基于t分布扩展概率主成分分析模型的一维距离像识别方法

李彬; 李辉; 郭淞云

doi:10.11999/JEIT161220

基于t分布扩展概率主成分分析模型的一维距离像识别方法

doi: 10.11999/JEIT161220

基金项目:

国家自然科学基金(61571364)，西北工业大学研究生创意创新种子基金(Z2017022)

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- 文章访问数: 1250
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- 被引次数: 0
出版历程
- 收稿日期: 2016-11-10
- 修回日期: 2017-04-06
- 刊出日期: 2017-08-19

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

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)

摘要

摘要: 该文针对概率主成分分析(PPCA)模型用于1维高分辨距离像(HRRP)识别对噪声敏感的问题，对经典PPCA模型进行修正。该方法将基于高斯分布的PPCA模型扩展为基于t分布的PPCA模型，能够综合利用t分布对噪声稳健和PPCA模型自由参数少的特性。同时为了减少目标方位敏感性对HRRP统计建模的影响，进一步将t分布模型扩展为混合概率t分布模型，能够以分布趋同的原则将不同方位帧内具有相同统计特性的HRRP数据进行聚类，减少模型的失配，改善识别性能。模型参数通过期望最大值(EM)算法估计，可提高计算效率。最后，通过贝叶斯规则，以获取的统计特征识别测试数据，仿真结果表明该方法能够提高低信噪比条件下PPCA模型的稳健性。
- 雷达目标识别 /
- 高分辨距离像 /
- 概率主成分分析 /
- t分布 /
- 特征提取
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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参考文献(28)

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