Optimal estimation of the relevant information coming from a rollover sensor placed in a car under performance tests

In this paper, an inverse square-root adaptive filtering algorithm for recursive least squares estimation (QR-RLS) was used to carry out the optimal estimation of the relevant information coming from a rollover sensor placed in car under performance tests. In this case, the noise that corrupts the relevant signal coming from the sensor is so dangerous that its negative influence on the electrical systems of the car can prevent both drivers and intelligent systems embedded in today’s cars from making intelligent driving decisions that can save millions of lives every year. For this reason, the solution to this problem safety related problem deserves our full attention. Here, in order to solve this problem, a QR-RLS adaptive filter was used. The results of the experiment were satisfactory, an optimal rollover sensor with very low uncertainty of measurement, very good repeatability, very low non-linearity and a satisfactory sensitivity was designed. Furthermore, a significant improvement of 45 dB in the signal-to-noise ratio (SNR) at the system output was achieved.