Enhanced Target Detection for HFSWR by 2-D MUSIC Based on Sparse Recovery
| Autor: | Fei Xie, Chen Zhao, Chao He, Zezong Chen |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
business.industry
Computer science 0211 other engineering and technologies Estimator 020206 networking & telecommunications 02 engineering and technology Geotechnical Engineering and Engineering Geology Signal Track-before-detect Object detection law.invention symbols.namesake law Surface wave Stationary target indication 0202 electrical engineering electronic engineering information engineering symbols Clutter Computer vision Artificial intelligence Electrical and Electronic Engineering Radar business Doppler effect 021101 geological & geomatics engineering |
| Zdroj: | IEEE Geoscience and Remote Sensing Letters. 14:1983-1987 |
| ISSN: | 1558-0571 1545-598X |
| DOI: | 10.1109/lgrs.2017.2745048 |
| Popis: | This letter proposes using the 2-D multiple-signal classification (MUSIC) based on sparse recovery (SR) to improve the target-detection capability of high-frequency surface wave radar (HFSWR). Usually, for wide-beam HFSWRs, target detection is first conducted in the range-Doppler spectrum, and bearings are then estimated by superresolution methods such as MUSIC. Unfortunately, the conventional cascaded method can easily result in unfavorable deterioration of multitarget detection when different target signals tend to become mixed in the Doppler spectrum. Moreover, sea clutter is an unwanted signal that frequently masks target signals. To enhance the detection of multiple targets and targets embedded in sea clutter, spatial–temporal joint estimation has been proposed. However, because of the lack of spatial–temporal snapshots caused by the nonstationarity of target signals, the efficiency of the estimator cannot be guaranteed. To overcome this shortcoming, multiple-measurement-vector-based SR, which has been used to solve many under-sampling problems in the past ten years, is adopted. Our approach can effectively detect a target embedded in sea clutter as well as multiple adjacent targets and distinguish them from each other. Results obtained using real data with opportunistic targets validate our approach. Therefore, the proposed 2-D SR-MUSIC approach improves target detection and outperforms conventional cascaded methods. |
| Databáze: | OpenAIRE |
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