Abstract
Among the complete family of sensors for automotive safety, consumer andindustrial application, speed sensors stand out as one of the most important. Actually, speedsensors have the diversity to be used in a broad range of applications. In today’s automotiveindustry, such sensors are used in the antilock braking system, the traction control systemand the electronic stability program. Also, typical applications are cam and crank shaftposition/speed and wheel and turbo shaft speed measurement. In addition, they are used tocontrol a variety of functions, including fuel injection, ignition timing in engines, and so on.However, some types of speed sensors cannot respond to very low speeds for differentreasons. What is more, the main reason why such sensors are not good at detecting very lowspeeds is that they are more susceptible to noise when the speed of the target is low. In short,they suffer from noise and generally only work at medium to high speeds. This is one of thedrawbacks of the inductive (magnetic reluctance) speed sensors and is the case under study.Furthermore, there are other speed sensors like the differential Hall Effect sensors that arerelatively immune to interference and noise, but they cannot detect static fields. This limitstheir operations to speeds which give a switching frequency greater than a minimumoperating frequency. In short, this research is focused on improving the performance of avariable reluctance speed sensor placed in a car under performance tests by using arecursive least-squares (RLS) lattice algorithm. Such an algorithm is situated in an adaptivenoise canceller and carries out an optimal estimation of the relevant signal coming from thesensor, which is buried in a broad-band noise background where we have little knowledgeof the noise characteristics. The experimental results are satisfactory and show a significantimprovement in the signal-to-noise ratio at the system output.
Highlights
The automobile is one of our main means of transportation, and we make extensive use of it throughout our lives
This paper shows the improvement of the real-time response of a wheel speed sensor placed in a car under performance tests
All this happens in such a way that the performance of the adaptive filter is continuously improved according to a specified performance criterion which has been previously established by the designer
Summary
Adaptive filtering is a very important field of research that is focused on the design of selfdesigning systems with the ability to perform satisfactorily in an environment where complete knowledge of the relevant signal characteristics is not available. It is important to point out that adaptive filters perform much better than classical filters in applications where the unwanted information and the relevant signal share the same frequency spectrum. As a matter of fact, the more the noise and the relevant signal share the same (or a very similar) frequency spectrum, the less the designer can remove the unwanted information by using classical filters [24, 44-49]. According to Hernandez [24], an adaptive filter is a filter with a mechanism for adjusting its own parameters automatically by using a recursive algorithm at the same time that it is in active interaction with the environment. The RLS lattice algorithm using a priori estimation errors with error feedback
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