Biochemical transport modeling, estimation, and detection in realistic environments

Mathias Ortner; Arye Nehorai

doi:10.1117/12.665987

10 May 2006 Biochemical transport modeling, estimation, and detection in realistic environments

Mathias Ortner, Arye Nehorai

Proceedings Volume 6201, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense V; 62010S (2006) https://doi.org/10.1117/12.665987
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

Abstract

Early detection and estimation of the spread of a biochemical contaminant are major issues for homeland security applications. We present an integrated approach combining the measurements given by an array of biochemical sensors with a physical model of the dispersion and statistical analysis to solve these problems and provide system performance measures. We approximate the dispersion model of the contaminant in a realistic environment through numerical simulations of reflected stochastic diffusions describing the microscopic transport phenomena due to wind and chemical diffusion using the Feynman-Kac formula. We consider arbitrary complex geometries and account for wind turbulence. Localizing the dispersive sources is useful for decontamination purposes and estimation of the cloud evolution. To solve the associated inverse problem, we propose a Bayesian framework based on a random field that is particularly powerful for localizing multiple sources with small amounts of measurements. We also develop a sequential detector using the numerical transport model we propose. Sequential detection allows on-line analysis and detecting wether a change has occurred. We first focus on the formulation of a suitable sequential detector that overcomes the presence of unknown parameters (e.g. release time, intensity and location). We compute a bound on the expected delay before false detection in order to decide the threshold of the test. For a fixed false-alarm rate, we obtain the detection probability of a substance release as a function of its location and initial concentration. Numerical examples are presented for two real-world scenarios: an urban area and an indoor ventilation duct.

Citation Download Citation

Mathias Ortner and Arye Nehorai "Biochemical transport modeling, estimation, and detection in realistic environments", Proc. SPIE 6201, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense V, 62010S (10 May 2006); https://doi.org/10.1117/12.665987

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KEYWORDS

Sensors

Diffusion

Monte Carlo methods

Inverse problems

Biological research

Systems modeling

Turbulence

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