Paper Info
Title
Improving the response of accelerometers for automotive applications by using LMS adaptive filters: Part II.
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.
Year
DOI
Venue
2010
10.3390/s100100952
SENSORS
Keywords
Field
DocType
piezoresistive accelerometer,conventional LMS adaptive filter,fast LMS adaptive filter
Least mean squares filter,Signal processing,Filter (signal processing),Electronic engineering,Filtering problem,Robustness (computer science),Adaptive filter,Kernel adaptive filter,Engineering,Multidelay block frequency domain adaptive filter
Journal
Volume
Issue
ISSN
10
1
1424-8220
Citations 
PageRank 
References 
1
0.48
5
Authors
4
Name
Order
Citations
PageRank
Wilmar Hernandez12815.89
Jesús de Vicente2102.30
Oleg Y Sergiyenko310.48
Eduardo B. Fernandez41653429.84