Detection and classification of atmospheric aerosols using multi-wavelength LWIR LIDAR

Warren, Russell E.; Vanderbeek, Richard G.; Ahl, Jeffrey L.

doi:10.1117/12.818694

Cited by 7 publications

(6 citation statements)

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“…For each cloud i = 1, …, 30 at 50 time periods (called bursts), which are sampled at one second apart, and various CO 2 laser wavelengths, the background corrected received signal is observed at 250 equally spaced range values. Here we concentrate on the (range invariant) backscatter efficiency of the true signal as estimated using the algorithm of Warren et al (2008, 2009), but applied to the observed data rather than the deconvolved data.…”

Section: Discussionmentioning

confidence: 99%

Significance tests for functional data with complex dependence structure

Staicu

Lahiri

Carroll

2015

Journal of Statistical Planning and Inference

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We propose an L2-norm based global testing procedure for the null hypothesis that multiple group mean functions are equal, for functional data with complex dependence structure. Specifically, we consider the setting of functional data with a multilevel structure of the form groups–clusters or subjects–units, where the unit-level profiles are spatially correlated within the cluster, and the cluster-level data are independent. Orthogonal series expansions are used to approximate the group mean functions and the test statistic is estimated using the basis coefficients. The asymptotic null distribution of the test statistic is developed, under mild regularity conditions. To our knowledge this is the first work that studies hypothesis testing, when data have such complex multilevel functional and spatial structure. Two small-sample alternatives, including a novel block bootstrap for functional data, are proposed, and their performance is examined in simulation studies. The paper concludes with an illustration of a motivating experiment.

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Section: Discussionmentioning

confidence: 99%

Significance tests for functional data with complex dependence structure

Staicu

Lahiri

Carroll

2015

Journal of Statistical Planning and Inference

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“…The data are described in detail by Carroll et al (2012), and the estimation of spectral backscatter uses the algorithm of Warren et al (2008, 2009), but applied to the observed data rather than the deconvolved data. Our main goal is to estimate the probability of high spectral backscatter at different wavelengths.…”

Section: Discussionmentioning

confidence: 99%

Multilevel Cross-Dependent Binary Longitudinal Data

2013

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We provide insights into new methodology for the analysis of multilevel binary data observed longitudinally, when the repeated longitudinal measurements are correlated. The proposed model is logistic functional regression conditioned on three latent processes describing the within- and between-variability, and describing the cross-dependence of the repeated longitudinal measurements. We estimate the model components without employing mixed-effects modeling but assuming an approximation to the logistic link function. The primary objectives of this paper are to highlight the challenges in the estimation of the model components, to compare two approximations to the logistic regression function, linear and exponential, and to discuss their advantages and limitations. The linear approximation is computationally efficient whereas the exponential approximation applies for rare events functional data. Our methods are inspired by and applied to a scientific experiment on spectral backscatter from long range infrared light detection and ranging (LIDAR) data. The models are general and relevant to many new binary functional data sets, with or without dependence between repeated functional measurements.

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“…These backscatter absorption features can be important signatures for discrimination. For example, it is believed that the existing long-wavelength infrared (LWIR) LIDAR system based on the frequency agile laser (FAL) exploits an absorption feature occurring around 9.2 μm [1, 14]. As observed in Fig.…”

Section: Self-absorption Effectmentioning

confidence: 97%

Optical properties and cross-sections of biological aerosols

et al. 2010

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There is an urgent need to develop standoff sensing of biological agents in aerosolized clouds. In support of the Joint Biological Standoff Detection System (JBSDS) program, lidar systems have been a dominant technology and have shown significant capability in field tests conducted in the Joint Ambient Breeze Tunnel (JABT) at Dugway Proving Ground (DPG). The release of biological agents in the open air is forbidden. Therefore, indirect methods must be developed to determine agent cross-sections in order to validate sensor against biological agents. A method has been developed that begins with laboratory measurements of thin films and liquid suspensions of biological material to obtain the complex index of refraction from the ultraviolet (UV) to the long wave infrared (LWIR). Using that result and the aerosols' particle size distribution as inputs to Mie calculations yields the backscatter and extinction cross-sections as a function of wavelength. Recent efforts to model field measurements from the UV to the IR have been successful. Measurements with aerodynamic and geometric particle sizers show evidence of particle clustering. Backscatter simulations of these aerosols show these clustered particles dominate the aerosol backscatter and depolarization signals. In addition, these large particles create spectral signatures in the backscatter signal due to material absorption. Spectral signatures from the UV to the IR have been observed in simulations of field releases. This method has been demonstrated for a variety of biological simulant materials such as Ovalbumin (OV), Erwinia (EH), Bacillus atrophaeus (BG) and male specific bacteriophage (MS2). These spectral signatures may offer new methods for biological discrimination for both stand-off sensing and point detection systems.

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Detection and classification of atmospheric aerosols using multi-wavelength LWIR LIDAR

Cited by 7 publications

References 0 publications

Significance tests for functional data with complex dependence structure

Significance tests for functional data with complex dependence structure

Multilevel Cross-Dependent Binary Longitudinal Data

Optical properties and cross-sections of biological aerosols

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