A Survey on Optimal Signal Processing Techniques Applied to Improve the Performance of Mechanical Sensors in Automotive Applications
Department of Circuits and Systems in the EUIT de Telecomunicacion at the Universidad Politecnica de Madrid (UPM),
Campus Sur UPM, Ctra. Valencia km 7, Madrid 28031, Spain
Sensors 2007, 7(1), 84-102; https://doi.org/10.3390/s7010084
Received: 14 January 2007 / Accepted: 26 January 2007 / Published: 27 January 2007
In this paper a survey on recent applications of optimal signal processing techniques to improve the performance of mechanical sensors is made. Here, a comparison between classical filters and optimal filters for automotive sensors is made, and the current state of the art of the application of robust and optimal control and signal processing techniques to the design of the intelligent (or smart) sensors that today’s cars need is presented through several experimental results that show that the fusion of intelligent sensors and optimal signal processing techniques is the clear way to go. However, the switch between the traditional methods of designing automotive sensors and the new ones cannot be done overnight because there are some open research issues that have to be solved. This paper draws attention to one of the open research issues and tries to arouse researcher’s interest in the fusion of intelligent sensors and optimal signal processing techniques.
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MDPI and ACS Style
Hernandez, W. A Survey on Optimal Signal Processing Techniques Applied to Improve the Performance of Mechanical Sensors in Automotive Applications. Sensors 2007, 7, 84-102. https://doi.org/10.3390/s7010084
AMA Style
Hernandez W. A Survey on Optimal Signal Processing Techniques Applied to Improve the Performance of Mechanical Sensors in Automotive Applications. Sensors. 2007; 7(1):84-102. https://doi.org/10.3390/s7010084
Chicago/Turabian StyleHernandez, Wilmar. 2007. "A Survey on Optimal Signal Processing Techniques Applied to Improve the Performance of Mechanical Sensors in Automotive Applications" Sensors 7, no. 1: 84-102. https://doi.org/10.3390/s7010084
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