۱ Introduction

۲ Rough Set Equivalence Relation

۳ Definition of Rough Sets

۴ Reduction of Attribute

۵ Conclusion

۶ Acknowledgments

References

Radar emitter signal is interfered by various noises during the propagation process, and the signal-to-noise ratio varies widely. Therefore, the features that play a key role in sorting and classifying or identification signals are often difficult to find. In addition, the extracted features are usually subjective and speculative, so it is necessary to select the features that can characterize the maximum difference mode information between the modulated signal categories and the changes in the signal-to-noise ratio. That is, the selected features also have good separability at low SNR. In this paper, based on rough set theory the feature selection method is studied, which lays a foundation for the feature selection of radiation source signals by rough set theory.

Introduction

As the mathematical tool, Rough set theory is proposed by Pawlak. Rough set theory(RST) is used to deal with inaccurate and incomplete information [1,2] . At present, in many fields the theory has been successfully used, such as learning of machine, knowledge discovery, mining of data, and analysis of decision. Among them, the problem of attribute reduction without reducing the ability of distinguishing different objects in information systems has always been one of the core issues among the research of RST [3] . Rough set theory mainly includes related concepts such as equivalence relation, definition of rough set and dependence.