Laser radar echo signal de-noising method based on variational mode decomposition

Abstract

The invention relates to a laser radar echo signal de-noising method based on variational mode decomposition. The method comprises the steps of according to a laser radar equation, simulating a laserradar echo signal; selecting an optimal decomposition layer number K in variational mode composition by means of detrended fluctuation analysis; performing variational mode decomposition on the laserradar echo signal; then calculating similarity between the laser radar echo signal and each mode probability density function after decomposition through Bhattacharyya distance, differentiating a correlation mode and an uncorrelated mode; then processing the uncorrelated mode by means of a moving average mode, and extracting a related signal; and finally reconstructing the correlation mode and theprocessed uncorrelated mode for realizing de-noising of the laser radar echo signal. The laser radar echo signal de-noising method can effectively filter the noise in the laser radar echo signal andimproves effective detecting distance and precision of the laser radar.

Description

technical field [0001] The invention relates to a laser radar echo signal denoising method based on variational mode decomposition, and belongs to the technical field of laser radar data processing. Background technique [0002] Lidar is the product of the combination of traditional radar technology and modern laser technology. It has the characteristics of high resolution, good detection performance, and strong anti-interference ability. , visibility, etc. [0003] In practical applications, the intensity of the lidar echo signal decreases with the increase of the detection distance, and it is mixed with various noises. In particular, the strong background light of the sun during the day will cause the signal to be completely submerged in the noise. It will directly affect the effective detection distance and accuracy of lidar. Therefore, using an effective noise filtering method to extract useful signals from the background noise is the key to realize all-weather and hig...

AI Technical Summary

Problems solved by technology

Kalman filtering is an algorithm that uses the minimum mean square error as the best criterion to seek recursive estimation. It can process time-varying systems, non-stationary signals, and multidimensional signals. However, when the signal changes sharply, this method will cause huge error

The wavelet transform can decompose the signal into different frequency components through multi-scale analysis, and has good time-frequency localization characteristics, but there is a problem with the selection of wavelet basis functions, and the adaptability is poor

Empirical mode decomposition makes up for the shortcomings of wavelet transform, has good adaptability, and can perfectly reflect the local frequency characteristics of the signal, but it lacks strict mathematical proof in theory, and there are endpoint effects, modal mixing, etc. The problem is that it interferes with the subsequent decomposition of the signal, so that the decomposition result cannot effectively separate the useful signal from the noise, which affects the denoising effect

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