Improving the Response of Accelerometers for Automotive Applications by Using LMS Adaptive Filters: Part II
| Autor: | Jesús De Vicente y Oliva, Wilmar Hernandez, Oleg Sergiyenko, Eduardo Fernandez |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Acceleration
Transducers 010401 analytical chemistry Equipment Design lcsh:Chemical technology conventional LMS adaptive filter 01 natural sciences Biochemistry Article Atomic and Molecular Physics and Optics Computer Science::Other 0104 chemical sciences Analytical Chemistry Equipment Failure Analysis 010309 optics piezoresistive accelerometer 0103 physical sciences lcsh:TP1-1185 Least-Squares Analysis Electrical and Electronic Engineering Artifacts Automobiles Instrumentation Algorithms fast LMS adaptive filter |
| Zdroj: | Sensors, Vol 10, Iss 1, Pp 952-962 (2010) Sensors (Basel, Switzerland) Sensors Volume 10 Issue 1 Pages 952-962 |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s100100952 |
| Popis: | In this paper, the fast least-mean-squares (LMS) algorithm was used to both eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications, and improve the convergence rate of the filtering process based on the conventional LMS algorithm. The response of the accelerometer under test was corrupted by process and measurement noise, and the signal processing stage was carried out by using both conventional filtering, which was already shown in a previous paper, and optimal adaptive filtering. The adaptive filtering process relied on the LMS adaptive filtering family, which has shown to have very good convergence and robustness properties, and here a comparative analysis between the results of the application of the conventional LMS algorithm and the fast LMS algorithm to solve a real-life filtering problem was carried out. In short, in this paper the piezoresistive accelerometer was tested for a multi-frequency acceleration excitation. Due to the kind of test conducted in this paper, the use of conventional filtering was discarded and the choice of one adaptive filter over the other was based on the signal-to-noise ratio improvement and the convergence rate. |
| Databáze: | OpenAIRE |
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