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XIONG Wenjun, LI Die, NIAN Yiheng, et al. Multimodal OAM projection-focusing least squares imaging algorithm for regional coverage[J]. Journal of Radars, in press. doi: 10.12000/JR26048
Citation: XIONG Wenjun, LI Die, NIAN Yiheng, et al. Multimodal OAM projection-focusing least squares imaging algorithm for regional coverage[J]. Journal of Radars, in press. doi: 10.12000/JR26048
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Multimodal OAM Projection-Focusing Least Squares Imaging Algorithm for Regional Coverage

DOI: 10.12000/JR26048 CSTR: 32380.14.JR26048
  • 1.

    School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, China

  • 2.

    China Academy Space Technology Xi’an Branch, Xi’an 710100, China

Funds:  The National Key Research and Development Program (2022YFB3902400), The National Natural Science Foundation of China (62471379, 62071371)
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    Abstract
  • Multimodal orbital angular momentum imaging, which uses sparse recovery models, is a form of computational imaging where target reconstruction can be formulated as a linear inverse problem defined by an imaging equation. However, solving this problem using the least squares method can lead to substantial degradation in reconstruction quality, even with minor noise perturbations. Moreover, because the imaging equation is often underdetermined and has multiple solutions, the least squares method, which prioritizes data fitting accuracy, frequently produces results that deviate considerably from the actual target. Given that solution errors caused by noise are inversely proportional to the singular values of the reference matrix, this study first introduces an array design method that uses nonuniform element placement. This method, when compared to traditional uniform circular array designs, increases the number of array elements, reduces the correlation among the column vectors of the reference matrix, and decreases the number of small singular values. On this basis, a regional focusing least squares algorithm based on subspace projection is proposed. This algorithm first uses a basic correlation method to identify the target region. The echo vector is then projected onto the linear subspace of this target region. This projection transforms the underdetermined imaging equation into an overdetermined one within the focused target region. Concurrently, it effectively reduces noise power by exploiting the low correlation between noise and the target region’s linear subspace. The proposed algorithm’s effectiveness is subsequently validated through simulation experiments.

     

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