Nonlinear least-squares post-processing for compressive radar imaging of a rotating target
Date
2018
Authors
Nguyen, N.H.
Dogancay, K.
Tran, H.T.
Berry, P.
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Conference paper
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2018 International Conference On Radar (Radar), 2018, pp.1-6
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International Conference on Radar (RADAR) (27 Aug 2018 - 31 Aug 2018 : Brisbane, Australia)
Abstract
In compressive radar imaging of rotating targets, conventional sparse reconstruction algorithms produce blurred and low-contrast reconstructed images of the target due to dictionary mismatch caused by off-grid scatterers. A nonlinear least-squares post-processing (NLSPP) method has recently been developed to tackle the blurring problem existing in the reconstructed images based on cluster analysis and nonlinear least-squares estimation (NLSE). In this paper, we propose a new improvement of the NLSPP method, which is called the I-NLSPP method, based on a reformulation of the NLSE process of the NLSPP method. Specifically, by jointly performing the NLSE process over all atom clusters using the original backscattered signal, the proposed I-NLSPP method results in more accurate estimates of the positions and reflectivities of the scatterers constituting the target. The superior performance of the proposed I-NLSPP method over the NLSPP method is demonstrated by way of simulation. In particular, we observe that the I-NLSPP method achieves a mean-squared-error performance much closer to the Cramer-Rao lower bound than the NLSPP method.
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Copyright 2018 IEEE