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| Citation: | ZHANG Yin, ZHANG Ping, TUO Xingyu, et al. Sparse targets angular super-resolution reconstruction method under unknown antenna pattern errors for scanning radar[J]. Journal of Radars, in press. doi: 10.12000/JR23208
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Sparse Targets Angular Super-resolution Reconstruction Method under Unknown Antenna Pattern Errors for Scanning Radar
doi: 10.12000/JR23208
More Information-
Abstract
Scanning radar angular super-resolution technology is based on the relationship between the target and antenna pattern, and a deconvolution method is used to obtain angular resolution capabilities beyond the real beam. Most current angular super-resolution methods are based on ideal distortion-free antenna patterns and do not consider pattern changes in the actual process due to the influence of factors such as radar radome, antenna measurement errors, and non-ideal platform motion. In practice, an antenna pattern often has unknown errors, which can result in reduced target resolution and even false target generation. To address this problem, this paper proposes an angular super-resolution imaging method for airborne radar with unknown antenna errors. First, based on the total least square criterion, this paper considers the effect of the pattern error matrix and derive the corresponding objective function. Second, this paper employs the iterative reweighted optimization method to solve the objective function by adopting an alternative iteration solution idea. Finally, an adaptive parameter update method is introduced for algorithm hyperparameter selection. The simulation and experimental results demonstrate that the proposed method can achieve super-resolution reconstruction even in the presence of unknown antenna errors, promoting the robustness of the super-resolution algorithm. -
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References
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- Figure 1. Geometric structure of airborne scanning radar
- Figure 2. Target distribution condition
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Figure 3. One-dimensional point target processing results under little broaden errors (
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Figure 4. One-dimensional point target processing results under large broden errors (
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Figure 5. One-dimensional point target processing results under little random errors (
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Figure 6. One-dimensional point target processing results under large random errors (
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Figure 7. One-dimensional point target processing results under little broaden errors with random errors (
$ \gamma $ $\xi $ -
Figure 8. One-dimensional point target processing results under large broaden errors with random errors (
$ \gamma $ $\xi $ - Figure 9. Root mean square error curve chart under different signal-to-noise ratios
- Figure 10. Area target scene and target distribution
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Figure 11. Two-dimensional area target processing results under little broaden errors (
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Figure 12. Two-dimensional area target processing results under large broaden errors (
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Figure 13. Two-dimensional area target processing results under little random errors (
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Figure 14. Two-dimensional area target processing results under large random errors (
$\xi $ -
Figure 15. Two-dimensional area target processing results under little broaden errors with random errors (
$ \gamma $ $\xi $ -
Figure 16. Two-dimensional area target processing results under large broaden errors with random errors (
$ \gamma $ $\xi $ - Figure 17. Image entropy curve chart under different signal-to-noise ratios
- Figure 18. Corner reflector laying optical figure
- Figure 19. Experiment data processing results