Parameter extraction of 3D acoustic images using a nonlinear optimization technique

Abstract

In biometrics, classification techniques are based on parameter extraction from a large data set, such as fingerprints. Specifically, when using 3D acoustic images, it is necessary to extract a set of meaningful parameters. This work assumes that each significant target will have an acoustic image characterized by the 2D radiation pattern of the array and the envelope of the transmission pulse used. Under this assumption the final acoustic image can be synthesized by a linear combination of the significant targets in the scene. The work uses a non-linear optimization algorithm that obtains the parameters (power, range, elevation and azimuth) for each of the significant targets from a 3D acoustic image. Specifically, the algorithm is applied to the parameterization of 3D images of people, as a prior step to the use of classification algorithms based on machine learning.