A Method of Range Walk Error Correction in SiPM LiDAR with Photon Threshold Detection

A silicon photomultiplier (SiPM) LiDAR with photon threshold detection can achieve high dynamic performance. However, the number fluctuations of echo signal photons lead to the range walk error (RWE) in SiPM LIDARs. This paper derives the RWE model of SiPM LiDAR by using the LiDAR equation and statistical property of SiPM’s response. Based on the LiDAR system parameters and the echo signal intensity, which is obtained through the SiPM’s photon-number-resolving capability, the RWE is calculated through the proposed model. After that, we carry out experiments to verify its effectiveness. The result shows that the method reduces the RWE in TOF measurements using photon threshold detection from 36.57 cm to the mean deviation of 1.95 cm, with the number of detected photons fluctuating from 1.3 to 46.5.

Лидарная система комбинационного рассеяния света для зондирования молекул водорода в атмосфере

Computer simulation of the Raman lidar equation for measurement of the hydrogen molecules at the concentration level of 1013 cm-3 and higher in atmosphere at the ranging distances up to 100 m in the synchronous photon counting mode and selection of such a lidar optimal parameters have been fulfilled. It is shown that for hydrogen molecules concentration of N(z)=1013 cm-3 measurement at the distances from 5 to 100 m the measurement time t is in the range from 3.83 s to 26.5 min, for measurement of concentration N(z) = 1015 cm-3 - from 38 ms to 15.9 s and for the concentration measurement of N(z) = 1017 cm-3 - already from 0.4 ms to 160 ms, respectively.

Fog Measurements with IR Whole Sky Imager and Doppler Lidar, Combined with In Situ Instruments

This study describes comprehensive measurements performed for four consecutive nights during a regional-scale radiation fog event in Israel’s central and southern areas in January 2021. Our data included both in situ measurements of droplets size distribution, visibility range, and meteorological parameters and remote sensing with a thermal IR Whole Sky Imager and a Doppler Lidar. This work is the first extensive field campaign aimed to characterize fog properties in Israel and is a pioneer endeavor that encompasses simultaneous remote sensing measurements and analysis of a fog event with a thermal IR Whole Sky Imager. Radiation fog, as monitored by the sensor’s field of view, reveals three distinctive properties that make it possible to identify it. First, it exhibits an azimuthal symmetrical shape during the buildup phase. Second, the zenith brightness temperature is very close to the ground-level air temperature. Lastly, the rate of increase in cloud cover up to a completely overcast sky is very fast. Additionally, we validated the use of a Doppler Lidar as a tool for monitoring fog by proving that the measured backscatter-attenuation vertical profile agrees with the calculation of the Lidar equation fed with data measured by in situ instruments. It is shown that fog can be monitored by those two, off-the-shelf-stand-off-sensing technologies that were not originally designed for fog purposes. It enables the monitoring of fog properties such as type, evolution with time and vertical depth, and opens the path for future works of studying the different types of fog events.

Sensing with Polarized LIDAR in Degraded Visibility Conditions Due to Fog and Low Clouds

LIDAR (Light Detection and Ranging) sensors are one of the leading technologies that are widely considered for autonomous navigation. However, foggy and cloudy conditions might pose a serious problem for a wide adoption of their use. Polarization is a well-known mechanism often applied to improve sensors’ performance in a dense atmosphere, but is still not commonly applied, to the best of our knowledge, in self-navigated devices. This article explores this issue, both theoretically and experimentally, and focuses on the dependence of the expected performance on the atmospheric interference type. We introduce a model which combines the well-known LIDAR equation with Stocks vectors and the Mueller matrix formulations in order to assess the magnitudes of the true target signal loss as well as the excess signal that arises from the scattering medium radiance, by considering the polarization state of the E–M (Electro-Magnetic) waves. Our analysis shows that using the polarization state may recover some of the poor performance of such systems for autonomous platforms in low visibility conditions, but it depends on the atmospheric medium type. This conclusion is supported by measurements held inside an aerosol chamber within a well-controlled and monitored artificial degraded visibility atmospheric environment. The presented analysis tool can be used for the optimization of design and trade-off analysis of LIDAR systems, which allow us to achieve the best performance for self-navigation in all weather conditions.

Зондирование молекул метанола в атмосфере по спектрам комбинационного рассеяния света

The Raman back scattering lidar equation for the methanol molecules numerical solution results at the methanol molecules remote sensing in the atmosphere with concentrations in the range 1012 cm-3... 1018 cm-3 at the range distances up to 1000 meters in the synchronous photon counting mode are presented. It is shown that during the measurement time of 1 s with such a Raman lidar at the 405 nm laser wavelength, it is possible to probe the methanol molecules with a concentration level of about 1016 cm-3 at distances up to 600 m.

Interpretation of weak backscattering signals in a weakly clouded atmosphere

It is used here the lidar equation describing signals from a weakly turbid atmosphere to solve the problem of the determination of the atmospheric aerosol parameters. It is worthwhile to note that the backscattering and extinction coefficients are constant along the beam path in this case. First approximation of the exponent process can be used to describe the atmospheric extinction. The weak lidar signals were analyzed here. It is useful for calculations of the extinction coefficient the preliminary known value of this parameter. The systematic errors were analyzed for different points along the beam path. The signal power was measured at sufficiently large distance. The systematic errors of the extinction coefficient can exceed the systematic errors of the backscattering signal power. It was shown that corresponding value achieve 20. There was investigated the influence of the systematic errors of the measured signal including background light on the obtained results. It was shown that the obtained results cannot be accurate enough if we use preliminary obtained data found before the measurement. It is found that the relative error of the measured signal ˂1%. It is very important the relative error of the corresponding extinction coefficient can be ˃ 100%. There were investigated the results of measurements and the results of computations. First of all it is associated with the scattered irradiance. The cases were considered with absence and presence of water in the aerosol particles coating. It was shown that the developed models adequately describe the process of scattering by a particle. So it is possible significantly reduce the aerosol sizing error. This model can be applied in determining the pollution of the Arctic air basin.

A Semianalytic Monte Carlo Simulator for Spaceborne Oceanic Lidar: Framework and Preliminary Results

Spaceborne lidar (light detection and ranging) is a very promising tool for the optical properties of global atmosphere and ocean detection. Although some studies have shown spaceborne lidar’s potential in ocean application, there is no spaceborne lidar specifically designed for ocean studies at present. In order to investigate the detection mechanism of the spaceborne lidar and analyze its detection performance, a spaceborne oceanic lidar simulator is established based on the semianalytic Monte Carlo (MC) method. The basic principle, the main framework, and the preliminary results of the simulator are presented. The whole process of the laser emitting, transmitting, and receiving is executed by the simulator with specific atmosphere–ocean optical properties and lidar system parameters. It is the first spaceborne oceanic lidar simulator for both atmosphere and ocean. The abilities of this simulator to characterize the effect of multiple scattering on the lidar signals of different aerosols, clouds, and seawaters with different scattering phase functions are presented. Some of the results of this simulator are verified by the lidar equation. It is confirmed that the simulator is beneficial to study the principle of spaceborne oceanic lidar and it can help develop a high-precision retrieval algorithm for the inherent optical properties (IOPs) of seawater.

The Nonlinear Effective Attenuation Coefficient of Spaceborne Oceanic Lidar Signal

Multiple scattering is an inevitable effect in spaceborne oceanic lidar because of the large footprint size and the high optical density of seawater. The effective attenuation coefficient klidar in oceanic lidar equation, which indicates the influence of the multiple scattering effect on the formation of lidar returns, is an important parameter in the retrieval of inherent optical properties (IOPs) of seawater. In this paper, the nonlinearity of klidar and the relationships between klidar and the IOPs of seawater are investigated by solving the radiative transfer equation with an improved semianalytic Monte Carlo model. klidar is found to decrease exponentially with the increase of depth in homogeneous waters. klidar is given as an exponential function of depth and IOPs of seawater. The results in this paper can help to have a better understanding of the multiple scattering effect of spaceborne lidar and improve the retrieval accuracy of the IOPs of seawater using spaceborne lidar.

RESEARCH ON INVERSION OF LIDAR EQUATION BASED ON NEURAL NETWORK

Lidar is an advanced atmospheric and meteorological monitoring instrument. The atmospheric aerosol physical parameters can be acquired through inversion of lidar signals. However, traditional methods of solving lidar equations require many assumptions and cannot get accurate analytical solutions. In order to solve this problem, a method of inverting lidar equation using artificial neural network is proposed. This method is based on BP (Back Propagation) artificial neural network, the weights and thresholds of BP artificial neural network is optimized by Genetic Algorithm. The lidar equation inversion prediction model is established. The actual lidar detection signals are inversed using this method, and the results are compared with the traditional method. The result shows that the extinction coefficient and backscattering coefficient inverted by the GA-based BP neural network model are accurate than that inverted by traditional method, the relative error is below 4%. This method can solve the problem of complicated calculation process, as while as providing a new method for the inversion of lidar equations.

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.