Radar HRRP Target Recognition Method Based on Multi-task Learning and Complex Factor Analysis

He Hua; Du Lan; Xu Dan-lei; Liu Hong-wei

doi:10.11999/JEIT141591

Volume 37 Issue 10

Sep. 2015

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Article Navigation > Journal of Electronics & Information Technology > 2015 > 37(10): 2307-2313

He Hua, Du Lan, Xu Dan-lei, Liu Hong-wei. Radar HRRP Target Recognition Method Based on Multi-task Learning and Complex Factor Analysis[J]. Journal of Electronics & Information Technology, 2015, 37(10): 2307-2313. doi: 10.11999/JEIT141591

Citation:

He Hua, Du Lan, Xu Dan-lei, Liu Hong-wei. Radar HRRP Target Recognition Method Based on Multi-task Learning and Complex Factor Analysis[J]. Journal of Electronics & Information Technology, 2015, 37(10): 2307-2313. doi: 10.11999/JEIT141591

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Radar HRRP Target Recognition Method Based on Multi-task Learning and Complex Factor Analysis

doi: 10.11999/JEIT141591

Funds:

The National Natural Science Foundation of China (61271024, 61201296, 61322103)

Received Date: 2014-12-11
Rev Recd Date: 2015-06-29
Publish Date: 2015-10-19

Abstract

Abstract

Most traditional recognition methods for High Resolution Range Profile (HRRP) only utilize the amplitude information and need large number of training samples to obtain better estimation precision of model parameters. To utilize the phase information contained in the complex echoes and obtain better recognition performance with small training data and low sampling rate, a statistical model based on Multi-Task Leaning (MTL) and Complex Factor Analysis (CFA), referred to as MTL-CFA, is proposed in this paper. The MTL-CFA model directly describes the complex HRRP data. The statistical modeling of each training aspect-frame is considered as a single task, and all tasks share a common loading matrix. The factor number of each task is automatically determined via the Beta-Bernoulli sparse prior. Experimental results based on measured data show that the proposed model MTL-CFA can not only describe the observed data with lower order of model complexity, but also obtain satisfactory recognition accuracy with small training data, compared with the traditional Single- Task Learning (STL) based on FA models.
- Radar Automatic Target Recognition (RATR),
- High Resolution Range Profile (HRRP),
- Multi-Task Learning (MTL),
- Factor Analysis (FA)

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References(24)

References

张瑞, 牛威寇鹏. 基于样本紧密度的雷达高分辨距离像识别方法研究[J]. 电子与信息学报, 2014, 36(3): 529-536.

Zhang Rui, Niu Wei, and Kou Peng. Radar high resolution range profiles recognition based on the affinity[J]. Journal of Electronics Information Technology, 2014, 36(3): 529-536.

张学峰, 王鹏辉, 冯博, 等. 基于多分类器融合的雷达高分辨距离像目标识别与拒判新方法[J]. 自动化学报, 2014, 40(2): 348-356.

Zhang Xue-feng, Wang Peng-hui, Feng Bo, et al.. A new method to improve radar HRRP recognition and outlier rejection performances based on classifier combination[J]. Acta Automatica Sinica, 2014, 40(2): 348-356.

张玉玺, 王晓丹, 姚旭, 等. 基于复数全极化HRRP的雷达目标识别[J]. 系统工程与电子技术, 2014, 36(2): 260-265.

Zhang Yu-xi, Wang Xiao-dan, Yao Xu, et al.. Radar target recognition based on complex fully polarimeric HRRP[J]. Systems Engineering and Electronics, 2014, 36(2): 260-265.

潘勉, 王鹏辉, 杜兰, 等. 基于 TSB-HMM 模型的雷达高分辨距离像目标识别方法[J]. 电子与信息学报, 2013, 35(7): 1547-1554.

Pan Mian, Wang Peng-hui, Du Lan, et al.. Radar HRRP target recognition based on truncated stick-breaking hidden markov model[J], Journal of Electronics Information Technology, 2013, 35(7): 1547-1554.

姚莉娜, 吴艳敏, 崔光照. 基于随机森林的雷达高分辨距离像目标识别新方法[J]. 郑州大学学报(工学版), 2014, 35(4): 105-108.

Yao Li-na, Wu Yan-min, and Cui Guang-zhao. New method of radar high resolution range image target recognition based on random forest[J]. Journal of Zhengzhou University (Engineering Science), 2014, 35(4): 105-108.

Jacobs S P and OSullivan J A. Automatic target recognition using sequences of high resolution radar range profiles[J]. IEEE Transactions on Aerospace Electronic Systems, 2000, 36(2): 364-380.

Du L, Liu H, and Bao Z. Radar HRRP statistical recognition: parametric model and model selection[J]. IEEE Transactions on Signal Processing, 2008, 56(5): 1931-1944.

Du L, Liu H, and Bao Z. A two-distribution compounded statistical model for radar HRRP target recognition[J]. IEEE Transactions on Signal Processing, 2006, 54(6): 2226-2238.

Du L, Liu H, and Bao Z. A novel feature vector using complex HRRP for radar target recognition[J]. Lecture Notes in Computer Science, 2007, 4491: 1303-1309.

王鹏辉, 杜兰, 刘宏伟. 基于复高斯模型的雷达高分辨距离像目标识别新方法[J]. 光学学报, 2014, 34(2): 275-284.

Wang Peng-hui, Du Lan, and Liu Hong-wei. A new method based on complex Gaussian models for radar high resolution range profile target recognition[J]. Acta Optica Sinica, 2014, 34(2): 275-284.

王鹏辉. 基于统计建模的雷达高分辨距离像目标识别方法研究[D]. [博士论文], 西安电子科技大学, 2012.

Wang Peng-hui. Study of radar high resolution range profile target recognition based on statistical modeling[D]. [Ph.D dissertation], Xidian University, 2012.

Xue Y, Liao X, and Carin L. Multi-task learning for classification with Dirichlet process priors[J]. The Journal of Machine Learning Research, 2007, 8: 35-63.

Pan M, Du L, and Wang P. Multi-task hidden Markov modeling of spectrogram feature from radar high-resolution range profiles[J]. EURASIP Journal on Advances in Signal Processing, 2012(1): 1-17.

Watanabe S. A widely applicable Bayesian information criterion[J]. The Journal of Machine Learning Research, 2013, 14(1): 867-897.

Paisley J and Carin L. Nonparametric factor analysis with beta process priors[C]. Proceedings of the 26th ACM Annual International Conference on Machine Learning, 2009: 777-784.

Ando T. Bayesian Model Selection and Statistical Modeling [M]. CRC Press, 2010, 43-96.

Du L, Liu H, and Wang P. Noise robust radar HRRP target recognition based on multitask factor analysis with small training data size[J]. IEEE Transactions on Signal Processing, 2012, 60(7): 3546-3559.

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