Name
Abstract | ||
|---|---|---|
High resolution range profile (HRRP) plays an important role in wideband radar automatic target recognition (ATR). In order to alleviate the sensitivity to clutter and target aspect, employing a sequence of HRRP is a promising approach to enhance the ATR performance. In this paper, a novel HRRP sequence-matching method based on singular value decomposition (SVD) is proposed. First, the HRRP sequence is decoupled into the angle space and the range space via SVD, which correspond to the span of the left and the right singular vectors, respectively. Second, atomic norm minimization (ANM) is utilized to estimate dominant scatterers in the range space and the Hausdorff distance is employed to measure the scatter similarity between the test and training data. Next, the angle space similarity between the test and training data is evaluated based on the left singular vector correlations. Finally, the range space matching result and the angle space correlation are fused with the singular values as weights. Simulation and outfield experimental results demonstrate that the proposed matching metric is a robust similarity measure for HRRP sequence recognition. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/s18020593 | SENSORS |
Keywords | Field | DocType |
automatic target recognition (ATR),high resolution range profile (HRRP),singular value decomposition (SVD),atomic norm minimization (ANM),feature extraction | Singular value decomposition,Singular value,Pattern recognition,Similarity measure,Automatic target recognition,Clutter,Feature extraction,Electronic engineering,Correlation,Hausdorff distance,Artificial intelligence,Engineering | Journal |
Volume | Issue | ISSN |
18 | 2.0 | 1424-8220 |
Citations | PageRank | References |
0 | 0.34 | 15 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuan Jiang | 1 | 0 | 0.68 |
| Yang Li | 2 | 0 | 0.34 |
| Jinjian Cai | 3 | 0 | 0.34 |
| Yanhua Wang | 4 | 47 | 6.35 |
| Jia Xu | 5 | 0 | 0.34 |