5.3 Physical Anti-Collision Based on Wavelet
185
Fig. 5.24 Test flow chart
has become a common frequency domain analysis tool for signal processing. The
principle of wavelet threshold denoising method is based on different properties of
wavelet coefficients in different scales between signal and noise. By using mathematical tool to select the corresponding coefficients, the wavelet threshold denoising
method can process the wavelet coefficients of noisy signals. The basic process is
shown in Fig. 5.25.
The basic idea of wavelet threshold denoising method is to set a critical threshold λ.
If wavelet coefficients are less than λ, it is considered that the coefficients are mainly
caused by noise, and the coefficients will be removed. If the wavelet coefficients are
larger than λ, the coefficients are considered to be mainly caused by the signal, and
the coefficients will be obtained. Finally, the wavelet coefficients are transformed by
Fig. 5.25 Wavelet denoising flow chart
185
Fig. 5.24 Test flow chart
has become a common frequency domain analysis tool for signal processing. The
principle of wavelet threshold denoising method is based on different properties of
wavelet coefficients in different scales between signal and noise. By using mathematical tool to select the corresponding coefficients, the wavelet threshold denoising
method can process the wavelet coefficients of noisy signals. The basic process is
shown in Fig. 5.25.
The basic idea of wavelet threshold denoising method is to set a critical threshold λ.
If wavelet coefficients are less than λ, it is considered that the coefficients are mainly
caused by noise, and the coefficients will be removed. If the wavelet coefficients are
larger than λ, the coefficients are considered to be mainly caused by the signal, and
the coefficients will be obtained. Finally, the wavelet coefficients are transformed by
Fig. 5.25 Wavelet denoising flow chart