Wireless positioning method based on wavelet transform modulus maxima

Abstract

The invention belongs to the technical field of wireless positioning, and relates to a wireless positioning method based on a Wavelet Transform (WT) modulus maximum value. The method mainly comprises the following steps of: performing multi-resolution analysis (MRA) on a signal at a signal preprocessing end; solving modulus maxima of each scale according to the wavelet transform coefficient on each scale; selecting a proper threshold value to process the modulus maximum value, and reserving extreme points greater than or equal to the threshold value; from the highest scale J to the scale 1, searching a propagation point of a modulus maximum value on an adjacent scale, and according to a propagation rule that the modulus extreme values of the signal and the noise are different, retaining the modulus extreme points of the signal and removing the modulus extreme points of the noise; and the processed modulus maximum point is utilized to reconstruct a signal for subsequent conventional positioning operation. According to the method, the signal noise reduction effect can be greatly improved, the signal-to-noise ratio (SNR) is improved, the positioning precision is improved, and convenience is provided for positioning.

Description

Technical field [0001] The present invention relates to a wireless positioning technique, which is a wireless positioning method based on a large value of a wavelet transform. Background technique [0002] IEEE802.15.4A standard based on UWB (Ultra Wide Band, Ultra-wideband) is the first international standard for wireless ranging and positioning. At present, there has been a large progress in UWB wireless positioning research at home and abroad, but because multipath Effect, noise interference and other factors, the positioning accuracy is not ideal, so it is necessary to reduce the noise reduction processing of the received signal. The classic signal noise reduction is mainly based on the frequency domain, such as by the Badwo low-pass filter, separating the useful signals and noise on the frequency domain. However, in the actual situation, the spectrum of the useful signal and the spectrum of the noise often overlap, such as Gaussia white noise, which is almost all over the en...

AI Technical Summary

Problems solved by technology

However, in actual situations, the spectrum of the useful signal and the spectrum of the noise often overlap, such as Gaussian white noise, whose spectrum is almost all over the entire frequency domain, so the noise reduction effect of the classic low-pass filtering method, or just Can remove a small part of the noise, or lose important components in the signal, such as points with large singularities in the signal

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