Optimal Target Classification Using Frequency-Based Radar Waveform Design

In this article, we introduce a frequency-based waveform design to maximize binary target classification. The optimization problems can be solved via optimal solvers available in the literature. presented formulation is applicable classification scenarios where extended frequency responses (TFRs) are complex, random, and normally distributed with unequal mean vectors. However, their covariance matrices either identical or different. For former, i.e., matrices, problem consists of cost function that has been obtained by deriving closed-form expression probability misclassification. latter, different present an Fisher separation function. this, also for approximate solution problem. We show proposed achieves performance levels comparable exact state-of-the-art while incurring low computational complexity. expand on two main introducing clutter (i.e., signal-dependent interference) signal model studying possible designs extreme energy levels. Simulations conducted using synthetically generated data addition civilian vehicle from Moving Stationary Target Acquisition Recognition (MSTAR) dataset, order validate method.