2021

IJEAT

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This paper mainly focuses on the maneuver of the satellite in orbit. A non-linear multi-inputs multi-outputs model has been derived from Newton-Euler equations of motion. The dynamics is presented with control methodologies allowing the Extended Kalman Filter (EKF) to iteratively provide improved data sets with zero errors. As the system is distracted from the atmospheric swings which are random hence the problem of stochastic disturbance is furnished. A set of differential equations of two dimensional Ito stochastic type is used for modeling the said disturbances (before t = 4s is recorded). The attitude parameters are recorded in RT-LAB setup with the Extended Kalman Filter (EKF) providing adequately superior estimation outcome which thereby makes the filter more appealing. With the presence of Gaussian noise in both dimension and system, Extended Kalman Filter gives the correct estimates. It’s collaboration with hardware setup RT-LAB is commendable. Hence, an Extended Kalman Filter which deals with such nonlinear models proves to be a higher choice for achieving best online results. A comparison reflecting the tracking and stable control of the satellite for the designed advanced adaptive robust controller (AARC) for two situations is plotted. The priority of making the system stable in the presence of stochastic disturbance is also visited. Also, the use of three different values of the confounding variables revealed that the control weighting line is completely diminished thereby boosting the tracking when the satellite is in orbit. Moreover, the previous research involves methods to improve satellite communication on ground station, this paper deals with exact positioning of concerned satellite attitude parameters and its validation tested experimentally on OPAL-RT hardware. To sum up, the development of advanced adaptive robust controllers have encouraged the stability and accuracy of systems considering the varying atmospheric conditions. The simulation results predict perfect tracking of output with respect to the desired set-point in the presence of stochastic disturbance for the proposed controller.

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Real time tracking of TV satellites on moving vehicles using Kalman filter

Cited by 5 publications

References 2 publications

Thermal Load Model of a Proportional Solenoid Valve Based on Random Load Conditions

Thermal Load Model of a Proportional Solenoid Valve Based on Random Load Conditions

Automatic Detection System Satellite Orbit Position with a Method of Control Azimuth and Elevation Angles Parabolic Antenna

Extended Kalman Filter Based Estimations for Satellite Attitude Control System

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