Analysis of Two- and Multidimensional Radar Signals

This chapter explores methods for the analysis and decomposition of multicomponent and multidimensional radar signals. It begins with the Two-Dimensional Fourier Transform (2D-FT), demonstrating its application to radar imagery, including Inverse Synthetic Aperture Radar (ISAR) data. Practical examples illustrate how 2D spectral analysis aids in identifying structural features within a radar scene. The framework is then extended to the Windowed Two-Dimensional Fourier Transform (W2D-FT), which generalizes the STFT to 2D signals. This four-dimensional representation allows for the study of the nonstationary behavior of complex echoes in the time-space-frequency-spatial-frequency domain. The text discusses methods for concentrating two-dimensional radar spectra, such as enhancing ISAR images or refining range-velocity representations, explaining how these approaches improve interpretability by highlighting dominant signal components. Furthermore, techniques for noise flattening and decomposition of 2D signals are introduced, offering strategies for isolating individual signal components and reducing interference. Overall, the chapter provides a comprehensive overview of advanced multidimensional signal processing tools that support the detailed characterization of radar targets and complex scenes.