Richard G. Vanderbeek

Simultaneous estimation of aerosol cloud concentration and spectral backscatter from multiple-wavelength lidar data

We present a sequential algorithm for estimating both concentration dependence on range and time and backscatter coefficient spectral dependence of optically thin localized atmospheric aerosols using data from rapidly tuned lidar. The range dependence of the aerosol is modeled as an expansion of the concentration in an orthonormal basis set whose coefficients carry the time dependence. Two estimators are run in parallel: a Kalman filter for the concentration range and time dependence and a maximum-likelihood estimator for the aerosol backscatter wavelength and time dependence. These two estimators exchange information continuously over the data-processing stream. The state model parameters of the Kalman filter are also estimated sequentially together with the concentration and backscatter. Lidar data collected prior to the aerosol release are used to estimate the ambient lidar return. The approach is illustrated on atmospheric backscatter long-wave infrared (CO2) lidar data.

Detection and classification of atmospheric aerosols using multi-wavelength CO2 lidar

This paper presents an overview of recent work by ECBC in algorithm development for parameter estimation, detection, and classification of localized aerosols in the atmosphere using information provided by multiple-wavelength rangeresolved lidar. The motivation for this work is the need to detect, locate, and identify potentially toxic atmospheric aerosols at safe standoff ranges using time-series data collected at a discrete set of CO2 laser wavelengths. The goals of the processing are to use the digitized transmitted and received backscatter array data to (1) decide if significant aerosol is present, (2) provide estimates of the range and size of the aerosol cloud, (3) produce estimates of the backscatter spectral dependence, and (4) use the backscatter signatures as feature vectors for training and implementation of a support vector machine aerosol classifier. The paper describes examples this processing derived from an extensive set of data collected by ECBC during JBSDS field-testing at Dugway Proving Ground.

Estimation and Discrimination of Aerosols Using Multiple Wavelength LWIR Lidar

This paper presents an overview of recent work by the Edgewood Chemical Biological Center (ECBC) in algorithm development for parameter estimation and classification of localized atmospheric aerosols using data from rapidly tuned multiple-wavelength range-resolved LWIR lidar. The motivation for this work is the need to detect, locate, and discriminate biological threat aerosols in the atmosphere from interferent materials such as dust and smoke at safe standoff ranges using time-series data collected at a discrete set of CO2 laser wavelengths. The goals of the processing are to provide real-time aerosol detection, localization, and discrimination. Earlier work by the authors has produced an efficient Kalman filter-based algorithm for estimating the range-dependent aerosol concentration and wavelength-dependent backscatter signatures. The latter estimates are used as feature vectors for training support vector machines classifiers for performing the discrimination. Several years of field testing under the Joint Biological Standoff Detection System program at Dugway Proving Ground, UT, Eglin Air Force Base, FL, and other locations have produced data and backscatter estimates from a broad range of biological and interferent aerosol materials for the classifier development. The results of this work are summarized in our presentation.

System performance and modeling of a bioaerosol detection lidar sensor utilizing polarization diversity

The weaponization and dissemination of biological warfare agents (BWA) constitute a high threat to civilians and military personnel. An aerosol release, disseminated from a single point, can directly affect large areas and many people in a short time. Because of this threat real-time standoff detection of BWAs is a key requirement for national and military security. BWAs are a general class of material that can refer to spores, bacteria, toxins, or viruses. These bioaerosols have a tremendous size, shape, and chemical diversity that, at present, are not well characterized [1]. Lockheed Martin Coherent Technologies (LMCT) has developed a standoff lidar sensor with high sensitivity and robust discrimination capabilities with a size and ruggedness that is appropriate for military use. This technology utilizes multiwavelength backscatter polarization diversity to discriminate between biological threats and naturally occurring interferents such as dust, smoke, and pollen. The optical design and hardware selection of the system has been driven by performance modeling leading to an understanding of measured system sensitivity. Here we briefly discuss the challenges of standoff bioaerosol discrimination and the approach used by LMCT to overcome these challenges. We review the radiometric calculations involved in modeling direct-detection of a distributed aerosol target and methods for accurately estimating wavelength dependent plume backscatter coefficients. Key model parameters and their validation are discussed and outlined. Metrics for sensor sensitivity are defined, modeled, and compared directly to data taken at Dugway Proving Ground, UT in 2008. Sensor sensitivity is modeled to predict performance changes between day and night operation and in various challenging environmental conditions.

Overview of chem-bio sensing

The US Army Edgewood Chemical Biological Center is the leader in development of military systems for chemical and biological defense, in collaboration with all Services, other Government laboratories, academia, and industry. Chemical and biological optical sensing principles, unique capabilities, state-of-the-art sensors, and emerging technologies will be discussed. Exciting new results will be presented on standoff biodiscrimination using infrared (IR) depolarization lidar and long-wave IR (LWIR) lidar.

Range-resolved frequency-agile CO2 lidar measurements of smokestack vapor effluents

Range-resolved lidar measurements of chemical vapor output from a smokestack were conducted using a moderate-power (100 millijoules per pulse) frequency-agile CO2 differential absorption lidar (DIAL) system. A 70-foot non-industrial smokestack, erected for the purpose of studying effluent emissions, was used in the experiment. These measurements were conducted for the purpose of obtaining real data to support development of advanced chemical and biological (CB) range- resolved vapor detection algorithms. Plume transmission measurements were made using natural atmospheric backscatter from points at the mouth of the stack and several positions downwind. Controlled releases of triethyl-phosphate (TEP), dimethyl-methylphosphonate (DMMP), and sulfur-hexaflouride (SF6) were performed. Test methodology and experimental results are presented. Effective application of ground-based lidar to the monitoring of smokestack effluents, without the use of fixed targets, is discussed.

Estimation of multiple-aerosol concentration and backscatter using multi-wavelength range-resolved lidar

Previous work by the authors has produced statistically based methods for detecting, estimating and classifying aerosol materials in the atmosphere using multiple-wavelength range-resolved CO2 lidar. This work has thus far been limited to the presence of a single aerosol material at a given time within the lidar line-of-sight. Practical implementation requires the ability to detect and discriminate multiple aerosol materials present simultaneously such as smoke and dust in addition to hazardous materials. Treating mixtures of materials necessitates fundamentally different approaches from the single-material case since neither the aerosol backscatter wavelength-dependence nor the concentrations as a function of range are known. Because of this, linear processing cannot resolve the mixture data into its components unambiguously, and non-linear methods must be considered. In this paper we describe an empirical Bayes (EB) approach for resolving mixtures of aerosol into their components. The basic idea of EB is to use the same data to estimate the prior distribution of a set of parameters as that used to estimate the parameters themselves. In our case the concentration and backscatter are the parameters that are estimated with the help of a prior distribution of the backscatter. We implement the EB estimator through the EM (Expectation Maximization) algorithm. The resulting processor is applied to injections of interferent dust into data sets collected by ECBC during JBSDS testing at Dugway Proving Ground, UT in 2006.

Online estimation of vapor path-integrated concentration and absorptivity using multiwavelength differential absorption lidar

Differential absorption lidar data processing traditionally assumes knowledge of the spectral dependence of the absorptivity coefficients. While this is sometimes a good assumption, it is often not in complicated collection environments where the material present is ambiguous. We present an alternative approach that estimates the vapor path-integrated concentration (CL) and absorptivity (rho) in parallel by a processor capable of online implementation. The algorithm is based on an extended Kalman filter (EKF) for CL and a sequential maximum likelihood estimator for rho. The state model parameters of the EKF are also estimated sequentially together with CL and rho. The approach is illustrated on simulated and real topographic backscatter lidar data collected by the Edgewood Chemical Biological Center.

Effect of spectral time-lag correlation coefficient and signal averaging on airborne CO 2 DIAL measurements

The effects of flight geometry, signal averaging and time- lag correlation coefficient on airborne CO2 dial lidar measurements are shown in simulations and field measurements. These factors have implications for multi- vapor measurements and also for measuring a shingle vapor with a wide absorption spectra for which one would like to make DIAL measurements at many wavelengths across the absorption spectra of the gas. Thus it is of interest to know how many wavelengths and how many groups of wavelengths can be used effectively in DIAL measurements. Our data indicate that for our lidar about 80 wavelengths can be used for DIAL measurements of a stationary vapor. The lidar signal is composed of fluctuations with three time scales: a very short time scale due to system noise which is faster than the data acquisition sampling rate of the receiver, a medium time scale due to atmospheric turbulence, and a long time scale due to slow atmospheric transmission drift from aerosol in homogeneities. The decorrelation time scale of fluctuations for airborne lidar measurements depends on the flight geometry.

Chemical-biological detection overview

State-of-the-art sensors and emerging technologies are under development for chemical and biological agent defense. Spectroscopic approaches such as differential scattering, depolarization, and Raman will be discussed.