۱ Introduction

۲ Analysis of Fuzzy Entropy

۳ Fuzzy Entropy Feature Extraction Of Radar Signals

۴ Conclusion

۵ Acknowledgments

۶ References

With the rapid application of the new radar system, radar signal sorting based on conventional parameters has not achieved the desired results. Therefore, new features need to be extracted for signal sorting. In response to this problem, the paper proposes to use the entropy theory to extract the fuzzy entropy characteristics of the radar signal to determine the uncertainty of the radar source signal. Experiments with test signals show that the fuzzy entropy characteristics are less affected by noise over a wide range of SNR.

Introduction

At present, in order to solve the problem of radar signal sorting in the modern electronic countermeasure environment, the sorting method combined with the intra-pulse feature extraction of radar signals has become a research hotspot. By analyzing the intrapulse data of radar signals, this method studies the intentional modulation features and unintentional modulation features in the pulse, constructs a new radar signal sorting model based on short-term observation data, expands the parameter space, and reduces the overlapping probability of multi-parameter space. To achieve fast and accurate sorting identification signals. Devijver [1] defined generalized feature extraction as: extracting the most relevant information (or features) from the original data, and the proposed information should have the property of minimizing intra-class and enhanced inter-model variability. For the in-pulse feature extraction, the feature with the intra-class aggregation and inter-class separation is extracted from the intrapulse data, so that the features between the signals are clearly distinguished, so as to prepare for sorting and identifying the radar signal.