基于聯(lián)合稀疏性的多視全極化HRRP目標識別方法

劉盛啟; 占榮輝; 翟慶林; 歐建平; 張軍

doi:10.11999/JEIT151019

基于聯(lián)合稀疏性的多視全極化HRRP目標識別方法

doi: 10.11999/JEIT151019 cstr: 32379.14.JEIT151019

基金項目:

國家自然科學基金(61471370, 61401479)

計量
- 文章訪問數(shù): 1581
- HTML全文瀏覽量: 141
- PDF下載量: 495
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2015-09-09
- 修回日期: 2016-02-25
- 刊出日期: 2016-07-19

Multi-view Polarization HRRP Target Recognition Based on Joint Sparsity

Funds:

The National Natural Science Foundation of China (61471370, 61401479)

摘要

摘要: 該文考慮利用連續(xù)獲取的多視全極化高分辨距離像(High Range Resolution Profile, HRRP)進行目標識別的問題。多視全極化HRRP樣本包含了3個層次的先驗信息：樣本內各分量來自同一目標；單視內4種極化組合方式下的HRRP均對應相同的目標姿態(tài)；相同極化方式下的多視觀測是相關的。為有效利用上述信息進行目標識別，該文提出一種基于聯(lián)合稀疏表示的多視全極化HRRP目標識別方法。該方法約束各分量對應的稀疏表示系數(shù)共享原子級的稀疏模式。原子級稀疏約束使得從各極化字典中選擇來自相同姿態(tài)的字典原子對樣本中各分量進行稀疏表示，可以有效利用上述3個層次的先驗信息進行目標識別。利用目標電磁散射數(shù)據對所提方法進行了驗證，結果表明，該方法具有較好的識別性能，并且對噪聲具有良好的魯棒性。
- 雷達目標識別 /
- 多視 /
- 全極化 /
- 高分辨距離像 /
- 聯(lián)合稀疏表示
Abstract: The issue of automatically recognizing a target from its Full-Polarization High Range Resolution Profiles (FPHRRPs) with consecutive observations is considered. The prior information contained in a multi-view FPHRRP sample is hierarchical: all the entries contained in the sample are originated from the same target; the entries within a single view are associated with the same target pose; the multiple views under the same polarization mode are correlated. To utilize efficiently the prior information for target recognition, a novel joint sparse representation based multi-view FPHRRPs target recognition method is proposed. The presented method assumes all the entries within a multi-view FPHRRP sample share a common sparsity pattern in their sparse representation vectors at atom-level, which has the advantage of exploiting the aforementioned information to enhance recognition performance. Experiments are conducted using a synthetic vehicle target dataset. The results show that the proposed method achieves promising recognition accuracy and it is robust with respect to noisy observations.
- Radar target recognition /
- Multi-view /
- Full-polarization /
- High Range Resolution Profile (HRRP) /
- Joint sparse representation

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