Analysis of Clutter for Passive Radar on Moving Platforms Using Tunable Q-factor Wavelet Transforms
Abstract
Passive radar systems on moving platforms suffer from Doppler-spread clutter that can mask moving targets of interest. Moreover, platform size restrictions further limit the number of antenna channels, which are needed to ameliorate the impacts of Doppler-spread clutter. However, an analysis of the clutter returns can potentially aid clutter cancellation. For this analysis, wavelet transforms (WT) are proposed to investigate different characteristics of clutter. A property of the WT is its ability to decompose signals at multiple scales, or resolutions, with an adaptive time window, compared to the Fourier transform and short time Fourier transform. A very different partitioning of the WT reveals different characteristics and information of signals which can potentially lead to the target signal being better distinguished. The paper also investigates a sparse signal separation method using tunable Q-factor wavelet transforms to extract target signals from clutter for a passive radar on moving platforms exploiting DVB-T transmissions.
