Optimal VMD-Based Signal Denoising for Laser Radar via Hausdorff Distance and Wavelet Transform

Tuan Hua,Tao Feng,He Zhang,Kefeng Xie,Keren Dai,Zongchen Yao,Hongjian Wang,Xiangjin Zhang

doi:10.1109/access.2019.2949063

Abstract

Laser radar echo signals are easily contaminated by noise, such as background light and electronic noise, and this noise is an obstacle for the subsequent signal detection. However, the conventional denoising methods cannot achieve satisfactory effects when the signal-to-noise-ratio (SNR) is ultralow. In this paper, a novel denoising method for laser radar echo signals based on the parameter-optimal variational mode decomposition (VMD) combined with the Hausdorff distance (HD) and wavelet transform (WT) is proposed. Compared with conventional VMD-based methods, the proposed method utilizes a newly developed grasshopper optimization algorithm (GOA) to obtain the optimal combination of parameters for the VMD. Then, the HD is applied to select the relevant modes and then uses the basis function to reconstruct the signal. In addition, the relevant modes are further processed by the WT denoising method, which allows the reconstructed signal to obtain a higher SNR. The simulation and experimental results show the feasibility, effectiveness and robustness of the proposed method compared to three other available denoising techniques. The proposed method could promote the distance measurement performance of laser radars in harsh environments.

Highlights

Noise has an enormous effect on signal acquisition and processing in many scientific and engineering applications, including laser radars [1], [2], light communications [3]–[5] and ammunition launches [6]
The performance improvement of the proposed method is discussed via some comparisons, which show the contributions of the three key innovations in the proposed OVMD-HDWT method
The performance of some other denoising methods such as the NeighCoeff-db4 wavelet method (WT-db4), the empirical mode decomposition (EMD) combined with wavelet denoising method (EMD-DWT) [37] and the detrended fluctuation analysis thresholded EMD-based denoising method (DFA-EMD) [38] are compared with the proposed method

Summary

Introduction

Noise has an enormous effect on signal acquisition and processing in many scientific and engineering applications, including laser radars [1], [2], light communications [3]–[5] and ammunition launches [6]. The associate editor coordinating the review of this manuscript and approving it for publication was Guolong Cui. performance of laser radars is threatened by low signalto-noise-ratio (SNR) echo signals. The conventional laser radar echo signal denoising methods are mainly based on empirical mode decomposition (EMD) [2], [12], [13]. It will cause mode mixing during the decomposition, which limits its applications in long-range, low SNR radar echo signal denoising [14]. The emergence of variational mode decomposition (VMD) provides new possibilities for solving these problems. It is a new adaptive denoising decomposition technique that can nonrecursively decompose a signal into an ensemble of band-limited intrinsic mode functions (BLIMFs) [15]

Objectives

Results

Conclusion