Next Article in Journal
Nanomaterials as Analytical Tools for Genosensors
Next Article in Special Issue
Using Fuzzy Logic to Enhance Stereo Matching in Multiresolution Images
Previous Article in Journal
A Device for Automatically Measuring and Supervising the Critical Care Patient’S Urine Output
Previous Article in Special Issue
Vision-Based Traffic Data Collection Sensor for Automotive Applications
Article Menu

Export Article

Open AccessArticle
Sensors 2010, 10(1), 952-962; doi:10.3390/s100100952

Improving the Response of Accelerometers for Automotive Applications by Using LMS Adaptive Filters: Part II

1
Department of Circuits and Systems, EUIT de Telecomunicación, Universidad Politécnica de Madrid (UPM), Campus Sur UPM, Ctra. Valencia km 7, Madrid 28031, Spain
2
Department of Applied Physics, ETSI Industriales, Universidad Politécnica de Madrid, Calle José Gutierrez Abascal 2, Madrid 28006, Spain
3
Engineering Institute of Autonomous, University of Baja California, Mexicali, Baja California, México
4
EUIT de Telecomunicación, Universidad Politécnica de Madrid (UPM), Campus Sur UPM, Ctra. Valencia km 7, Madrid 28031, Spain
*
Author to whom correspondence should be addressed.
Received: 22 December 2009 / Revised: 15 January 2010 / Accepted: 25 January 2010 / Published: 26 January 2010
View Full-Text   |   Download PDF [1381 KB, uploaded 21 June 2014]   |  

Abstract

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.
Keywords: piezoresistive accelerometer; conventional LMS adaptive filter; fast LMS adaptive filter piezoresistive accelerometer; conventional LMS adaptive filter; fast LMS adaptive filter
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Hernandez, W.; De Vicente, J.; Sergiyenko, O.Y.; Fernández, E. Improving the Response of Accelerometers for Automotive Applications by Using LMS Adaptive Filters: Part II. Sensors 2010, 10, 952-962.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top