Abstract
Today's automotive industry has a soaring interest in efficient, reliable and robust sensors able to make intelligent driving decisions that can save millions of lives every year. To that end, the sensors used in today's cars are being provided with microprocessors and application-specific integrated circuit technologies that incorporate a certain amount of intelligence into the sensors themselves. In this paper, an inverse square-root adaptive filtering algorithm for recursive least-squares estimation (QR-RLS) is used to improve the response of a wheel speed sensor placed in a car undergoing performance tests. Such an algorithm is used to carry out an optimal estimation of the relevant signal coming from the sensor, which is buried in a broad-band noise background where we have little knowledge of the noise characteristics. The results of the experiment are satisfactory, a significant improvement of 32.5 dB in the signal-to-noise ratio at the QR-RLS adaptive filter output was achieved. Also, in order to compare classical filtering techniques with optimal adaptive filtering techniques, the signal coming from the wheel speed sensor was also filtered by using a second-order lowpass digital Butterworth filter. The results of comparing the aforementioned filters show that the optimal adaptive filter is superior to the classical filter.
Published Version
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