Using t-distribution Based Probabilistic Principal Component Analysis Model for High Resolution Range Profile Recognition

LI Bin; LI Hui; GUO Songyun

doi:10.11999/JEIT161220

Volume 39 Issue 8

Aug. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(8): 1857-1864

LI Bin, LI Hui, GUO Songyun. Using t-distribution Based Probabilistic Principal Component Analysis Model for High Resolution Range Profile Recognition[J]. Journal of Electronics & Information Technology, 2017, 39(8): 1857-1864. doi: 10.11999/JEIT161220

Citation:

LI Bin, LI Hui, GUO Songyun. Using t-distribution Based Probabilistic Principal Component Analysis Model for High Resolution Range Profile Recognition[J]. Journal of Electronics & Information Technology, 2017, 39(8): 1857-1864. doi: 10.11999/JEIT161220

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Using t-distribution Based Probabilistic Principal Component Analysis Model for High Resolution Range Profile Recognition

doi: 10.11999/JEIT161220

Funds:

The National Natural Science Foundation of China (61571364), The Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Poly-technical University (Z2017022)

Received Date: 2016-11-10
Rev Recd Date: 2017-04-06
Publish Date: 2017-08-19

Abstract

Abstract

In order to improve the sensitivity problem of using Probabilistic Principal Component Analysis (PPCA) model for HRRP recognition, a modified method is proposed. T-distribution is adopted as the basis of PPCA model rather than Gaussian distribution in this method, which utilizes not only the t-distributions robustness, but also less free parameters of PPCA characteristic. Further, to eliminate the targets azimuth sensitivity, the mixture t-distribution is substituted for single t-distribution. This modification offers a potential to model the similar density of HRRP in different azimuth range adequately for clustering and reduces the mismatch between models, thus improves the recognition performance. Estimation of parameters is achieved by EM algorithm to avoid the drawbacks of maximum-likelihood estimation and improve the estimation efficiency. Finally, in the simulation experiment Bayesian rule and the estimation statistical features are adopted together to test new HRRPs, the results show this method can improve the robustness of PPCA model in low SNR conditions.
- Radar target recognition,
- High Resolution Range Profile (HRRP),
- Probabilistic Principal Component Analysis (PPCA),
- t-distribution,
- Feature extraction

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References(28)

References

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