AI Article Synopsis
- Large 2-D sparse arrays produce high-resolution microwave images, but the presence of high sidelobes creates artifacts that reduce dynamic range.
- The paper proposes using the CLEAN technique for removing these artifacts, although it acknowledges that the traditional DFT method for estimating signal amplitudes is not very effective.
- Instead, it combines DFT for initial estimates with a maximum likelihood estimation method to improve accuracy and uses time domain information to lessen sidelobe effects, demonstrating improved results in numerical simulations.
Article Abstract
Large 2-D sparse array provides high angular resolution microwave images but artifacts are also induced by the high sidelobes of the beam pattern, thus, limiting its dynamic range. CLEAN technique has been used in the literature to extract strong scatterers for use in subsequent signal cancelation (artifacts removal). However, the performance of DFT parameters estimation based CLEAN algorithm for the estimation of the signal amplitudes is known to be poor, and this affects the signal cancelation. In this paper, DFT is used only to provide the initial estimates, and the maximum likelihood parameters estimation method with steepest descent implementation is then used to improve the precision of the calculated scatterers positions and amplitudes. Time domain information is also used to reduce the sidelobe levels. As a result, clear, artifact-free images could be obtained. The effects of multiple reflections and rotation speed estimation error are also discussed. The proposed method has been verified using numerical simulations and it has been shown to be effective.
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| http://dx.doi.org/10.1109/TIP.2010.2045711 | DOI Listing |
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