Penerapan Filter Kalman untuk Estimasi Jarak dan Posisi pada Lokalisasi Outdoor berbasis RSSI dengan Komunikasi LoRa

MUSAYYANAH MUSAYYANAH, CHARISMA DIMAS AFFANDI, KRISTIN LEBDANINGRUM

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ABSTRAK

Wireless Sensor Networ (WSN) merupakan jaringan nirkabel terdiri dari kumpulan node sensor tersebar di lingkungan observasi, saling berkomunikasi sesuai dengan algoritma komunikasi. Salah satu penerapan WSN adalah lokalisasi berbasis RSSI. RSSI merupakan hasil lokalisasi yang bersifat fluktuatif disebabkan oleh fenomena multipath fading, sehingga berpengaruh terhadap hasil lokalisasi. Artikel ini menerapkan Filter Kalman (FK) untuk lokalisasi Outdoor berbasis RSSI dengan komunikasi LoRa untuk lima Node Diam (ND) dan empat Node Anchor (NA). Pengujiannya terdiri dari membandingkan FK dengan tanpa FK, dan FK dengan metode Normalisasi. FK dapat memperbaiki akurasi pada estimasi jarak dan posisi. FK memperbaiki akurasi estimasi jarak sebesar 0,57% untuk ND1; 0,19% untuk ND2; 4,59% untuk ND3; 0,73% untuk ND4 dan 20,11% untuk ND5. Pada estimasi posisi, FK dapat meningkatkan akurasi sebesar 2,45% untuk ND1; 11,19% untuk ND2; 6,03% untuk ND3; 7,64% untuk ND4; dan 5,42% untuk ND5. Selain itu, FK dapat memperbaiki akurasi 15,94% untuk ND1; 3,41% untuk ND 2 dan 15,76% untuk ND 4 terhadap metode Normalisasi.

Kata kunci: Filter Kalman, Localization,LoRa, RSSI, Wireless Sensor Network

 

ABSTRACT

Wireless Sensor Network (WSN) is a wireless network consisting of sensor nodes spread across an observation environment, communicating with others separately according to a communication algorithm. In addition, one of the WSN applications is RSSI-based localization. RSSI is the development of localization which fluctuates due to the multipath fading phenomenon, thus affecting the localization results. This article executes a Kalman Filter (KF) for RSSI-based Outdoor localization with LoRa communication for five Diam Nodes (ND) and four Anchor Nodes (NA). The experiment involves comparing KF with neither KF nor the Normalization method. KF can improve the accuracy of distance and location estimation. FK increases the distance estimation accuracy by 0,57% for ND1; 0,19% for ND2; 4,59% for ND3; 0,73% for ND4; and 20,11% for ND5. For ND1, ND2, ND3, ND4, and ND5, KF can improve position estimation accuracy by 2,45%, 11,19%, 6,03%, 7,64%, 5,42%, and 2,45%, respectively. In addition, KF can increase accuracy by 15,94% for ND1, 3,41% for ND2, and 15,76% for ND4 in comparison to the Normalization approach.

Keywords: Kalman Filter, Localization, LoRa, RSSI, Wireless Sensor Network


Kata Kunci


Filter Kalman; Localization; LoRa; RSSI; Wireless Sensor Network

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Referensi


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DOI: https://doi.org/10.26760/elkomika.v11i4.849

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