Lidar Echo Signal Research Articles

Lidar AOD inversion and aerosol extinction profile correction method based on GA-BP neural network

Lidar is an effective remote sensing method to obtain the vertical distribution of aerosols, and how to select the aerosol extinction-backscattering ratio (AE-BR) during the inversion process is a key step to guarantee the accuracy of the lidar inversion of aerosol optical thickness (AOD) and aerosol extinction coefficient profile (AECP). In this paper, an inversion algorithm for AOD and AECP based on a genetic BP (GA-BP) neural network is proposed. Simultaneous measurements are carried out using CE318 sun photometer and lidar, and the mapping relationship between the lidar echo signal and AOD is established based on the genetic BP (GA-BP) neural network method, which achieves the accurate inversion of AOD with an absolute error mean value of 0.0156. Based on the AOD output from the GA-BP neural network, the real-time best AE- BR to improve the inversion accuracy of AECP. Finally, practical tests show that the method achieves accurate inversion of AOD, determines the range of AE-BR from 20-50sr, realizes real-time dynamic correction of AECP, and has strong generalization ability and applicability in practical situations.

Comparison of timing discrimination method for pulse-based Lidar

Time discrimination methods of pulse signals serve as the foundation for ranging, identification, and precision in pulse Lidar. Therefore, employing a judicious time discrimination method is pivotal in enhancing Lidar overall performance. This study initially delves into the theoretical distinctions among various time discrimination methods, including constant fraction timing, waveform centroid, dual-threshold waveform, and amplitude with rising edge compensation timing. It meticulously analyzes and compares these diverse methods for time discrimination. Subsequently, employing these methodologies to process Lidar echo signals to contrast their time measurement accuracy. The conclusions will provide valuable guidance for the design and practical application of pulse-type Lidar systems.

Lidar Research during the First Stage of the 89th Cruise of the R/V “Academic Mstislav Keldysh”

A lidar survey of the western part of the Kara Sea was carried out in September 2022. The shipborne radiometric (profiling) lidar PLD-1 was used. The lidar optical unit was located on the 8th deck of the R/V ‘Akademik Mstislav Keldysh’ at an altitude of 15 m above the water surface. Lidar sounding was carried out at stations and underway. The vessel route passed through water areas characterized by a wide range of changes in hydrooptical characteristics. Lidar measurements were accompanied by synchronized measurements of hydrooptical and hydrological characteristics. These measurements were carried out using submersible instruments at stations and using a flow-through measuring complex along the ship's route. The hydrooptical characteristics vertical distribution uniformity in the upper ten-meter layer was controlled remotely using underway lidar data.Good agreement between the spatial distributions of the lidar echo signals parameters, of the hydrooptical and of the hydrological characteristics (coincidence of the locations of various distribution features, local maxima, minima and frontal zones) was demonstrated. A large volume of measurement data has been obtained, which allows for further statistical processing in order to find relationships between the parameters of lidar echo signals and hydrooptical characteristics recorded by contact methods.

Enhanced Wind-Field Detection Using an Adaptive Noise-Reduction Peak-Retrieval (ANRPR) Algorithm for Coherent Doppler Lidar

Wind fields provide direct power for exchanging energy and matter in the atmosphere. All-fiber coherent Doppler lidar is a powerful tool for detecting boundary-layer wind fields. According to the characteristics of the lidar echo signal, an adaptive noise-reduction peak retrieval (ANRPR) algorithm is proposed in this study. Firstly, the power spectrum data are divided into several continuous range gates according to the time series. Then, the adaptive iterative reweighted penalized least-squares (airPLS) method is used to reduce the background noise. Secondly, the continuity between spectra is enhanced by 2D Gaussian low-pass filtering. Finally, an adaptive peak-retrieval algorithm is employed to extract the Doppler shift, facilitating the synthesis of a spatial atmospheric 3D wind field through the vector synthesis method. When comparing data from different heights of the meteorological gradient tower, both the horizontal wind-speed correlation and the horizontal wind-direction correlation exceed 0.90. Experimental results show that the proposed algorithm has better robustness and a longer detection distance than the traditional algorithm.

Real-Time Synchronous Acquisition and Processing of Signal in Coherent Doppler Wind Lidar Using FPGA

Coherent Doppler wind lidar has become a primary remote sensing technique for measuring atmospheric wind fields in recent years. However, the high bandwidth of the time domain echo signal has limited real-time data acquisition and processing. In this work, we propose a real-time data acquisition and preprocessing integrated solution. This approach is implemented using on-chip system field programmable gate array (FPGA) hardware while utilizing a 7-channel base-4 polyphase fast Fourier transform calculation module and a pipelined structure operation. Within a 1 s data collection time, the real-time rapid acquisition and spectral preprocessing of lidar echo signals at a range of 9.9 km can be achieved. We compare the observation data with the measurement data from Windcube100s and radiosondes, and the results indicate that the coherent wind lidar developed in this paper can detect heights above 4.0 km, with effective data acquisition rates of 82% and 61% in the height ranges of 1500–2000 m and 2000–2500 m, respectively. At the height range of 3.5 km, the correlations for horizontal wind speed and direction measured by the lidar were all above 0.99, with the wind speed and direction measurement deviations being better than 0.56 m/s and 8.40∘, respectively.

Investigation of the Relationships between the Parameters of Lidar Echo Signals and Hydrooptical Characteristics in the Western Kara Sea

Investigation of the Relationships between the Parameters of Lidar Echo Signals and Hydrooptical Characteristics in the Western Kara Sea

On the Influence of Spatial Fluctuations of the Water Inherent Optical Properties on the Energy of a Lidar Echo Signal Coming from a Water

Theoretical models of the statistical characteristics of the lidar echo signal have been developed to interpret the results of optical sounding of heavily eutrophicated water bodies. Formulas are obtained for calculating the statistically average value and coefficient of variation of the energy of the elastic backscattering signal coming from the near-surface layer of water with randomly inhomogeneous absorption and scattering coefficients. Examples of the dependence of the indicated signal characteristics on the coefficients of variation of the optical characteristics of water are given. It has been established that fluctuations in the absorption coefficient lead to an increase in the average energy of the received signal, and fluctuations in the scattering coefficient to its slight decrease. A significant decrease in the average echo signal energy can be observed with cross-correlated fluctuations in the absorption and scattering coefficients, i. e. in the case when the attenuation coefficient fluctuates at a constant single scattering albedo. Considerations are made on how algorithms for estimating the average values of the optical characteristics of water and the parameters of their inhomogeneities from the average value and the coefficient of variation of the echo signal energy can be constructed.

Spectral missing color correction based on an adaptive parameter fitting model.

With the development of remote sensing technology, true-color visualization of hyperspectral LiDAR echo signals has become a hotspot for both academic research and commercial applications. The limitation of the emission power of hyperspectral LiDAR causes the loss of spectral-reflectance information in some channels of the hyperspectral LiDAR echo signal. The color reconstructed based on the hyperspectral LiDAR echo signal is bound to have serious color cast problem. To solve the existing problem, a spectral missing color correction approach based on adaptive parameter fitting model is proposed in this study. Given the known missing spectral-reflectance band intervals, the colors in incomplete spectral integration are corrected to accurately restore target colors. Based on the experimental results, the color difference between color blocks and the hyperspectral image corrected by the proposed color correction model is smaller than that of the ground truth, and the image quality is higher, realizing the accurate reproduction of the target color.

PMT gain self-adjustment system for high-accuracy echo signal detection

ABSTRACT The intensity difference between the echo signals from the water surface and bottom during bathymetry LiDAR operation requires a photomultiplier tube (PMT) gain self-adjustment. Otherwise, noise echo signals are collected, while weak and useful signals are not. For this reason, this paper proposes a PMT gain self-adjustment system for the high-accuracy detection of LiDAR echo signals. The developed system uses a field programmable gate array (FPGA) collector as a feedback signal generator, an STM32 controller as the PMT gain and voltage control signal generator and a DA module as the PMT gain voltage conversion circuit. The system controls the PMT gain voltage by judging the feedback signal to achieve PMT gain self-adjustment. The system was verified in an indoor tank, building roof, and outdoor pond experiments. By comparing the experiments, the developed system is shown to detect laser energy intensity with a sensitivity of at least 2.26 times stronger than the traditional system, and can measure water depth at least 2.5 times deeper than the traditional system. Therefore, it can be concluded that the proposed PMT gain self-adjusting system can effectively adapt to the changes in laser energy, improve the measurement of water depth, control the amplitude of the echo signals, increase the accuracy of water depth detection, reduce the saturation of the PMT detector, and protect PMT from damages.

Complex Processing of Lidar Survey Data of Marine Areas

The complex processing of data from the shipborne lidar survey of the Black Sea coastal areas was carried out. In this area, internal waves are often observed according to contact and satellite observations. The source of probing linearly polarized laser pulses in the lidar is a solid-state laser with diode pumping of the YAG: Nd active element with a pulse power of 20 mJ at a wavelength of 532 nm and a duration of 7 ns. The co- and cross-polarized components of the echo signal were recorded during the lidar survey. The cross-polarized component of the echo signal was mainly used due to its greater sensitivity to various kinds of inhomogeneities in the vertical distribution of hydrooptical characteristics. The purpose of processing is to identify quasi-periodic structures in the spatial distribution of the characteristics of lidar echo signals caused the propagation of short-period internal waves. Three processing methods were applied: the approximation method, the wavelet analysis method, and the Hilbert-Huang transform method. A large array of data obtained during 50 hours of lidar survey has been processed. Three 60-minute tracks containing quasi-periodic structures have been identified. The three processing methods results are in good agreement with each other and make it possible to obtain the most complete information about the parameters of the studied processes. The parameters of the recorded quasi-periodic processes are typical for internal waves observed in the coastal areas of the Black Sea. In the future, when processing a large amount of lidar sounding data, it is advisable to carry out fast processing by automated spectral methods at the first stage, and to carry out detailed processing by the approximation method only for those survey areas where quasi-periodic processes have been identified.

Echo-Signal De-Noising of CO2-DIAL Based on the Ensemble Empirical Mode Decomposition

The carbon dioxide (CO2) differential absorption lidar echo signal is susceptible to noise and must satisfy the high demand for signal-retrieval precision. Thus, a proper de-noising method should be selected to improve the inversion result. In this paper, we simultaneously decompose three signal pairs into different intrinsic mode functions (IMFs) using the method of ensemble empirical mode decomposition (EEMD). Further, the correlation coefficients of the IMFs with the same temporal scale are regarded as the criterion to determine the components that need removal. This method not only retains the useful information effectively but also removes the noise component. A significant improvement in the R2 of the differential absorption optical depth (DAOD) of the de-noised signals is obtained. The results of the simulated and observed analysis signal demonstrated improvement both in the SNR and in the retrieval precision.

New Denoising Method for Lidar Signal by the WT-VMD Joint Algorithm.

Light detection and ranging (LIDAR) is an active remote sensing system. Lidar echo signal is non-linear and non-stationary, which is often accompanied by various noises. In order to filter out the noise and extract valid signal information, a suitable method should be chosen for noise reduction. Some denoising methods are commonly used, such as the wavelet transform (WT), the empirical mode decomposition (EMD), the variational mode decomposition (VMD), and their improved algorithms. In this paper, a new denoising method named the WT-VMD joint algorithm based on the sparrow search algorithm (SSA), for lidar signal is selected by comparative experiment analysis. It is shown that this method is the most suitable one with the maximum signal-to-noise ratio (SNR), the minimum root-mean-square error (RMSE), and a relatively small indicator of smoothness when it is used in three kinds (50, 100, and 1000 pulses) of simulate lidar signals. The SNR is increased by 138.5%, 77.8% and 42.8% and the RMSE is decreased by 81.8%, 72.0% and 68.8% when being used to the three kinds of cumulative signal without pollution. Then, the SNR is increased by 83.3%, 60.4% and 24.0% and the RMSE is decreased by 70.8%, 66.0% and 50.5% when being used to the three kinds of cumulative signal with aerosol and clouds. The WT-VMD joint algorithm based on SSA is used in the denoising process for the actual lidar signal, showing extraordinary denoising effect and will improve the inversion accuracy of the lidar signal.

A Pulse Delay Control Method by External Trigger to Underwater Illumination

Aiming at the problem of high precision pulse synchronization control in digital signal processing, we proposed an external trigger synchronization control method by double-layer hierarchical delay. The method consists of hardware circuit and control algorithm. The hardware circuit employs the main control unit and the digital delay unit, in which the main control unit makes the coordination between the digital delay unit and the peripheral circuit, and the two-layer digital delay unit performs multiple fixed period delay operation according to the instruction of the main control unit. The control algorithm adopts the two-layer hierarchical control mode, decomposes the total delay time according to the two-layer delay precision, and performs the combined delay in the two-layer digital delay unit successively. The delay result achieves a fixed delay of less than 40 ns, a delay step of 0.25 ns and a delay range of less than 1000 ns. The method adopts the combination of hardware and software to realize the external trigger double-layer hierarchical delay synchronization control, which has the characteristics of high delay step and pulse width adjustment accuracy and small cumulative delay error. It is suitable for the application scenarios of lidar echo signal and ultra-wide spectrum power source synthesis.

Coherent Doppler wind lidar signal denoising adopting variational mode decomposition based on honey badger algorithm.

Coherent Doppler wind lidar (CDWL) is used to measure wind velocity distribution by using laser pulses. However, the echo signal is easily affected by atmospheric turbulence, which could decrease the effective detection range of CDWL. In this paper, a variation modal decomposition based on honey badger algorithm (VMD-HBA) is proposed and demonstrated. Compared with conventional VMD-based methods, the proposed method utilizes a newly developed HBA to obtain the optimal VMD parameters by iterating the spectrum fitness function. In addition, the Correlation Euclidean distance is applied to identify the relevant mode and used to reconstruct the signal. The simulation results show that the denoising performance of VMD-HBA is superior to other available denoising methods. Experimentally, this combined method was successfully realized to process the actual lidar echo signal. Under harsh detection conditions, the effective detection range of the homemade CDWL system is extended from 13.41 km to 20.61 km.

Frequency measurement and amplification of lidar echo signal based on optomechanical effects

In this work, we have proposed a new scheme to measure the frequency of lidar echo signals by optomechanically induced transparency or amplification. Our calculation is based on the quantum Langevin equations and the mean-field approximation. The phenomenon that the transmission probabilities of echo signal light of lidar are sensitive to its frequency gives us a method to obtain the probe frequency. The expressions of the external force that keeps the system on the blue or red sidebands are offered, which is the necessary condition for our measurements. For the blue sideband, the process leads to the amplification of the echo signal light if the frequency of the pump field satisfies certain relations. For the red sideband, the echo signal light may be fully absorbed and at this time whose frequency can be obtained by a formula that has been proved in this work. In contrast to the conventional method, our method may have higher accuracy, and cavity optomechanics can amplify the signal under the condition of the blue sideband. Our scheme is based on the quantum theory, which may be easier to associate with quantum Fisher information that can give the highest precision.

A Method of Inversing Dynamic Aerosol Extinction-to-Backscattering Ratio Based on Lidar Echo Signal and Ground Aerosol Extinction Coefficient or Aerosol Optical Depth

When using the Fernald method to solve the lidar equation, the selection of aerosol extinction-to-backscattering ratio (lidar ratio) is an important error source, which becomes a major factor limiting the accuracy of aerosol extinction profile. Therefore, we proposed a method for dynamic lidar ration inversion, named iteration inverse generation method (IIGM). This method is based on the Fernald method, takes the ground extinction coefficient as the loop iteration constraints, and is combined with the lidar signal to inverse the local dynamic lidar ratio. The local lidar ratio obtained by IIGM is in the range of 10–50 sr. The average relative error between the ground extinction coefficient inversed from dynamic lidar ratio and the measured ground extinction coefficient is 0.0041%. The relative error between the inverse aerosol optical depth (AOD) and the measured AOD is 20.38%. In addition to using the ground extinction coefficient as the iteration constraints of IIGM, AOD is introduced as another optional iterative constraint. The IIGM can achieve accurate inversion of lidar signals in different situations.

A Novel Fast Multiple-Scattering Approximate Model for Oceanographic Lidar

An effective lidar simulator is vital for its system design and processing algorithms. However, laser transmission is a complex process due to the effects of sea surface and various interactions in seawater such as absorption, scattering, and so on. It is sophisticated and difficult for multiple scattering to accurately simulate. In this study, a multiple-scattering lidar model based on multiple-forward-scattering-single-backscattering approximation for oceanic lidar was proposed. Compared with previous analytic models, this model can work without assuming a homogeneous water and fixed scattering phase function. Besides, it takes consideration of lidar system and environmental parameters including receiver field of view, different scattering phase functions, particulate sizes, stratified water, and rough sea surface. One should note that because the scattering phase function is difficult to determine accurately, the simulation accuracy may be reduced in a complex oceanic environment. The Cox–Munk model used in our method simulates capillarity waves but ignores gravity waves, and the pulse stretching is not included. The wide-angle scattering occurs in the dense subsurface phytoplankton, which sometimes makes it hard to use this model. In this study, we firstly derived this method based on an analytical solution by convolving Gaussians of the forward-scattering contribution of layer dr and the energy density at R in the small-angle-scattering approximation. Then, the effects of multiple scattering and water optical properties were analyzed using the model. Meanwhile, the validation with Monte Carlo model was implemented. Their coefficient of determination is beyond 0.9, the RMSE is within 0.02, the MAD is within 0.02, and the MAPD is within 8%, which indicates that our model is efficient for oceanographic lidar simulation. Finally, we studied the effects of FOV, SPF, rough sea surface, stratified water, and particle size. These results can provide reference for the design of the oceanic lidar system and contribute to the processing of lidar echo signals.

Overview of Underwater Transmission Characteristics of Oceanic LiDAR

Oceanic LiDAR (hereafter referred to as O-LiDAR) is an important remote sensing device for measuring the near-coastal water depth and for studying the optical properties of water bodies. With the commercialization of LiDAR, the theoretical research on the underwater transmission characteristics of LiDAR has been intensified worldwide. Primary research interests include the simulation and modeling of LiDAR underwater echo signals and the inversion of optical parameters using LiDAR water echo signals. This article provides an overview of the principle of LiDAR echo signal formation, and comprehensively summarizes the LiDAR echo signal simulation modeling methods and the corresponding factors that affect modeling accuracy by focusing on the characteristics of different methods. We found that the current simulation methods of LiDAR underwater transmission echo signals primarily include an analytical method based on the radiation transfer equation and a statistical method based on the Monte Carlo (MC) model. The radiation transport equation needs to be appropriately simplified using the analytical method, usually using the quasi-single-small-angle approximation principle. The analytical method has high calculation efficiency but its accuracy is dependent to the quasi-single small-angle approximation. The statistical method can analyze the influence of various factors on echo signals by controlling the variables, but it has poor calculation efficiency. Finally, the semianalytical MC model was used to quantitatively analyze the three main factors (LiDAR system parameters, water body optical parameters, and environmental parameters) affecting underwater LiDAR transmission characteristics, and summarizes the mechanism and results of different factors.

Shallow-Water LiDAR Echo Signal Denoising Based on Improved EWT

Shallow-Water LiDAR Echo Signal Denoising Based on Improved EWT

Comparison Analysis of Five Waveform Decomposition Algorithms for the Airborne LiDAR Echo Signal

The information from the components obtained by waveform decomposition is usually used to inverse topography, and classify tree species, etc. Many efforts on waveform decomposition algorithms have been presented, but they lack comparison analysis and evaluation. Thereby, this article compares and analyzes the performance of five waveform decomposition algorithms, which are Gaussian, Adaptive Gaussian, Weibull, Richardson–Lucy (RL), and Gold, under different topographic conditions such as forests, glaciers, lakes, and residential areas. The experimental results reveal that: first, the Gaussian algorithm causes the biggest fitting error at 9.96 mV in the forested area. It is easy to identify multiple dense peaks as single peaks. Second, there are many misjudged, superimposed, and overlapped waveform components separated by the Weibull algorithm. The Adaptive Gaussian is more capable of fitting complex waveforms but has 122 more outliers than the Weibull algorithm does. Third, the Gold and RL algorithms decompose the largest number of waveform components (272.2k and 265.9k) in the forested area; both RL and Gold algorithms can effectively improve the separability of peaks. Fourth, the RL algorithm is only more effective for the area with sparse vegetation than the Gold algorithm does, i.e., the Gold algorithm is capable of processing data with dense vegetation areas at a lowest false component detection rate of 1.3%, 0.9%, 1.1%, and 0.1% in four areas. Finally, the Gaussian and Gold algorithms have much faster decomposition speed at 1000/s and 2000/s than the other three algorithms do. These results are useful for selecting different algorithms under different environments.