Lidar Simulator Research Articles

An LES-based airborne Doppler lidar simulator and its application to wind profiling in inhomogeneous flow conditions

Abstract. Wind profiling by Doppler lidar is common practice and highly useful in a wide range of applications. Airborne Doppler lidar can provide additional insights relative to ground-based systems by allowing for spatially distributed and targeted measurements. Providing a link between theory and measurement, a first large eddy simulation (LES)-based airborne Doppler lidar simulator (ADLS) has been developed. Simulated measurements are conducted based on LES wind fields, considering the coordinate and geometric transformations applicable to real-world measurements. The ADLS provides added value as the input truth used to create the measurements is known exactly, which is nearly impossible in real-world situations. Thus, valuable insight can be gained into measurement system characteristics as well as retrieval strategies. As an example application, airborne Doppler lidar wind profiling is investigated using the ADLS. For commonly used airborne velocity azimuth display (AVAD) techniques, flow homogeneity is assumed throughout the retrieval volume, a condition which is violated in turbulent boundary layer flow. Assuming an ideal measurement system, the ADLS allows to isolate and evaluate the error in wind profiling which occurs due to the violation of the flow homogeneity assumption. Overall, the ADLS demonstrates that wind profiling is possible in turbulent wind field conditions with reasonable errors (root mean squared error of 0.36 m s−1 for wind speed when using a commonly used system setup and retrieval strategy for the conditions investigated). Nevertheless, flow inhomogeneity, e.g., due to boundary layer turbulence, can cause an important contribution to wind profiling error and is non-negligible. Results suggest that airborne Doppler lidar wind profiling at low wind speeds (<5ms-1) can be biased, if conducted in regions of inhomogeneous flow conditions.

Method and Applications of Lidar Modeling for Virtual Testing of Intelligent Vehicles

With the common use of lidar sensors on intelligent vehicles, the simulation of lidar in autonomous driving is necessary. This study proposes an innovative lidar modeling method, and introduces solutions for the simulation of the lidar detection function and its physical mechanism. The model consists of geometric and physical models, and can simulate both point clouds and targets. The geometric model addresses the spatial relationship between lidar and the environment. The physical model describes how the lidar detection mechanism may influence detection results. Signal attenuation and unwanted raw data caused by raindrops are the main consideration in the physical model. Characteristics of lidar signal attenuation in different weather conditions are modeled, and a simplified lidar equation is derived for use by lidar users, as opposed to designers. Unwanted raw data are simulated in a stochastic model employing the Monte Carlo method, where raindrop size and distance are sampled. The model is calibrated and validated with real lidar data. The application of the proposed lidar model for an autonomous emergency braking system is introduced.

Optimized profile retrievals of aerosol microphysical properties from simulated spaceborne multiwavelength Lidar

This work is an expanded study of one previously published on retrievals of aerosol microphysical properties from space-borne multiwavelength lidar measurements. The earlier studies and this one were done in the framework of the NASA Aerosol-Clouds-Ecosystems (now the Aerosol Clouds Convection and Precipitation) NASA mission. The focus here is on the capabilities of a simulated spaceborne multiwavelength lidar system for retrieving aerosol complex refractive index (m = mr + imi) and spectral single scattering albedo (SSA(λ)), although other bulk parameters such as effective (reff) radius and particle volume (V) and surface (S) concentrations are also studied. The novelty presented here is the use of recently published, case-dependent optimized-constraints on the microphysical retrievals using three backscattering coefficients (β) at 355, 532 and 1064 nm and two extinction coefficients (α) at 355 and 532 nm, typically known as the stand-alone 3β + 2α lidar inversion. Case-dependent optimized-constraints (CDOC) limit the ranges of refractive index, both real (mr) and imaginary (mi) parts, and of radii that are permitted in the retrievals. Such constraints are selected directly from the 3β + 2α measurements through an analysis of the relationship between spectral dependence of aerosol extinction-to-backscatter ratios (LR) and the Ångström exponent of extinction. The analyses presented here for different sets of size distributions and refractive indices reveal that the direct determination of CDOC are only feasible for cases where the uncertainties in the input optical data are less than 15%. For the same simulated spaceborne system and yield than in Whiteman et al., (2018), we demonstrated that the use of CDOC as essential for the retrievals of refractive index and also largely improved retrieval of bulk parameters. A discussion of the global representativeness of CDOC is presented using simulated lidar data from a 24 h satellite track using GEOS model output to initialize the lidar simulator. We found that CDOC are representative of many aerosol mixtures in spite of some outliers (e.g. highly hydrated particles) associated with the assumptions of bimodal size distributions and of the same refractive index for fine and coarse modes. Moreover, sensitivity tests performed using synthetic data reveal that retrievals of imaginary refractive index (mi) and SSA are extremely sensitive to β(355).

Augmented LiDAR Simulator for Autonomous Driving

In Autonomous Driving (AD), detection and tracking of obstacles on the roads is a critical task. Deep-learning based methods using annotated LiDAR data have been the most widely adopted approach for this. Unfortunately, annotating 3D point cloud is a very challenging, time- and money-consuming task. In this paper, we propose a novel LiDAR simulator that augments real point cloud with synthetic obstacles (e.g., cars, pedestrians, and other movable objects). Unlike previous simulators that entirely rely on CG models and game engines, our augmented simulator bypasses the requirement to create high-fidelity background CAD models. Instead, we can simply deploy a vehicle with a LiDAR scanner to sweep the street of interests to obtain the background point cloud, based on which annotated point cloud can be automatically generated. This unique "scan-and-simulate" capability makes our approach scalable and practical, ready for large-scale industrial applications. In this paper, we describe our simulator in detail, in particular the placement of obstacles that is critical for performance enhancement. We show that detectors with our simulated LiDAR point cloud alone can perform comparably (within two percentage points) with these trained with real data. Mixing real and simulated data can achieve over 95% accuracy.

A voxel matching method for effective leaf area index estimation in temperate deciduous forests from leaf-on and leaf-off airborne LiDAR data

The quantification of leaf area index (LAI) is essential for modeling the interaction between atmosphere and biosphere. The airborne LiDAR has emerged as an effective tool for mapping plant area index (PAI) in a landscape consisting of both woody and leaf materials. However, the discrimination between woody and leaf materials and the estimation of effective LAI (eLAI) have, to date, rarely been studied at landscape scale. We applied a voxel matching algorithm to estimate eLAI of deciduous forests using simulated and field LiDAR data under leaf-on and leaf-off conditions. We classified LiDAR points as either a leaf or a woody hit on leaf-on LiDAR data by matching the point with leaf-off data. We compared the eLAI result of our voxel matching algorithm against the subtraction method, where the leaf-off effective woody area index (eWAI) is subtracted from the effective leaf-on PAI (ePAI). Our results, which were validated against terrestrial LiDAR derived eLAI, showed that the voxel matching method, with an optimal voxel size of 0.1 m, produced an unbiased estimation of terrestrial LiDAR derived eLAI with an R2 of 0.70 and an RMSE of 0.41 (RRMSE: 20.1%). The subtraction method, however, yielded an R2 of 0.62 and an RMSE of 1.02 (RRMSE: 50.1%) with a significant underestimation of 0.94. Reassuringly, the same outcome was observed using a simulated dataset. In addition, we evaluated the performance of 96 LiDAR metrics under leaf-on conditions for eLAI prediction using a statistical model. Based on the importance scores derived from the random forest regression, nine of the 96 leaf-on LiDAR metrics were selected. Cross-validation showed that eLAI could be predicted using these metrics under leaf-on conditions with an R2 of 0.73 and an RMSE of 0.27 (RRMSE: 17.4%). The voxel matching method yielded a slightly lower accuracy (R2: 0.70, RMSE:0.41, RRMSE: 20.1%) than the statistical model. We, therefore, suggest that the voxel matching method offers a new opportunity for the estimating eLAI and other ecological applications that require the classification between leaf and woody materials using airborne LiDAR data. It potentially allows transferability to different sites and flight campaigns.

Lidar assisted wake redirection in wind farms: A data driven approach

Lidar based wind measurement is an integral part of wind farm control. The major issues and challenges in power maximization include the potential losses due to wake effect observed among wind turbines. This manuscript presents a wake management technique that utilizes lidar simulations for wake redirection. The proposed methodology is validated for 2-turbine and 15-turbine wind farm layouts involving a PI control based yaw angle correction. Yaw angle misalignment using wake center tracking of the upstream turbines is used to increase the power generation levels. Results of wake center estimation are compared with a Kalman filter based method. Further, the velocity deficit and overall farm power improvement by yaw angle correction is calculated. Results reveal a 1.7% and 0.675% increase in total wind farm power for two different wind speed cases.

海洋激光雷达实验与仿真结果的对比

海洋激光雷达实验与仿真结果的对比

A New Airborne Doppler Lidar for Boundary Layer Research & LES-Based Simulation

Airborne Doppler lidar measurements are a valuable tool in meteorological research. A new airborne Doppler lidar system has been developed jointly by the Karlsruhe Institute of Technology (KIT) and the Technical University of Brunswick. The system is designed for use aboard a medium-range turboprop aircraft, thereby targeting meso-scale flow phenomena. In order to optimize system setup and retrieval strategy, as well as evaluate measurement quality under turbulent conditions, a first LES-based airborne Doppler lidar simulator has been developed. The results provide detailed insight into airborne Doppler lidar wind profiling characteristics.

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.

采用半解析蒙特卡洛技术模拟星载海洋激光雷达回波信号的软件

In this paper, a spaceborne oceanic lidar simulation system used semianalytic Monte Carlo method was developed. The system can simulate the lidar returns of the atmosphere and the ocean with different optical properties through entering the parameters of the lidar system and the environmental parameters. At the same time, a user-friendly software interface for users was designed to operate input parameters and observe the output results intuitively. A variety of simulations was done, such as different types of water and different scattering phase functions. The simulation results were highly consistent with the theoretical lidar equations. The system was important to the research on the detection mechanism of spaceborne oceanic lidars.

Autonomous Landing Control of Highly Flexible Aircraft Based on Lidar Preview in the Presence of Wind Turbulence

This paper investigates preview-based autonomous landing control of a highly flexible flying wing model using short-range light detection and ranging (Lidar) wind measurements in the presence of wind turbulence. The preview control system is developed based on a reduced-order linear aeroelastic model and employs a two-loop control scheme. The outer loop employs the linear active disturbance rejection control and PI algorithms to track the reference landing trajectory and vertical speed, respectively, and to generate the attitude angle command. This is then used by the inner loop using H $_{\infty }$ preview control to compute the control inputs to the actuators (control flaps and thrust). A landing trajectory navigation system is designed to generate real-time reference commands for the landing control system. A Lidar simulator is developed to measure the wind disturbances at a distance in front of the aircraft, which is provided to the inner-loop H $_{\infty }$ preview controller as prior knowledge to improve control performance. Simulation results based on the full-order nonlinear flexible aircraft dynamic model show that the preview-based landing control system is able to land the flying wing effectively and safely, showing better control performance than the baseline landing control system (without preview) with respect to landing effectiveness and disturbance rejection. The control system's robustness to measurement error in the Lidar system is also demonstrated.

Airborne Waveform Lidar Simulator Using the Radiative Transfer of a Laser Pulse

An airborne lidar simulator creates a lidar point cloud from a simulated lidar system, flight parameters, and the terrain digital elevation model (DEM). At the basic level, the lidar simulator computes the range from a lidar system to the surface of a terrain using the geomatics lidar equation. The simple computation effectively assumes that the beam divergence is zero. If the beam spot is meaningfully large due to the large beam divergence combined with high sensor altitude, then the beam plane with a finite size interacts with a ground target in a realistic and complex manner. The irradiance distribution of a delta-pulse beam plane is defined based on laser pulse radiative transfer. The airborne lidar simulator in this research simulates the interaction between the delta-pulse and a three-dimensional (3D) object and results in a waveform. The waveform will be convoluted using a system response function. The lidar simulator also computes the total propagated uncertainty (TPU). All sources of the uncertainties associated with the position of the lidar point and the detailed geomatics equations to compute TPU are described. The boresighting error analysis and the 3D accuracy assessment are provided as examples of the application using the simulator.

Learning motion field of LiDAR point cloud with convolutional networks

This paper proposes a novel method for motion field estimation in two consecutive LiDAR scans with convolutional neural networks (CNNs) wherein object detection, point-wise motion, and object-level motion are learned hierarchically. In the input stage, a unique mapping model serves to describe the LiDAR point cloud and its motion. In the encode network, two input maps are subjected to spatial compression and merged into a correlation layer. In the decode network, the model outputs data encompassing object detection, point-wise motion, and object-level motion. Since existing ground truth datasets are not sufficiently large to train a CNN, we generate a synthetic Vehicle Motion dataset for training. The set contains scenes with several simultaneously moving 3D vehicles recorded by a simulated LiDAR. An experiment conducted on both real and synthetic datasets shows that the proposed method outperforms other state-of-the-art motion field estimation methods.

A Stochastic Physical Simulation Framework to Quantify the Effect of Rainfall on Automotive Lidar

<div class="section abstract"><div class="htmlview paragraph">The performance of environment perceiving sensors such as e.g. lidar, radar, camera and ultrasonic sensors is safety critical for automated driving vehicles. Therefore, one has to assess the sensors’ performance to assure the automated driving system’s safety. The performance of these sensors is however to some degree sensitive towards adverse weather conditions. A challenge is to quantify the effect of adverse weather conditions on the sensor’s performance early in the development of an automated driving system. This challenge is addressed in this work for lidar sensors. The lidar equation was previously employed in this context to derive estimates of a lidar’s maximum range in different weather conditions. In this work, we present a stochastic simulation framework based on a probabilistic extension of the lidar equation, to quantify the effect of adverse rainfall conditions on a lidar’s raw detection performance. To this end, we combine basic probabilistic models for key rainfall parameters with Mie theory and the theory of signal detection in a Monte Carlo simulation framework. This allows to analyze and optimize a sensor’s design early in the sensor development, when physical testing is not yet possible. A challenge not addressed in this work is to include the effect of road spray water on the lidar’s performance. Combining the effect of other noise sources with the presented framework in a ray tracer is an opportunity for realistic physical lidar simulations and would allow to virtually estimate the performance of a lidar’s object detection and tracking performance. Such simulations could contribute to verify the safety of automated driving functionalities.</div></div>

The GEDI Simulator: A Large-Footprint Waveform Lidar Simulator for Calibration and Validation of Spaceborne Missions.

NASA's Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne lidar mission which will produce near global (51.6°S to 51.6°N) maps of forest structure and above‐ground biomass density during its 2‐year mission. GEDI uses a waveform simulator for calibration of algorithms and assessing mission accuracy. This paper implements a waveform simulator, using the method proposed in Blair and Hofton (1999; https://doi.org/10.1029/1999GL010484), and builds upon that work by adding instrument noise and by validating simulated waveforms across a range of forest types, airborne laser scanning (ALS) instruments, and survey configurations. The simulator was validated by comparing waveform metrics derived from simulated waveforms against those derived from observed large‐footprint, full‐waveform lidar data from NASA's airborne Land, Vegetation, and Ice Sensor (LVIS). The simulator was found to produce waveform metrics with a mean bias of less than 0.22 m and a root‐mean‐square error of less than 5.7 m, as long as the ALS data had sufficient pulse density. The minimum pulse density required depended upon the instrument. Measurement errors due to instrument noise predicted by the simulator were within 1.5 m of those from observed waveforms and 70–85% of variance in measurement error was explained. Changing the ALS survey configuration had no significant impact on simulated metrics, suggesting that the ALS pulse density is a sufficient metric of simulator accuracy across the range of conditions and instruments tested. These results give confidence in the use of the simulator for the pre‐launch calibration and performance assessment of the GEDI mission.

A Simulation Method for LIDAR of Autonomous Cars

Vehicle-mounted LIDAR simulation module is an important part of autonomous car simulation test platform. According to the working principle of LIDAR, its working process and interface can be simulated in the digital simulation environment. The environment includes LIDAR emulation module, high precision 3D scene model and data interface module. The LIDAR simulation module uses GPU(Graphic Processing Unit)to calculate the depth of high-precision 3D scene and output the results through data interface. On the PC workstation, 64 lines vehicle-mounted LIDAR can be implemented for real-time simulation of large-scale road scene.

Cloud Response to Arctic Sea Ice Loss and Implications for Future Feedback in the CESM1 Climate Model

AbstractOver the next century, the Arctic is projected to become seasonally sea ice‐free. Assessing feedback between clouds and sea ice as the Arctic loses sea ice cover is important because of clouds' radiative impacts on the Arctic surface. Here we investigate present‐day and future Arctic cloud‐sea ice relationships in a fully coupled global climate model forced by business‐as‐usual increases in greenhouse gases. Model evaluation using a lidar simulator and lidar satellite observations shows agreement between present‐day modeled and observed cloud‐sea ice relationships. Summer clouds are unaffected by sea ice variability, but more fall clouds occur over open water than over sea ice. Because the model reproduces observed cloud‐sea ice relationships and their underlying physical mechanisms, the model is used to assess future Arctic cloud‐sea ice feedback. With future sea ice loss, modeled summer cloud fraction, vertical structure, and optical depth barely change. Future sea ice loss does not influence summer clouds, but summer sea ice loss does drive fall cloud changes by increasing the amount of sunlight absorbed by the summertime ocean and the latent and sensible heat released into the atmosphere when the Sun sets in fall. The future fall boundary layer deepens and clouds become more opaque over newly open water. The future nonsummer longwave cloud radiative effect strengthens as nonsummer cloud cover increases. In summary, we find no evidence for a summer cloud‐sea ice feedback but strong evidence for a positive cloud‐sea ice feedback that emerges during nonsummer months as the Arctic warms and sea ice disappears.

How Well Are Clouds Simulated over Greenland in Climate Models? Consequences for the Surface Cloud Radiative Effect over the Ice Sheet

AbstractUsing lidar and radiative flux observations from space and ground, and a lidar simulator, we evaluate clouds simulated by climate models over the Greenland ice sheet, including predicted cloud cover, cloud fraction profile, cloud opacity, and surface cloud radiative effects. The representation of clouds over Greenland is a central concern for the models because clouds impact ice sheet surface melt. We find that over Greenland, most of the models have insufficient cloud cover during summer. In addition, all models create too few nonopaque, liquid-containing clouds optically thin enough to let direct solar radiation reach the surface (−1% to −3.5% at the ground level). Some models create too few opaque clouds. In most climate models, the cloud properties biases identified over all Greenland also apply at Summit, Greenland, proving the value of the ground observatory in model evaluation. At Summit, climate models underestimate cloud radiative effect (CRE) at the surface, especially in summer. The primary driver of the summer CRE biases compared to observations is the underestimation of the cloud cover in summer (−46% to −21%), which leads to an underestimated longwave radiative warming effect (CRELW= −35.7 to −13.6 W m−2compared to the ground observations) and an underestimated shortwave cooling effect (CRESW= +1.5 to +10.5 W m−2compared to the ground observations). Overall, the simulated clouds do not radiatively warm the surface as much as observed.

Space lidar observations constrain longwave cloud feedback

Some of the most challenging questions in atmospheric science relate to how clouds will respond as the climate warms. On centennial scales, the response of clouds could either weaken or enhance the warming due to greenhouse gas emissions. Here we use space lidar observations to quantify changes in cloud altitude, cover, and opacity over the oceans between 2008 and 2014, together with a climate model with a lidar simulator to also simulate these changes in the present-day climate and in a future, warmer climate. We find that the longwave cloud altitude feedback, found to be robustly positive in simulations since the early climate models and backed up by physical explanations, is not the dominant longwave feedback term in the observations, although it is in the model we have used. These results suggest that the enhanced longwave warming due to clouds might be overestimated in climate models. These results highlight the importance of developing a long-term active sensor satellite record to reduce uncertainties in cloud feedbacks and prediction of future climate.

Enabling Off-Road Autonomous Navigation-Simulation of LIDAR in Dense Vegetation

Machine learning techniques have accelerated the development of autonomous navigation algorithms in recent years, especially algorithms for on-road autonomous navigation. However, off-road navigation in unstructured environments continues to challenge autonomous ground vehicles. Many off-road navigation systems rely on LIDAR to sense and classify the environment, but LIDAR sensors often fail to distinguish navigable vegetation from non-navigable solid obstacles. While other areas of autonomy have benefited from the use of simulation, there has not been a real-time LIDAR simulator that accounted for LIDAR–vegetation interaction. In this work, we outline the development of a real-time, physics-based LIDAR simulator for densely vegetated environments that can be used in the development of LIDAR processing algorithms for off-road autonomous navigation. We present a multi-step qualitative validation of the simulator, which includes the development of an improved statistical model for the range distribution of LIDAR returns in grass. As a demonstration of the simulator’s capability, we show an example of the simulator being used to evaluate autonomous navigation through vegetation. The results demonstrate the potential for using the simulation in the development and testing of algorithms for autonomous off-road navigation.