Data Fusion Method Based on Adaptive Kalman Filtering

Authors

Bernadus Herdi Sirenden, Research Center of Metrology LIPI, Puspiptek Serpong, South Tangerang 15311, BantenFollow

Abstract

This paper discusses data fusion methods to combine the data from a rotary encoder and ultrasonic sensor. Both sensors are used in a micro-flow calibration system developed by the Research Center of Metrology LIPI. The methods studied are hierarchical data fusion and Kalman filtering. Three types of Kalman filters (KFs) are compared: the conventional Kalman filter and two adaptive Kalman filters. Moreover, a method to combine the uncertainty results from KF in hierarchical data fusion is proposed. The aim of this study is to find appropriate methods of data fusion that can be implemented in micro-flow calibration systems. Data from two experiment setups are used to compare the methods. The result indicates that one of the methods (with little adjustment) is more appropriate than the other.

Bahasa Abstract

Metode Penggabungan Data Berdasarkan Adaptive Kalman Filtering. Makalah ini membahas tentang metode fusi data antara rotary encoder dan sensor ultrasonik. Kedua sensor yang digunakan pada sistem aliran kalibrasi mikro yang dikembangkan oleh Pusat Penelitian Metrologi LIPI (RCM-LIPI). Metode yang dikaji dalam makalah ini adalah fusi data hierarkis dan Kalman Filter. Tiga jenis Kalman Filter dibandingkan dalam makalah ini, konvensional dan dua metode adaptif. Makalah ini juga mengusulkan metode untuk menggabungkan hasil ketidakpastian dari Kalman Filter dalam fusi data yang hiearkis. Tujuannya adalah untuk menemukan metode yang tepat, serta dapat diimplementasikan untuk sistem aliran kalibrasi mikro. Data dari dua konfigurasi percobaan digunakan untuk membandingkan metode-metode tersebut. Hasilnya mengarah ke kesimpulan bahwa salah satu metode (dengan sedikit penyesuaian), lebih tepat daripada lainnya.

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Recommended Citation

Sirenden, Bernadus Herdi (2019) "Data Fusion Method Based on Adaptive Kalman Filtering," Makara Journal of Technology: Vol. 23: Iss. 1, Article 6.
DOI: 10.7454/mst.v23i1.3432
Available at: https://scholarhub.ui.ac.id/mjt/vol23/iss1/6