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Full-waveform LiDAR echo decomposition based on wavelet decomposition and particle swarm optimization

To measure the distances and properties of the objects within a laser footprint, a decomposition method for full-waveform light detection and ranging (LiDAR) echoes is proposed. In this method, firstly, wavelet decomposition is used to filter the noise and estimate the noise level in a full-waveform echo. Secondly, peak and inflection points of the filtered full-waveform echo are used to detect the echo components in the filtered full-waveform echo. Lastly, particle swarm optimization (PSO) is used to remove the noise-caused echo components and optimize the parameters of the most probable echo components. Simulation results show that the wavelet-decomposition-based filter is of the best improvement of SNR and decomposition success rates than Wiener and Gaussian smoothing filters. In addition, the noise level estimated using wavelet-decomposition-based filter is more accurate than those estimated using other two commonly used methods. Experiments were carried out to evaluate the proposed method that was compared with our previous method (called GS-LM for short). In experiments, a lab-build full-waveform LiDAR system was utilized to provide eight types of full-waveform echoes scattered from three objects at different distances. Experimental results show that the proposed method has higher success rates for decomposition of full-waveform echoes and more accurate parameters estimation for echo components than those of GS-LM. The proposed method based on wavelet decomposition and PSO is valid to decompose the more complicated full-waveform echoes for estimating the multi-level distances of the objects and measuring the properties of the objects in a laser footprint.