Abstract :
In this paper, a recursive least-squares lattice (RLSL) adaptive filter was used to carry out the optimal estimation of the relevant signal coming from an accelerometer placed in car under performance tests. Here, the signal of interest is buried in a broadband noise background where we have little knowledge of the noise characteristics. In addition, due to the fact that the noise and the relevant information sometimes share the same or a very similar frequency spectrum, it is very difficult to cancel the noise that corrupts the relevant information without causing that information to deteriorate. The results of the experiment are satisfactory and, in order to compare classical filtering with optimal adaptive filtering, the signal coming from the accelerometer was also filtered by using a third-order lowpass digital Butterworth filter. The results of comparing the aforementioned filters show that the optimal adaptive filter is superior to the classical filter. Here, a significant improvement of 22.4 dB in the signal-to-noise ratio (SNR) at the RLSL adaptive filter output was achieved. However, the improvement in the SNR at the Butterworth filter output was 6.1 dB, which shows very clear that the optimal adaptive filter performs much better than the classical one
Keywords :
Butterworth filters; accelerometers; adaptive filters; digital filters; low-pass filters; vehicle dynamics; Butterworth filter; accelerometer; adaptive filter; broadband noise background; car under performance tests; digital filter; lowpass filter; optimal estimation; recursive least-squares lattice; Accelerometers; Adaptive filters; Background noise; Digital filters; Frequency; Lattices; Life estimation; Noise cancellation; Recursive estimation; Testing; Accelerometer; adaptive noise canceller; recursive least-squares lattice (RLSL) adaptive filter; third-order lowpass digital Butterworth filter;