Implementasi Logika Fuzzy pada Kekuatan Sinyal yang Diterima Antena Viasat X-Band

The data that the antenna receives during satellite data acquisition has a signal strength that is affected by the antenna's movement at an elevation and azimuth angle. Every change in the two angles causes the signal strength received by the antenna to change. Signal strength calculation is important to be able to ensure satellite data is received well. Fuzzy Mamdani's logic as a method that can be used to calculate uncertain variables will be implemented in the calculation of the signal strength received by the Viasat X-Band antenna when the acquisition process of Aqua satellite data takes place. The results of the calculation of fuzzy mamdani logic by testing 6 signal strength data obtained from the Aqua satellite track analysis owned by LAPAN are shown in the percentage of errors, among others: DOY 197 of 1.33%; DOY 213 by 2.89%; DOY 259 of 1.93%; DOY 304 of 1.18%; DOY 320 by 4.73%; and DOY 357 of 2.27% and the average error (overall) of the entire data tested was 2.39%. This shows that the mamdani fuzzy logic is suitable for use in calculating the signal strength received by the Viasat X-Band antenna.

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Emendation to "Determination of Antenna Direction Using TV Signal-Strength Data"

IEEE Transactions on Aerospace and Electronic Systems ◽

10.1109/taes.1984.310478 ◽

1984 ◽

Vol AES-20 (6) ◽

pp. 844-844

Keyword(s):

Signal Strength ◽

Strength Data

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X-band compact dual circularly polarized isoflux antenna for nanosatellite applications

International Journal of Microwave and Wireless Technologies ◽

10.1017/s1759078716001410 ◽

2017 ◽

Vol 9 (7) ◽

pp. 1509-1516 ◽

Cited By ~ 5

Author(s):

Eric Arnaud ◽

Cyrille Menudier ◽

Jamil Fouany ◽

Thierry Monediere ◽

Marc Thevenot

Keyword(s):

Circular Polarization ◽

Microstrip Line ◽

Ground Plane ◽

Axial Ratio ◽

Horn Antenna ◽

Azimuth Angle ◽

Original Solution ◽

Circularly Polarized ◽

X Band

This paper presents an original solution to design a compact dual circularly polarized isoflux antenna for nanosatellite applications. This kind of antenna has been previously designed in our laboratory, for a single circular polarization. This antenna is composed of a dual circularly polarized feed and a choke horn antenna. This feed is a cross-shaped slot in the ground plane, which provides coupling between a patch and a ring microstrip line with two ports. It is located at the center of a choke horn antenna. The simulated antenna presents an axial ratio <3 dB and a realized gain close to 0 dB over a 400 MHz bandwidth (8.0–8.4 GHz) at the limit of coverage, i.e. 65° whatever the azimuth angle (φ) and the port. A 20 dB matching for each port and 13 dB isolation characteristics between the two ports have been achieved on this bandwidth. It has been realized and successfully measured.

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Arctic Sea X-band Microwave Emission Modeling Based on Satellite Data: Considering the Measurement Angle

Russian Meteorology and Hydrology ◽

10.3103/s1068373921040063 ◽

2021 ◽

Vol 46 (4) ◽

pp. 256-262

Author(s):

E. V. Zabolotskikh ◽

B. Chapron

Keyword(s):

Satellite Data ◽

Microwave Emission ◽

X Band ◽

Arctic Sea ◽

Emission Modeling

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Mobile Robot Wall-Following Control Using Fuzzy Logic Controller with Improved Differential Search and Reinforcement Learning

Mathematics ◽

10.3390/math8081254 ◽

2020 ◽

Vol 8 (8) ◽

pp. 1254 ◽

Cited By ~ 1

Author(s):

Cheng-Hung Chen ◽

Shiou-Yun Jeng ◽

Cheng-Jian Lin

Keyword(s):

Fuzzy Logic ◽

Reinforcement Learning ◽

Mobile Robot ◽

Fuzzy Logic Controller ◽

Search Algorithm ◽

Experimental Results ◽

Average Error ◽

Stopover Site ◽

Accumulated Reward ◽

Wall Following

In this study, a fuzzy logic controller with the reinforcement improved differential search algorithm (FLC_R-IDS) is proposed for solving a mobile robot wall-following control problem. This study uses the reward and punishment mechanisms of reinforcement learning to train the mobile robot wall-following control. The proposed improved differential search algorithm uses parameter adaptation to adjust the control parameters. To improve the exploration of the algorithm, a change in the number of superorganisms is required as it involves a stopover site. This study uses reinforcement learning to guide the behavior of the robot. When the mobile robot satisfies three reward conditions, it gets reward +1. The accumulated reward value is used to evaluate the controller and to replace the next controller training. Experimental results show that, compared with the traditional differential search algorithm and the chaos differential search algorithm, the average error value of the proposed FLC_R-IDS in the three experimental environments is reduced by 12.44%, 22.54% and 25.98%, respectively. Final, the experimental results also show that the real mobile robot using the proposed method can effectively implement the wall-following control.

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