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Fusion of High-Dynamic and Low-Drift Sensors Using Kalman Filters

College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, China
Department of Mechanical Engineering, The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia
Authors to whom correspondence should be addressed.
Sensors 2019, 19(1), 186;
Received: 15 November 2018 / Revised: 3 January 2019 / Accepted: 3 January 2019 / Published: 7 January 2019
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
PDF [6496 KB, uploaded 7 January 2019]


In practice, a high-dynamic vibration sensor is often plagued by the problem of drift, which is caused by thermal effects. Conversely, low-drift sensors typically have a limited sample rate range. This paper presents a system combining different types of sensors to address general drift problems that occur in measurements for high-dynamic vibration signals. In this paper, the hardware structure and algorithms for fusing high-dynamic and low-drift sensors are described. The algorithms include a drift state estimation and a Kalman filter based on a linear prediction model. Key issues such as the dimension of the drift state vector, the order of the linear prediction model, are analyzed in the design of algorithm. The performance of the algorithm is illustrated by a simulation example and experiments. The simulation and experimental results show that the drift can be removed while the high-dynamic measuring ability is retained. A high-dynamic vibration measuring system with the frequency range starting from 0 Hz is achieved. Meanwhile, measurement noise was improved 9.3 dB through using the linear-prediction-based Kalman filter. View Full-Text
Keywords: dual sensor system; high dynamic; low drift; Kalman filter dual sensor system; high dynamic; low drift; Kalman filter

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Wu, B.; Huang, T.; Jin, Y.; Pan, J.; Song, K. Fusion of High-Dynamic and Low-Drift Sensors Using Kalman Filters. Sensors 2019, 19, 186.

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