摘要: Abstract: In this paper, we establish a data model for the feature extraction of point scatterers in the presence of motion through resolution cell (MTRC) errors and unknown noise, the data model is a su... 展开 Abstract: In this paper, we establish a data model for the feature extraction of point scatterers in the presence of motion through resolution cell (MTRC) errors and unknown noise, the data model is a sum of 2-dimensional sinusoidal signals with quadratic phase errors, which are caused by 'range walk' and 'variable range rate' respectively. Based on the data model, we propose a parametric RELAX-based algorithm to extract the target features when there are MTRC errors in radar imaging. The algorithm minimizes a complicated nonlinear least-squares (NLS) cost function, and it is performed alternately by letting only the parameters and errors of one scatterer vary and freezing all others at their most recently determined values. The Cramer-Rao bounds (CRB's) for the parameters of the data model are also derived. We compare the performance of the proposed algorithm with the CRB's by simulation. And the results show that the mean squared errors of the parameter estimates obtained by the algorithm can approach the corresponding CRB's. Then we apply the algorithm to the simulated radar data with MTRC errors. The proposed algorithm generates 'focused' point image with higher resolution, which conforms the algorithm and the data model. !7 收起