Radar Coincidence Imaging under Grid Mismatch
Author(s) -
Dongze Li,
Xiang Li,
Yongqiang Cheng,
Yuliang Qin,
Hongqiang Wang
Publication year - 2014
Publication title -
isrn signal processing
Language(s) - English
Resource type - Journals
eISSN - 2090-505X
pISSN - 2090-5041
DOI - 10.1155/2014/987803
Subject(s) - parameterized complexity , coincidence , grid , radar , computer science , radar imaging , algorithm , artificial intelligence , computer vision , resolution (logic) , image resolution , mathematics , geometry , telecommunications , medicine , alternative medicine , pathology
Radar coincidence imaging is an instantaneous imaging technique which does not depend on the relative motion between targets and radars. High-resolution, fine-quality images can be obtained using a single pulse either for stationary targets or for complexly maneuvering ones. There are two image-reconstruction algorithms used for radar coincidence imaging, that is, the correlation method and the parameterized method. In comparison with the former, the parameterized method can achieve much higher resolution but is seriously sensitive to grid mismatch. In the presence of grid mismatch, neither of the two algorithms can obtain recognizable high-resolution images. The above problem largely limits the applicability of radar coincidence imaging in actual imaging scenes where grid mismatch generally exists. This paper proposes a joint correlation-parameterization algorithm, which uses the correlation method to estimate the grid-mismatch error and then iteratively modifies the results of the parameterized method. The proposed algorithm can achieve high resolution with fine imagery quality under the grid mismatch. Examples are provided to illustrate the improvement of the proposed method.
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