【作者】 史亚；

【导师】 姬红兵；

【作者基本信息】 西安电子科技大学 ， 模式识别与智能系统， 2011， 硕士

【摘要】 雷达辐射源个体识别在整个雷达对抗中占有重要地位。随着雷达技术的迅速发展及新体制雷达的应用,传统的识别方法逐渐失效。因此,从信号处理角度看,需要发展更为有效的个体特征提取方法,而从模式识别角度看,相应的分类器设计及特征的进一步精简与优化也变得至关重要。本文以两种有效的辐射源个体特征为依托,重点研究了雷达辐射源个体识别中的分类器设计及基于子空间学习的降维技术。在个体特征提取中,分别介绍了基于循环谱零点切片和基于模糊函数切片串联的特征提取方法。在实测雷达辐射源数据上的实验结果充分验证了这两种特征提取方法的有效性,从而为辐射源的个体识别提供了稳定可靠的分类特征。在分类器设计中,首先介绍了六种能够输出后验概率的分类器,继而通过一定的概率融合函数可靠地实现了多分类器的组合。进一步,考虑到实际系统必须能够拒判库外目标,先由后验概率估计出广义置信度,从而设定一定的门限实现了系统对库外目标的拒判功能,最后给出多种评价系统性能的指标。实验结果验证了分类器组合的优势,以及基于广义置信度的拒识算法的可行性。在子空间特征降维中,重点研究了线性判别子空间学习和核判别子空间学习两大降维技术。其中,核子空间方法是通过特定的核技巧对线性子空间方法的扩展。我们将目前已有的各种经典算法归纳为单子空间学习和多子空间学习两大类,并研究了各方法在图像识别与雷达辐射源识别中的应用。实验结果表明,无论是线性方法还是核方法,由于考虑到子空间的互补性,多子空间学习具有更鲁棒的识别性能;而由于算法的数据依赖性,从线性方法到核方法的推广不一定意味着识别率的提升,需要根据实际数据的分布和工程需求选择合适的方法。更多还原

【Abstract】 Radar emitter recognition plays an important role in the ECM. With the rapid development of the radar technology and the utility of new types of radar, exploring more effective feature extraction methods is urgent from the point view of signal processing. From the perspective of pattern recognition, it is also essential to concentrate on the classifier design and feature optimization. Based on the two effective features of radar signals, this thesis deals with classifier design, as well as subspace learning based dimensionality reduction technology, for radar emitter recognition.For feature extraction, zero-slice of the cyclic spectrum and concatenation of the slices in ambiguity function based methods are studied respectively. The experimental results on the real radar data show the validity of the two methods, which provide reliable features for subsequent classification tasks.For classifier design, six types of classifiers which can produce posterior probability are introduced. Then classifier combination can be conveniently realized via proper fusion strategy. To further reject the out-of-database targets, generalized confidence is calculated by the posterior probability, and then thresholds are empirically chosen to implement the rejection operation. Finally, several evaluation indices are given. The experimental results demonstrate the advantage of classifier combination, as well as the feasibility of generalized confidence based rejection methods.For subspace based dimensionality reduction, both linear and kernel discriminant subspace learning are investigated in depth. Therein, kernel-based methods are the extensions of corresponding linear methods via some specific kernel trick. These classical algorithms are summarized into single-subspace learning and multi-subspace learning, which are applied to image recognition and radar emitter recognition. The experimental results indicate that multi-subspace learning algorithms take into account the complementarity of different subspaces and are more robust than single-subspace based methods, no matter linear or kernel cases are concerned. Due to data dependence, kernel extension not always guarantees higher accuracy, so appropriate method needs to be selected according to the actual data distribution and engineering requirements.更多还原