Algoritma Kalman Filter Terdistribusi Pada Jaringan Sensor Network

Prabowo, Evan Satya (2015) Algoritma Kalman Filter Terdistribusi Pada Jaringan Sensor Network. Undergraduate thesis, Institut Technology Sepuluh Nopember.

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Abstract

Jaringan Sensor Nirkabel (Wireless Sensor Network) adalah hal baru dalam perkembangan Jaringan Sensor (Sensor Network) yang mempunyai potensi yang sangat bagus dalam pemanfaatan potensi alam. Banyak aplikasi yang mulai memanfaatkan teknologi Jaringan Sensor Nirkabel (Wireless Sensor Network) seperti alat pendukung pemberi informasi trafik dalam dunia telekomunikasi, pendukung pemberi informasi tentang kondisi lalu lintas, eksplorasi dan pemantauan lahan, penanggulangan bencana, pengintaian medan perang, pemberi informasi temperatur, cahaya, getaran, dan lain lain. Algoritma Kalman Filter banyak diterapkan pada sistem dinamik stokastik. Model matematika dari masalah-masalah fisis di alam dapat dinyatakan dalam bentuk eksplisit, tetapi ada masalah–masalah yang hanya bisa dinyatakan dalam bentuk implisit. Sampai saat ini Kalman Filter lebih banyak diterapkan pada sistem dinamik stokastik eksplisit Pada tugas akhir ini, akan dibandingkan antara algoritma Kalman Filter terdistribusi tanpa melalui consensus dan algoritma Kalman Filter terdistribusi dengan melalui consensus. Berdasarkan hasil yang didapat dari simulasi Algoritma Kalman Filter dengan menggunakan consensus didapatkan nilai 54.2610C sedangkan pada Algoritma Kalman Filter yang diterapkan pada setiap node didapatkan nilai 34.23870C pada node 1, 48.69340C pada node2, dan 64,03870C pada node 3. ======================================================================================================= Wireles Sensor Network is a new thing in the development of sensor network which has very good potential in the itilization of natural potential. Many applications start utilizing technology Wireless Sensor Network as a tool too support providers of traffic information in the world of telecommunications, information providers supporters on traffic conditions,exploration and land monitoring, disaster response, battlefield surveillance, informers temperature, light, vibrations, and others. . Kalman filter algorithm is applied to many stochastic dynamic systems. Mathematical model of the problem that can only expressed in implicit form. Until now, the Kalman Filter applied to the more explicit stochastic dynamic systems. In this final project, will be compared between the Kalman filter algorithm is distributed without consensus and Kalman filter algorithms distributed through consensus. Based on the results obtained from simulations using the Kalman filter algorithm consensus 54.2610C whereas the values obtained Kalman filter algorithm is applied to each node in the node values obtained 34.23870C 1, 48.69340C on node2, and 64.03870 C at node 3.

Item Type: Thesis (Undergraduate)
Additional Information: RSE 621.381 536 Pra a
Uncontrolled Keywords: Kalman Filter, Star, Jaringan Sensor Network
Subjects: Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science)
Divisions: Faculty of Industrial Technology > Electrical Engineering > 20201-(S1) Undergraduate Thesis
Depositing User: Mr. Tondo Indra Nyata
Date Deposited: 26 Jun 2018 08:39
Last Modified: 26 Jun 2018 08:39
URI: http://repository.its.ac.id/id/eprint/52043

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