Time-averaged subspace methods for radar clutter texture retrieval

Subspace approaches have become popular in the last two decades for retrieving constant amplitude harmonics observed in white additive noise because they may exhibit superior resolution over the FFT-based methods, especially with short data records and closely spaced harmonics. We demonstrate that MUSIC and ESPRIT methods can also be applied when the harmonics are corrupted by white or wideband multiplicative noise. The application context is the retrieval of texture information from high resolution and low grazing angle radar clutter data affected by wideband colored speckle that is modeled as complex multiplicative noise. Texture information is fundamental for clutter cancellation and constant false alarm rate (CFAR) radar detection. A thorough numerical analysis compares the two subspace methods and validates the theoretical findings.