The AES signature in the thermal IR portion of the spectrum, however, is dominated by the atmospheric interference , and the terrain background. In order to completely remove the atmospheric effects, detect the plume, and identify and quantify its contents, one has to have a decent knowledge of the environmental parameters that describes the radiative exchange. These parameters include, terrain temperature and spectral emissivity, atmospheric profiles of temperature, pressure, humidity, and other non-fixed components of the atmosphere. In addition, plume parameters such as temperature, composition, dimensions, are also required for a complete description. Since these environmental parameters are unknown, the approach constitutes and inverse problem , i.e., given the sensor measurements, the objective is to find out the environmental parameters that contribute to these measurements.
Inverse problems are ill-posed mathematical problems and are difficult to solve.
Most common approach is based on many simplifications of the atmospheric parameters and the radiative exchange model as depicted below. The approach for solving the inverse problem casts the problem as an optimization problem, minimizing an error function between model predictions and sensor observations. The optimization problem is then addressed using either a stochastic search process or a deterministic global optimization approach. One solution derived using genetic algorithms is depicted below. It shows the sensor data and the model predictions based on the parameter vector that was obtained from the optimization solution.