Source identification of puff-based dispersion models using convex optimization

A convex optimization based source estimation method is presented for dynamic models. The effectiveness of the method is illustrated in the context of a simple atmospheric puff-based dispersion model. Source estimation is the process of inferring the source parameters from the sensor measurements and the physical model. In dispersion, the most important source parameters include the locations and strengths of the sources as well as their number. A source identification method usually involves global search of the multidimensional parameter space, including a large area of possible source locations based on a batch of sensor data gathered over a reasonably long time interval. In this work, a grid-based algorithm is presented for efficient source identification where the number of sources is unknown and may be large. The source identification problem is formulated as a convex optimization problem in the ℓ
₁
metric, which exploits the sparse nature of the solution to efficiently estimate the source characteristics.