Lidar Return Signals Research Articles

A novel lidar signal denoising method based on variational mode decomposition optimized using whale algorithm

Original lidar return signals are covered by high levels of noise that seriously affect the accuracy of subsequent data processing and inversion. Therefore, it is important to separate the effective signal from the returned signal with noise interference. In this paper, an efficient denoising method based on the variational mode decomposition (VMD) algorithm optimized using the global search strategy-based whale algorithm and the total variational stationary wavelet transform (GSWOA-VMD-SWTTV) is proposed, and this method is applied to denoising of lidar return signals. First, the global search strategy-based whale optimization algorithm (GSWOA) is used to acquire the optimal parameters of the VMD algorithm adaptively, and the lidar return signal is then decomposed by global search strategy-based whale optimization algorithm (GSWOA)-VMD. The effective modal components are then determined using the cross-correlation coefficient method from the decomposed modal components, and total variation stationary wavelet denoising is performed on each effective mode. Finally, the effective modes are reconstructed to obtain a clean lidar return signal. Moreover, to provide further verification of the effectiveness of the proposed method, it is compared with the ensemble empirical mode decomposition (EEMD) method, the complete EEMD with adaptive noise (CEEMDAN) method, the singular value decomposition (SVD) method, and the wavelet threshold method under sunny, cloudy, and dusty weather conditions. The experimental results demonstrate the superior noise reduction performance of the proposed algorithm, which can filter out strong noise from the signal while retaining the complete signal details without distortion; additionally, the proposed method has the highest signal-to-noise ratio and lowest mean square error.

A novel lidar signal noise reduction algorithm based on improved deep belief network

To reduce noise in the lidar return signal, an improved deep belief network (DBN) denoising algorithm is proposed in this study. In the traditional implementation process of DBN, a multi-layer fully connected network is realized by stacking restricted Boltzmann machines (RBMs). However, the RBM is an undirected graph model, and there is no clear causal relationship between random variable nodes. The denoising autoencoder (DAE) can avoid this problem and produce field generalization performance by adding random contamination during training and stacking, thereby achieving better performance than the traditional DBN. In this study, a new multi-layer DBN called DADBN is implemented by stacking DAE and RBM. First, the multi-layer DAE is placed in the beginning layer of the network as the primary filter of the signal to provide data dimensionality reduction and feature extraction. Then, the RBM is used as the lower layer, the hidden layer is calculated according to the initial value of the visible layer by the contrast of the divergence algorithm, and the visible layer is reconstructed from the samples of the hidden layer. And the sparse representation penalty is added to the RBM model to solve the assimilation phenomenon of hidden layer nodes in the RBM model. It not only optimizes the log-likelihood function when training data, but also makes the probability of each hidden layer node being activated tend to a minimum value, so as to sparsely activate the sparse hidden layer nodes. The weight matrix W is obtained by computing the results of the hidden layer twice. Finally, the original input signal was reconstructed by Gibbs sampling layer and the reconstructed signal was decoded by the decoder to achieve the purpose of noise reduction. To verify its effectiveness, this method is compared with four other denoising methods: wavelet packet algorithm, complete ensemble empirical modal decomposition (CEEMDAN), wavelet transform and empirical mode decomposition (WT-EMD), and wavelet transform-variational mode decomposition (WT-VMD). The noise reduction effect of DADBN is better than that of the other four methods, thus effectively eliminating complex noise in the lidar detection signal.

Performance of Wide Dynamic Photomultiplier Applied in a Low Blind Zone Lidar

Aerosol lidars play a vital role in the investigations of atmospheric pollution formation and meteorological processes. The intensity of lidar return signals in the near range changes much faster compared with the one in the far range, so extremely wide dynamic outputs from the photomultiplier tube (PMT) are needed to avoid saturation in the near range. Usually, to obtain the wide dynamic range, simultaneously, a transient digitizer (Licel) is applied to provide an analog detection chain for strong signals and a photon counting (PC) detection chain for weak signals. However, the near-range signals are still often saturated due to the very high aerosol loading. In this paper, we proposed to use a new PMT module with eight orders of magnitude for a low blind zone lidar, which can achieve both analog and PC separately. A comprehensive evaluation of this potential PMT, which could perform better in near-range detection, compared with the ordinary PMT was tested, but similar features are maintained in the far-range. The photon count rate and signal-to-noise ratio were tested for both the new PMT module and the ordinary PMT module. The results showed that the new PMT module is useful to extend the dynamic range of lidar detection.

An EEMD-SVD method based on gray wolf optimization algorithm for lidar signal noise reduction

ABSTRACT Atmospheric lidar is susceptible to light attenuation, sky background light and detector dark current during detection, which results in a lot of noise in the lidar return signal. In order to improve the SNR and extract useful signals, this paper proposes a new joint denoising method EEMD-GWO-SVD, which includes empirical mode decomposition (EEMD), grey wolf optimization (GWO) and singular value decomposition (SVD). Firstly, the grey wolf optimization algorithm was used to optimize two parameters of EEMD algorithm according to moderate values: the standard deviation Nstd of adding Gaussian white noise to the signal and the number NE of adding Gaussian white noise. Secondly, the mode components obtained by EEMD-GWO decomposition are screened and reconstructed according to the correlation coefficient method. Finally, the SVD algorithm with strong noise reduction ability was used to further remove the noise in the reconstructed signal, and the lidar return signal with high SNR was obtained. In order to verify the effectiveness of the proposed method, the proposed method was compared with empirical mode decomposition (EMD), complete ensemble empirical modal decomposition (CEEMDAN), wavelet packet decomposition and EEMD-SVD-lifting wavelet transform (EEMD-SVD-LWT). The results show that the noise reduction effect of the proposed method was better than that of the other four methods. This method can eliminate the complex noise in the lidar return signal while retaining all the details of the signal. In fact, the denoised signal is not distorted, the waveform is smooth, the far-field noise interference can be suppressed and the denoised signal is closer to the real signal with higher accuracy, which indicates the feasibility and practicability of the proposed method.

Novel Detection of Atmospheric Turbulence Profile Using Mie-Scattering Lidar Based on Non-Kolmogorov Turbulence Theory.

Turbulence can cause effects such as light intensity fluctuations and phase fluctuations when a laser is transmitted in the atmosphere, which has serious impacts on a number of optical engineering application effects and on climate improvement. Therefore, accurately obtaining real-time turbulence intensity information using lidar-active remote sensing technology is of great significance. In this paper, based on residual turbulent scintillation theory, a Mie-scattering lidar method was developed to detect atmospheric turbulence intensity. By extracting light intensity fluctuation information from a Mie-scattering lidar return signal, the atmospheric refractive index structure constant, Cn2, representing the atmospheric turbulence intensity, could be obtained. Specifically, the scintillation effect on the detection path was analyzed, and the probability density distribution of the light intensity of the Mie-scattering lidar return signal was studied. It was verified that the probability density of logarithmic light intensity basically follows a normal distribution under weak fluctuation conditions. The Cn2 profile based on Kolmogorov turbulence theory was retrieved using a layered, iterative method through the scintillation index. The method for detecting Kolmogorov turbulence intensity was applied to the detection of the non-Kolmogorov turbulence intensity. Through detection using the scintillation index, the corresponding C˜n2 profile could be calculated. The detection of the C˜n2 and Cn2 profiles were compared with the Hufnagel-Valley (HV) night model in the Yinchuan area. The results show that the detection results are consistent with the overall change trend of the model. In general, it is feasible to detect a non-Kolmogorov turbulence profile using Mie-scattering lidar.

CW coherent detection lidar for micro-Doppler sensing and raster-scan imaging of drones.

We demonstrate a continuous-wave (CW) coherent detection lidar (CDL) capable of detecting micro-Doppler (propeller) signatures and acquiring raster-scan images of small unmanned aerial systems/vehicles (UAS/UAV). The system uses a narrow-linewidth 1550 nm CW laser and takes advantage of mature and low-cost fiber-optics components from the telecommunications industry. Using either collimated or focused probe beam geometry, lidar based detection of characteristic periodic motions of drone propellers up to a remote distance of 500 m has been achieved. Furthermore, by raster scanning a focused CDL beam with a galvo-resonant mirror beamscanner, two-dimensional images of flying UAVs up to 70 m range were obtained. Each pixel of the raster-scan images provides both lidar return signal amplitude and target radial speed information. The raster-scan images obtained up to 5 frames per second make it possible to discriminate various UAV types based on their profile and even resolve the presence of payloads. With feasible improvements, the anti-drone lidar is a promising alternative to expensive EO/IR and active SWIR cameras used in counter-UAV systems.

A Novel Lidar Signal-Denoising Algorithm Based on Sparrow Search Algorithm for Optimal Variational Modal Decomposition

Atmospheric lidar is susceptible to the influence of light attenuation, sky background light, and detector dark currents during the detection process. This results in a large amount of noise in the lidar return signal. To reduce noise and extract a useful signal, a novel denoising method combined with variational modal decomposition (VMD), the sparrow search algorithm (SSA) and singular value decomposition (SVD) is proposed. The SSA is used to optimize the number of decomposition layers K and the quadratic penalty factor α values of the VMD algorithm. Some intrinsic mode function (IMF) components obtained from the VMD-SSA decomposition are grouped and reconstructed according to the interrelationship number selection criterion. Then, the reconstructed signal is further denoised by combining the strong noise-reduction ability of SVD to obtain a clean lidar return signal. To verify the effectiveness of the VMD-SSA-SVD method, the method is compared and analysed with wavelet packet decomposition, empirical modal decomposition (EMD), ensemble empirical modal decomposition (EEMD), and adaptive noise-complete ensemble empirical modal decomposition (CEEMD), and its noise-reduction effect is considerably improved over that of the other four methods. The method can eliminate the complex noise in the lidar return signal while retaining all the details of the signal. The signal is not distorted, the waveform is smoother, and far-field noise interference can be suppressed. The denoised signal is closer to the real signal with higher accuracy, which shows the feasibility and the practicality of the proposed method.

Novel Inversion Algorithm for the Atmospheric Aerosol Extinction Coefficient Based on an Improved Genetic Algorithm

As an important atmospheric component, aerosols play a very important role in the radiation budget balance of the earth–atmosphere system. To study the optical characteristics of aerosols, it is necessary to use an inversion algorithm to process the lidar return signal to obtain both the aerosol extinction coefficient and the backscattering coefficient. However, the lidar return power equation is ill-conditioned and contains two unknown parameters, meaning that traditional inversion algorithms must be solved by adopting certain assumptions (e.g., a uniform atmosphere and the lidar ratio), which to a certain extent can seriously affect the inversion accuracy. Here, to improve the accuracy of the aerosol extinction coefficient inversion, an inversion method based on an improved genetic algorithm is proposed. Using the U.S. Standard Atmosphere model and the return power equation, the aerosol extinction coefficient and the backscattering coefficient are independent variables that randomly provide initial values to simulate the theoretical lidar power. Then, the genetic algorithm is used to approximate the theoretical lidar power to the measured lidar return power with height; when the two are infinitely close, the values of the corresponding two independent variables (i.e., the extinction and backscattering coefficients) are inverted. Experiments performed to compare the different effects between a simple genetic algorithm and the improved genetic algorithm showed the proposed method capable of inverting the aerosol extinction coefficient without reliance on traditional inversion methods, representing a novel approach to the inversion of the aerosol extinction coefficient and the backscattering coefficient.

Characterizing the aerosol atmosphere above the Observatorio del Roque de los Muchachos by analysing seven years of data taken with an GaAsP HPD-readout, absolutely calibrated elastic LIDAR

ABSTRACT We present a new elastic LIDAR concept, based on a bi-axially mounted Nd:YAG laser and a telescope with HPD readout, combined with fast FADC signal digitization and offline pulse analysis. The LIDAR return signals have been extensively quality checked and absolutely calibrated. We analyse seven years of quasi-continuous LIDAR data taken during those nights when the MAGIC telescopes were operating. Characterization of the nocturnal ground layer yields zenith and azimuth angle dependent aerosol extinction scaleheights for clear nights. We derive aerosol transmission statistics for light emitted from various altitudes throughout the year and separated by seasons. We find further seasonal dependencies of cloud base and top altitudes, but none for the LIDAR ratios of clouds. Finally, the night sky background light is characterized using the LIDAR photon backgrounds.

Martian Atmospheric CO2 and Pressure Profiling With Differential Absorption Lidar: System Consideration and Simulation Results

AbstractThis study explores a new concept of Martian differential absorption Lidar operating at the 2‐μm CO2 absorption band for atmospheric CO2 and pressure observations. For the considered system, two or more closely spaced wavelengths are selected, so that environmental factors such as surface reflection, atmospheric scattering, and absorptions from other trace gases on the lidar return signals are very similar for all selected wavelengths, but the difference in CO2 absorption is substantial. Thus, Martian CO2 amount and air pressure could be retrieved from the measured CO2 differential absorption optical depth at the selected wavelengths. Simulations based on Mars's environmental conditions and technically feasible lidar systems show that lidar returns from the surface could have sufficient signal strengths that allow surface air pressure measurements with 1 Pa precision after a horizontally 5‐km averaging when dust optical depth is smaller than 1.5. Even when the dust optical depth increases to 3, the uncertainty in the air pressure and column CO2 amount measurements with a random error is still smaller than 1%. In the presence of moderate dust aerosol loads and with the optimal selection of offline and online wavelengths, atmospheric CO2 and pressure profiles could also be retrieved from the surface up to an ∼13‐km altitude with the random error smaller than 1% for a horizontal resolution of 100 km and a vertical resolution of 100 m during night or 400 m during day. These CO2 and pressure measurements would significantly improve weather and climate modeling and prediction.

A Novel Lidar Signal Denoising Method Based on Convolutional Autoencoding Deep Learning Neural Network

The lidar is susceptible to the dark current of the detector and the background light during the measuring process, which results in a significant amount of noise in the lidar return signal. To reduce noise, a novel denoising method based on the convolutional autoencoding deep-learning neural network is proposed. After the convolutional neural network was constructed to learn the deep features of lidar signal, the signal details were reconstructed by decoding part to obtain the denoised signal. To verify the feasibility of the proposed method, both the simulated signals and the actually measured signals by Mie-scattering lidar were denoised. Some comparisons with the wavelet threshold denoising method and the variational modal decomposition denoising method were performed. The results show the denoising effect of the proposed method was significantly better than the other two methods. The proposed method can eliminate complex noise in the lidar signal while retaining the complete details of the signal.

A Compact Rayleigh Autonomous Lidar (CORAL) for the middle atmosphere

Abstract. The Compact Rayleigh Autonomous Lidar (CORAL) is the first fully autonomous middle atmosphere lidar system to provide density and temperature profiles from 15 to approximately 90 km altitude. From October 2019 to October 2020, CORAL acquired temperature profiles on 243 out of the 365 nights (66 %) above Río Grande, southern Argentina, a cadence which is 3–8 times larger as compared to conventional human-operated lidars. The result is an unprecedented data set with measurements on 2 out of 3 nights on average and high temporal (20 min) and vertical (900 m) resolution. The first studies using CORAL data have shown, for example, the evolution of a strong atmospheric gravity wave event and its impact on the stratospheric circulation. We describe the instrument and its novel software which enables automatic and unattended observations over periods of more than a year. A frequency-doubled diode-pumped pulsed Nd:YAG laser is used as the light source, and backscattered photons are detected using three elastic channels (532 nm wavelength) and one Raman channel (608 nm wavelength). Automatic tracking of the laser beam is realized by the implementation of the conical scan (conscan) method. The CORAL software detects blue sky conditions and makes the decision to start the instrument based on local meteorological measurements, detection of stars in all-sky images, and analysis of European Center for Medium-range Weather Forecasts Integrated Forecasting System data. After the instrument is up and running, the strength of the lidar return signal is used as additional information to assess sky conditions. Safety features in the software allow for the operation of the lidar even in marginal weather, which is a prerequisite to achieving the very high observation cadence.

Hydrooptical Signals of an Airborne Polarization Lidar for Sensing of Homogeneous Sea Water Depth

The characteristic features of hydrooptical signals of an airborne polarization lidar are studied both theoretically and experimentally for sensing of a homogeneous water mass under controllable conditions. The results obtained can be used to expand the possibilities of interpretation of lidar return signals, especially in complex and ambiguous situations. Results of theoretical and experimental investigations of the depth profiles of hydrooptical signals of airborne polarization lidars obtained under conditions of homogeneous water depths are presented.

An EEMD-SVD-LWT algorithm for denoising a lidar signal

A segmentation singular value decomposition (SVD)-lifting wavelet transform (LWT) denoising algorithm based on ensemble empirical mode decomposition (EEMD) was proposed to better suppress noise in an atmospheric lidar return signal. The EEMD method is used to distinguish inherent modal functions (IMFs) of the noise and signal, and remove the IMF with noise as its main component. Moreover, the SVD-LWT method is adopted to remove the noise in the IMF component containing the signal and thus finely extract the signal. The simulated Bumps signal with different sequences of Gaussian white noise was denoised, and the denoising effect of the EEMD-SVD-LWT algorithm was compared with the effects of the wavelet soft threshold, EEMD (correlation coefficient), and EEMD (difference value) methods. Simulation shows that the denoising effect of the EEMD-SVD-LWT algorithm was best. The EEMD-SVD-LWT algorithm was also used to denoise practical lidar signals and was better than that achieved with the other methods.

Polarized lidar and ocean particles: insights from a mesoscale coccolithophore bloom.

Oceanographic lidar can provide remote estimates of the vertical distribution of suspended particles in natural waters, potentially revolutionizing our ability to characterize marine ecosystems and properly represent them in models of upper ocean biogeochemistry. However, lidar signals exhibit complex dependencies on water column inherent optical properties (IOPs) and instrument characteristics, which complicate efforts to derive meaningful biogeochemical properties from lidar return signals. In this study, we used a ship-based system to measure the lidar attenuation coefficient (α) and linear depolarization ratio (δ) across a variety of optically and biogeochemically distinct water masses, including turbid coastal waters, clear oligotrophic waters, and calcite rich waters associated with a mesoscale coccolithophore bloom. Sea surface IOPs were measured continuously while underway to characterize the response of α and δ to changes in particle abundance and composition. The magnitude of α was consistent with the diffuse attenuation coefficient (Kd), though the α versus Kd relationship was nonlinear. δ was positively related to the scattering optical depth and the calcite fraction of backscattering. A statistical fit to these data suggests that the polarized scattering properties of calcified particles are distinct and contribute to measurable differences in the lidar depolarization ratio. A better understanding of the polarized scattering properties of coccolithophores and other marine particles will further our ability to interpret polarized oceanographic lidar measurements and may lead to new techniques for measuring the material properties of marine particles remotely.

Iterative retrieval method for ocean attenuation profiles measured by airborne lidar.

Lidar remote sensing for ocean optical properties has been increasingly applied because of its ability to provide vertical structure information, which cannot be directly obtained by ocean color remote sensing. However, the application of this technology demands an inversion method to infer two quantities, i.e., attenuation and backscatter, from a single measurement. Here, a new iterative retrieval method is demonstrated to deduce the attenuation coefficient from ocean lidar return signals. One calculates the logarithmic backscatter-to-attenuation ratio k by an iterative solution based on a bio-optical model. Procedural examples of lidar-processing results-from raw data to attenuation-are presented, and the inversion results are compared with in situ measurements. The correlation coefficient R between the lidar-retrieval and in situ measurements is 0.8, and the root mean square error (RMSE) is 0.032. We then map the vertical structure of the lidar-retrieved attenuation along airborne lidar flight tracks and discuss the influences of k, the reference depth zm, and the reference value αm. Consequently, the reference value has little influence on the results for high-optical-thickness water, and k is the main error source in lidar return inversion. Primary results indicate that this method provides a more accurate k and improves the inversion accuracy of the lidar attenuation coefficient.

船载激光雷达测量水体光学参数的仿真模拟研究

As an important instrument for optical observation in the water, lidar is able to efficiently obtain vertical profiles of ocean optical properties. Based on the airborne lidar model of Gordon (1982) for measuring water optical property, the processes of shipborne lidar radiative transfer and distribution of underwater light field were investigated using Monte Carlo simulation. In particular, taking into consideration receiving FOV and telescope aperture, a shipborne oceanographic lidar simulator was developed. After lidar radiative transfer was equivalent to solar radiative transfer, the simulator was validated with commonly used HydroLight which showed good coincidence. On this basis, using simulated lidar return signal, the relationships between lidar extinction coefficient α and ocean optical properties were analyzed for different observation modes and typical water bodies. The results of shipborne lidar show that with narrow receiving field of view(FOV), lidar extinction coefficient α is close to beam attenuation coefficient c, and with large receiving FOV, lidar extinction coefficient α gradually approaches to diffuse attenuation coefficient of downwelling irradiance Kd. The extinction coefficient α measured with shipborne lidar approaches to Kd slower than the airborne situation. The results of stratified water show that the α values measured in lower layer water shift towards the α values in upper layer water. The results provide the further understanding for researching the relationship between parameters measured by oceanographic lidar and ocean optical properties.

Vertical distribution of aerosol mass concentration over Pearl River Delta observed by Lidar during autumn and winter

This study aims to analyze the vertical distribution of aerosol particles during autumn and winter by deriving the number density of aerosol particles from Mie Lidar return signals. Haze has always existed in regions with low humidity and stable weather. In such regions,particle vertical distribution characters are needed for transport mechanisms. As a widely used instrument, Mie Lidar surfer from the uncertainties caused by the Lidar ratio and boundary value assumption. However, Mie Lidar is the most powerful tool to observe the atmospheric vertical distribution because it performs well in day and night under all weather conditions in comparison with passive sensors.Moreover, Mie Lidar is easier to carry compared with Raman Lidar.Lidar radiation principles were studied to understand the Lidar equation and its solution. Several parameters were carefully chosen for the numerical solutions. Instead of using boundary value for approximation, the Lidar parameter k was directly calculated on the basis of Lidar-validated parameters to avoid uncertainties. Thus, aerosol particles backscatter and extinction coefficient were retrieved by solving the Lidar equation and integrated for Aerosol Optical Depth(AOD) results. A Cimel CE318 sun photometer and an ASD Field Spec3 were used to measure AOD as validation data. Lastly, the number density of aerosol particles was derived on the basis of Mie theory with the estimation of aerosol particle distribution, size, and refractive index of particles.According to the number density vertical profile, nearly no difference was observed between the number density vertical profile of aerosol particles during daytime and nighttime in one day at a low altitude. This result indicated that the sunlight effect is ignorable for aerosol particle distribution near ground. The retrieved AODs with the CE318 and ASD observations showed a root mean square error of approximately 0.0541 and 0.0100, demonstrating that the retrieved result is reliable. The comparison with traditional method showed that the proposed algorithm in this study improved the accuracy of the traditional Lidar equation solution. The near-real distribution results showed aerosol particle distribution characteristics near the ground. The number density of aerosol particles in Guangzhou was mainly distributed in low-altitude regions, and its relationship with height was approximately a negative exponential distribution. The retrieved results of the aerosol particle number density were similar to those from the previous years, indicating that the air pollution in Guangzhou has not worsened. However, potential environmental threats and air pollution problems still need to be governed.Several uncertainties, such as changes in air temperature and pressure, validation of Lidar instrument k, and overlap factor, that could cause deviation were identified. The overlap factor and molecular backscatter coefficient were carefully calculated using the theoretical method. Frequent updates on Lidar parameters will be requested from the Lidar company in the future. The theoretical and experimental methods were combined for the calculation of the overlap factor and molecular backscatter coefficient. Raman Lidar was used to measure the actual Lidar ratios and characteristics of molecular scatter.The aerosol particle number density-retrieved algorithm was based on Lidar radiation principles and revealed the particles’ spatial distribution characters. The vertical distribution characters remained unchanged for several days under stable weather in the autumn and winter seasons, and its relationship with height was approximately a negative exponential distribution;hence, air conditions can have huge effect on human health. Air pollution problem still needs to be governed.

Statistical Analysis of the Potential of a Bathymetric Lidar with Time-of-Flight Matrix Spad Photodetector

The Monte Carlo method is used to solve the nonstationary laser sensing equation for a multicomponent optically dense water medium taking into account the influence of the water-air interface and the contributions of multiple radiation scattering by sea water and of the signal reflection from the sea bottom. Dependences of the signal power recorded with a monostatic lidar equipped with a time-of-flight matrix single-photon avalanche diode (SPAD) photodetector on the water depth and surface microwaves for different field-of-view angles of the receiver are obtained. Results of investigations have shown that the maximum depth of sea bottom detection is 40–50 m given that the optical water thickness does not exceed 3.5–4 m. The dynamic range of lidar signals from water reaches 7–9 orders of magnitude for lidar sensing of sea bottom at limiting depths of 40–50 m in very transparent water in the presence of the Fresnel reflection from the water surface. Results of statistical simulation have shown that the lidar system with the matrix receiver intended for sea water sensing can be realized at the modern technical level. Under favorable conditions, the lidar return signal power provides sensing of sea bottom to depths of 40–50 m.

PROCESSING JUMP POINT OF LIDAR DETECTION DATA AND INVERSING THE AEROSOL EXTINCTION COEFFICIENT

Abstract. For a long time, the research of the optical properties of atmospheric aerosols has aroused a wide concern in the field of atmospheric and environmental. Many scholars commonly use the Klett method to invert the lidar return signal of Mie scattering. However, there are always some negative values in the detection data of lidar, which have no actual meaning，and which are jump points. The jump points are also called wild value points and abnormal points. The jump points are refered to the detecting points that are significantly different from the surrounding detection points, and which are not consistent with the actual situation. As a result, when the far end point is selected as the boundary value, the inversion error is too large to successfully invert the extinction coefficient profile. These negative points are jump points, which must be removed in the inversion process. In order to solve the problem, a method of processing jump points of detection data of lidar and the inversion method of aerosol extinction coefficient is proposed in this paper. In this method, when there are few jump points, the linear interpolation method is used to process the jump points. When the number of continuous jump points is large, the function fitting method is used to process the jump points. The feasibility and reliability of this method are verified by using actual lidar data. The results show that the extinction coefficient profile can be successfully inverted when different remote boundary values are chosen. The extinction coefficient profile inverted by this method is more continuous and smoother. The effective detection range of lidar is greatly increased using this method. The extinction coefficient profile is more realistic. The extinction coefficient profile inverted by this method is more favorable to further analysis of the properties of atmospheric aerosol. Therefore, this method has great practical application and popularization value.