This paper investigates the application of SVM (Support Vector Machines) for the classification of stationary human targets and indoor clutter via spectral features. Applying Finite Difference Time Domain (FDTD) techniques allows us to examine the radar cross section (RCS) of humans and indoor clutter objects by utilizing different types of computer models. FDTD allows for the spectral characteristics to be acquired over a wide range of frequencies, polarizations, aspect angles, and materials. The acquired target and clutter RCS spectral characteristics are then investigated in terms of their potential for target classification using SVMs. Based upon variables such as frequency and polarization, a SVM classifier can be trained to classify unknown targets as a human or clutter. Furthermore, the application of feature selection is applied to the spectral characteristics to determine the SVM classification accuracy of a reduced dataset. Classification accuracies of nearly 90% are achieved using radial and polynomial kernels.
Sudoku puzzles, often seen in magazines and newspapers, are logic-based challenges where each entry within the puzzle is comprised of symbols adhering to row, column and box constraints. Previously, we had investigated their potential in frequency-hopped waveforms to achieve desirable radar ambiguity functions and compared them with random, as well as the more familiar Costas sequences. This paper further examines the properties of Sudoku codes in more detail through computational search and analysis. We examine the co-hit and cross-hit arrays, defined as the correlation between two sequences, to quickly and efficiently evaluate numerous Sudoku puzzles. Additionally, we investigate the use of Sudoku puzzles for antenna applications, including array interleaving, array thinning and random element spacing.
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