雷达目标一维距离像识别中的最优因式分析子空间法

周代英; 杨万麟

doi:10.3724/SP.J.1146.2006.00268

雷达目标一维距离像识别中的最优因式分析子空间法

doi: 10.3724/SP.J.1146.2006.00268

计量
- 文章访问数: 2947
- HTML全文浏览量: 61
- PDF下载量: 688
- 被引次数: 0
出版历程
- 收稿日期: 2006-03-09
- 修回日期: 2006-09-13
- 刊出日期: 2007-10-19

Recognition of Radar Target Based on Optimal Factor Analysis Subspace Using Rangeprofile

摘要

摘要: 该文提出一种基于因式分析子空间进行特征提取的雷达目标识别方法。通过对目标训练样本集进行因式分析，在最大似然估计准则和最小错误分类率准则下建立最优因式分析子空间，利用因式分析子空间能够增强同类目标特征之间的相关性，提高同类目标特征的聚集度，从而改善目标识别性能。对三类飞机目标的仿真实验结果表明了方法的有效性。
- 雷达目标识别;一维距离像;最优因式分析子空间
Abstract: A novel approach of radar target recognition is proposed in this paper. For each target class, according to maximum likelihood rule and minimum classification error rate rule, an optimal factor analysis subspace is constructed by factor analysis on the training sample sets of target rangeprofiles. The optimal factor analysis subspace can produce an enhancement of correlation existing in the features of same target class. Such an enhancement provides higher clustering degree of the same class features. Thus, the factor analysis subspace is used to extract the feature of target for improving the accuracy of classification. The experimental results of three kinds of airplane targets demonstrate the efficiency of the approach proposed in this paper.

HTML全文

参考文献(1)

Rihaczek A W and Hershkowitz S J. Theory and Practice of Radar Target Identification[M]. Norwood, MA: Artech House, 2000: 168-181.[2]Hudson S and Psaltis D. Correlation filters for aircraft identification from radar range profiles[J].IEEE Trans. on Aerospace and Electronic Systems.1993, 29(3):741-748[3]Nelson D E, Starzyk J A, and Ensley D D. Iterated wavelet transformation and discrimination for HRR radar target recognition[J]. IEEE Trans. on System Man and Cybernetics-part: system and humans, 2002, 33(1): 52-57.[4]Jacobs S P and Sullivan J A. Automatic target recognition using sequences of high range resolution radar rangeprofiles[J].IEEE Trans. on Aerospace and Electronic Systems.2000, 36(2):364-381[5]Novak L M and Owirka G J. Radar target recognition using an eigen-image approach[C]. IEEE International Radar Conference, America, 1994: 129-131.[6]Liu B Y and Yang W L. Radar target recognition using canonical transformation to extract features[J].Proc. SPIE.1998, 3545:368-371[7]Hinton G, Dayan P, and Revow M. Modeling the manifolds of images of handwritten digits[J].IEEE Trans. on Neural Networks.1997, 8(1):65-74[8]Kambhatla N and Leen T K. Fast nonlinear dimension reduction. In Advances in Neural Information Processing Systems[C]. Cowan J, Tesauro G, and Alspector J, Eds. San Mateo. CA: Morgan Kaufman, 1994, 6: 152-159.[9]Itagaki A.[J].Takashima M and Ashino Y, et al.. Fuzzy inference systems by genetic algorithm and factor analysis modeling for multivariate complex systems[C]. IEEE Symposium on Emerging Technologies Factory Automation, Europe.1994,:-[10]Ghahramani Z and Hinton G. The EM algorithm for mixtures of factor analyzers[R]. Uni. Toronto, Toroto, Ont., Cananda, Tech. Rep. CRG-TR-96-1, 1996.[11]Saul L K and Rahim M G. Maximum likelihood and Minimum classification error factor analysis for automatic speech recognition[J].IEEE Trans. on Speech and Audio Processing.2000, 8(2):115-125

施引文献

资源附件(0)

访问统计