Multi-view Polarization HRRP Target Recognition Based on Joint Sparsity

LIU Shengqi; ZHAN Ronghui; ZHAI Qinglin; OU Jianping; ZHANG Jun

doi:10.11999/JEIT151019

Volume 38 Issue 7

Jul. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(7): 1724-1730

LIU Shengqi, ZHAN Ronghui, ZHAI Qinglin, OU Jianping, ZHANG Jun. Multi-view Polarization HRRP Target Recognition Based on Joint Sparsity[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1724-1730. doi: 10.11999/JEIT151019

Citation:

LIU Shengqi, ZHAN Ronghui, ZHAI Qinglin, OU Jianping, ZHANG Jun. Multi-view Polarization HRRP Target Recognition Based on Joint Sparsity[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1724-1730. doi: 10.11999/JEIT151019

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Multi-view Polarization HRRP Target Recognition Based on Joint Sparsity

doi: 10.11999/JEIT151019 cstr: 32379.14.JEIT151019

Funds:

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

Received Date: 2015-09-09
Rev Recd Date: 2016-02-25
Publish Date: 2016-07-19

Abstract

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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References(28)

References

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