Characterization of optimum polarization for multiple target discrimination using genetic algorithms

A stochastic optimization algorithm is used to characterize the polarization states of a nonpolarimetric radar transmitter and receiver antennas for optimal target classification. Specifically, the optimized solution is sought when a multitude of targets are to be categorized. It is shown that the objective function of the optimization problem is highly nonlinear and discontinuous, hence, classical optimization algorithms fail to provide satisfactory results. The stochastic optimization algorithm used is based on a genetic algorithm (GA) which operates on a discretized form of the parameter space and searches globally for the optimum point. In this process, it is assumed that the polarimetric responses of the targets are known a priori. The optimization algorithm is applied to two sets of data: (1) a synthetic backscatter data for four point targets with similar radar cross sections (RCSs) and (2) a set of polarimetric backscatter measurements of asphalt surfaces under different physical conditions at 94 GHz. The purpose of the latter study is to come up with the optimal design for polarization states of an affordable millimeter-wave radar sensor that can assess traction of road surfaces.