Parametric Decomposition of Pulsed Lidar Signals with Noise Corruption Using FRFT Spectrum Analysis

Fan Xu,Xiaofei Zhang,Qihui Wu,Zhengyang Shu,Ya Liu,Jun Chen

doi:10.3390/rs13163296

Abstract

The parametric decomposition of full-waveform Lidar data is challenging when faced with heavy noise scenarios. In this paper, we report a fractional Fourier transform (FRFT)-based approach for accurate parametric decomposition of pulsed Lidar signals with noise corruption. In comparison with other joint time-frequency analysis (JTFA) techniques, FRFT is found to present a one-dimensional Lidar signal by a particular two-dimensional spectrum, which can exhibit the mathematical distribution of the multiple components in Lidar signals even with a heavy noise interference. A FRFT spectrum-processing solution with histogram clustering and moving LSM fitting is designed to extract the amplitude, time offset, and pulse width contained in the mathematical distribution. Extensive experimental results demonstrate that the proposed FRFT spectrum analysis method can remarkably outperform the conventional Levenberg–Marquardt-based method. In particular, it can accurately decompose the amplitudes, time offsets, and pulse widths of the pulsed Lidar signal with a −10-dB signal-to-noise-ratio by mean deviation ratios of 4.885%, 0.531%, and 7.802%, respectively.

Highlights

We explored the performances of the some representative joint time-frequency analysis (JTFA) methods including continuous wavelet transform (CWT), synchrosqueezing transform (SST), Wigner-Ville distribution (WVD), and fractional Fourier transform (FRFT) for light detection and ranging (Lidar) signals
To investigate the real FRFT distribution of multi-pulsed Lidar signals in a strong noise scenario, we carried out an on-site experiment
To inspire the use of JTFA techniques for representation of pulse Lidar signals, some representative JTFA techniques including CWT, SST, WVD, and FRFT are investigated and compared. This preliminary trial indicates that FRFT distribution of pulsed Lidar signal is somewhat particular and quite robust to noise

Summary

Introduction

Laser beams are quite sensitive to environmental factors and optoelectronic systems during the long-distance remote sensing, which hampers the accuracy of these decomposition techniques. Some approaches such as wavelet thresholding, empirical mode decomposition (EMD), and Kalman filtering have been used practically for denoising and enhancement of Lidar signals [8,9,10,12,13,14,15], they can only support the decomposition techniques to work well in a low-level noise condition.

Results

Discussion

Conclusion