論文ID: 2017XBL0015
Super-resolution time of arrival estimation methods have attracted much attention in radar signal processing. Many studies have used compressed sensing (CS)-based approaches to attain the super-resolution property because they assume sparseness of the object temporal distribution. However, this approach still suffer from accuracy degradation when decomposing highly correlated signals in heavily noise-contaminated situations. To resolve this problem, this study introduces an enhanced CS method by exploiting the sparseness of both the time and frequency domains of the target signals. Numerical simulation and comparison with results obtained by conventional methods demonstrate that the proposed method considerably enhances the reconstruction accuracy for multiple highly correlated signals in lower signal-to-noise ratio situations.
