Autofocus technique for radar coincidence imaging with model error via iterative maximum a posteriori
| Autor: | Feng Zhang, Xunling Liu, Xiaoli Zhou, Xu Wang, Weijian Liu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
radar imaging
iterative methods calibration stochastic processes maximum likelihood estimation matrix algebra autofocus technique radar coincidence imaging model error RCI wavefront random modulation temporal–spatial stochastic radiation field coincidence imaging processing imaging performance sparsity-driven autofocus imaging simultaneous sparse imaging staring imaging technique reference matrix iterative maximum a posteriori algorithm SA-MAP algorithm Engineering (General). Civil engineering (General) TA1-2040 |
| Zdroj: | The Journal of Engineering (2019) |
| Druh dokumentu: | article |
| ISSN: | 2051-3305 |
| DOI: | 10.1049/joe.2019.0129 |
| Popis: | Radar coincidence imaging (RCI) is a recently developed staring imaging technique based on the wavefront random modulation and temporal–spatial stochastic radiation field. Before coincidence imaging processing, the RCI needs to compute the reference matrix accurately. Unfortunately, model error usually exists, which degrades the imaging performance considerably. In this article, by exploiting the sparse prior of target, the authors propose a sparsity-driven autofocus imaging via iterative maximum a posteriori (SA-MAP) algorithm for the RCI when model error exists. The algorithm performs well via simultaneous sparse imaging, self-calibration and parameters update. Simulation results demonstrate the validity of the proposed algorithm and performance improvement over the existing algorithms. |
| Databáze: | Directory of Open Access Journals |
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