Improving the Response of Accelerometers for Automotive Applications by Using LMS Adaptive Filters
AbstractIn this paper, the least-mean-squares (LMS) algorithm was used to eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications. This kind of accelerometer is designed to be easily mounted in hard to reach places on vehicles under test, and they usually feature ranges from 50 to 2,000 g (where is the gravitational acceleration, 9.81 m/s2) and frequency responses to 3,000 Hz or higher, with DC response, durable cables, reliable performance and relatively low cost. However, here we show that the response of the sensor under test had a lot of noise and we carried out the signal processing stage by using both conventional and optimal adaptive filtering. Usually, designers have to build their specific analog and digital signal processing circuits, and this fact increases considerably the cost of the entire sensor system and the results are not always satisfactory, because the relevant signal is sometimes buried in a broad-band noise background where the unwanted information and the relevant signal sometimes share a very similar frequency band. Thus, in order to deal with this problem, here we used the LMS adaptive filtering algorithm and compare it with others based on the kind of filters that are typically used for automotive applications. The experimental results are satisfactory.
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Hernandez, W.; De Vicente, J.; Sergiyenko, O.; Fernández, E. Improving the Response of Accelerometers for Automotive Applications by Using LMS Adaptive Filters. Sensors 2010, 10, 313-329.
Hernandez W, De Vicente J, Sergiyenko O, Fernández E. Improving the Response of Accelerometers for Automotive Applications by Using LMS Adaptive Filters. Sensors. 2010; 10(1):313-329.Chicago/Turabian Style
Hernandez, Wilmar; De Vicente, Jesús; Sergiyenko, Oleg; Fernández, Eduardo. 2010. "Improving the Response of Accelerometers for Automotive Applications by Using LMS Adaptive Filters." Sensors 10, no. 1: 313-329.