Denoising method of lidar echo signal based on variational mode decomposition

Abstract

The invention relates to a denoising method of laser radar echo signal based on variational mode decomposition. The method first simulates the laser radar echo signal according to the laser radar equation; then uses detrending fluctuation analysis to select the variational mode decomposition The optimal number of decomposition layers for K ; Then, the variational mode decomposition is performed on the lidar echo signal; then the similarity between the lidar echo signal and the probability density function of each mode after decomposition is calculated by the Bhattacharyachian distance, and the relevant mode is distinguished from the non-correlated mode ; Then use the moving average method to process the non-correlated modes, and extract the relevant signals; finally, reconstruct the relevant modes and the processed non-correlated modes to realize the denoising of the lidar echo signal. This method can effectively filter out the noise in the laser radar echo signal, and improve the effective detection distance and accuracy 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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