ABSTRACT
In this paper, we propose a continuous wavelet transform and iterative decrement algorithm to decompose the light detection and ranging (LiDAR) full-waveform echoes into a series of components, each of which can be assumed as Gaussian essentially. We calculate the scale of continuous wavelet transform in real time according to the relationship between the center frequency of the mother wavelet and the approximate frequency of the transmitted laser pulse. The approximated frequency is calculated according to the half-width of the effective part of transmitted laser pulse. The positions of the Gaussian model components in the echoes can be precisely predicted according to the positions of the maxima of the continuous wavelet transform coefficient. And the boundary points which locate at the left and right sides of the position of the detected components can be detected. Then, the effective sections can be clipped according to the positions of the boundary points. In order to detect the hidden components which are obscured by the high responses from their adjacent components and estimate the initial parameters, the iterative decrement algorithm is carried out. The initial parameters are fitted by the Levenberg-Marquardt algorithm. In order to verify the proposed method, the simulations and experiments whose data is recorded by our coding LiDAR have been done. The simulations and experiments results indicate that the proposed method exhibits excellent performances, and it is valid for the complex full-waveform echo, which includes serious overlapping components.
ABSTRACT
The light detection and ranging (LIDAR) full-waveform echo decomposition method based on empirical mode decomposition (EMD) and the local-Levenberg-Marquard (LM) algorithm is proposed in this paper. The proposed method can decompose the full-waveform echo into a series of components, each of which can be assumed as essentially Gaussian. The original full-waveform echo is decomposed into the intrinsic mode functions (IMFs) and a final residual by using the EMD first. Then, the average period (Tm¯) and corresponding energy densities (EDs) of all IMFs are calculated. A suitable IMF is selected based on the relationship between the EDs of IMFs and the white-noise theoretical spread lines of the 99% confidence-limit level. The components in the full-waveform echo can be detected according to the positions of the maxima of the selected IMF. The initial parameters are estimated by using local-LM fitting. The initial parameters are fitted by global-LM fitting. Compared to the traditional (zero-crossing) ZC method, the proposed method has strong anti-noise performance. It can precisely detect the components and estimate the initial parameters of the components. The proposed method is verified by using the synthetic data; coding LIDAR recorded data; and Land, Vegetation, and Ice Sensor data.
