Satellite-Signal Dependence on Rain and Atmospheric Temperature

2021 ◽  
Vol 9 (2) ◽  
pp. 71-74
Author(s):  
Ravindra Naithani ◽  
◽  
Thaisa Jawhly ◽  
Ramesh Chandra Tiwari ◽  
◽  
...  
Keyword(s):  
Rain Rate ◽  
Signal Strength ◽  
Atmospheric Temperature ◽  
Received Signal Strength ◽  
Strong Positive Correlation ◽  
Rain Intensity ◽  
Satellite Signal ◽  
Signal Dependence ◽  
Band Signal ◽  
Ku Band

In this paper, we present the Ku band signal strength relationship with rain rate and temperature. The correlation analysis of rain intensity with signal data indicates that rain rate negatively impacts the Ku band link while the atmospheric temperature has a strong positive correlation with the received signal strength. The statistical analysis showed that both temperature and rain intensity significantly influenced the received-signal strength data. This study is a preliminary analysis and aims to draw the association between a Ku band signal with rain intensity and temperature.

scholarly journals Development of software for Ku-Band signal availability due to rain attenuation

2019 ◽  
Vol 13 (3) ◽  
pp. 1252
Author(s):  
Norsuzila Ya’acob ◽  
Noraisyah Tajudin ◽  
Muhammad Rezza Alui ◽  
Nani Fadzlina Naim ◽  
Murizah Kassim ◽  
...  
Keyword(s):  
Satellite Communication ◽  
Rain Rate ◽  
Prediction Models ◽  
Weather Prediction ◽  
Rain Attenuation ◽  
Cumulative Distribution ◽  
One Year ◽  
Satellite Dish ◽  
Band Signal ◽  
Ku Band

<span>Ku-Band signal is often used for satellite communication mainly for direct to home (DTH) broadcasting. One of the major issues using this band is that the signal will be affected by raindrops. Raindrops absorb and scatter signal that operates at a frequency of more than 10 GHz. However, studies have been done to predict and measure the rainfall rate and rain attenuation. The rain attenuation in Ku-Band range and the rain rate were measured at satellite receiving dish, pointed towards the orbital slot 91.5 E over a one-year period in 2013. The cumulative distribution of rain rate obtained as well as a cumulative distribution of rain attenuation obtained are presented and compared with the rain prediction models. The aim is to get the best model to be used for the purpose of software development. It was found out that the DAH prediction model is fairly equitable when compared to direct satellite dish receiving measurements in Malaysia. The model provided a suitable baseline in developing a user interface software for weather prediction.</span>

KAJIAN KEKUATAN SINYAL RADIO (RSSI) XBEE DALAM RANGKA PEMASANGAN LANDSLIDE EARLY WARNING SYSTEM (LEWS) DI KABUPATEN GARUT, TASIKMALAYA DAN MAJALENGKA

2019 ◽  
Vol 3 (2) ◽  
pp. 88
Author(s):  
Riski Fitriani
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Received Signal Strength Indicator ◽  
Landslide Early Warning ◽  
Point To Point

Salah satu inovasi untuk menanggulangi longsor adalah dengan melakukan pemasangan Landslide Early Warning System (LEWS). Media transmisi data dari LEWS yang dikembangkan menggunakan sinyal radio Xbee. Sehingga sebelum dilakukan pemasangan LEWS, perlu dilakukan kajian kekuatan sinyal tersebut di lokasi yang akan terpasang yaitu Garut, Tasikmalaya, dan Majalengka. Kajian dilakukan menggunakan 2 jenis Xbee yaitu Xbee Pro S2B 2,4 GHz dan Xbee Pro S5 868 MHz. Setelah dilakukan kajian, Xbee 2,4 GHz tidak dapat digunakan di lokasi pengujian Garut dan Majalengka karena jarak modul induk dan anak cukup jauh serta terlalu banyak obstacle. Topologi yang digunakan yaitu topologi pair/point to point, dengan mengukur nilai RSSI menggunakan software XCTU. Semakin kecil nilai Received Signal Strength Indicator (RSSI) dari nilai receive sensitivity Xbee maka kualitas sinyal semakin baik. Pengukuran dilakukan dengan meninggikan antena Xbee dengan beberapa variasi ketinggian untuk mendapatkan kualitas sinyal yang lebih baik. Hasilnya diperoleh beberapa rekomendasi tinggi minimal antena Xbee yang terpasang di tiap lokasi modul anak pada 3 kabupaten.

2.4 GHz RF Received Signal Strength Based Node Separation in WSN Monitoring Infrastructure for Millet and Rice Vegetation

2021 ◽  
pp. 1-1
Author(s):  
Pankaj Pal ◽  
Rashmi Priya Sharma ◽  
Sachin Tripathi ◽  
Chiranjeev Kumar ◽  
Dharavath Ramesh
Keyword(s):  
Signal Strength ◽  
Received Signal Strength ◽  
Strength Based

scholarly journals A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2392
Author(s):  
Óscar Belmonte-Fernández ◽  
Emilio Sansano-Sansano ◽  
Antonio Caballer-Miedes ◽  
Raúl Montoliu ◽  
Rubén García-Vidal ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Indoor Localization ◽  
Hidden Markov ◽  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Machine Learning Algorithms ◽  
Forward Algorithm ◽  
Generative Method

Indoor localization is an enabling technology for pervasive and mobile computing applications. Although different technologies have been proposed for indoor localization, Wi-Fi fingerprinting is one of the most used techniques due to the pervasiveness of Wi-Fi technology. Most Wi-Fi fingerprinting localization methods presented in the literature are discriminative methods. We present a generative method for indoor localization based on Wi-Fi fingerprinting. The Received Signal Strength Indicator received from a Wireless Access Point is modeled by a hidden Markov model. Unlike other algorithms, the use of a hidden Markov model allows ours to take advantage of the temporal autocorrelation present in the Wi-Fi signal. The algorithm estimates the user’s location based on the hidden Markov model, which models the signal and the forward algorithm to determine the likelihood of a given time series of Received Signal Strength Indicators. The proposed method was compared with four other well-known Machine Learning algorithms through extensive experimentation with data collected in real scenarios. The proposed method obtained competitive results in most scenarios tested and was the best method in 17 of 60 experiments performed.

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