Design And Implementation Of Radar Source Sorting Method Based On Machine Learning

Radar radiation source sorting is an important part of the electronic reconnaissance link.The effect of signal sorting directly determines the development of the electronic war situation.With the increasing development of radar technology,the radar system has gradually diversified and complicated.The current electromagnetic environment is becoming more and more complex,and the traditional radar sorting technology is far from meeting the needs of current electronic warfare.In view of this situation,this thesis studies how to use machine learning algorithms to design reasonable and effective radar radiation source sorting methods.The main contributions and research contents of this thesis are as follows:This thesis studies the sorting of radar radiation sources under the condition of class imbalance,and proposes a sorting method based on balanced random forest(BRF).Firstly,the concept of class imbalance and the technical means to solve this problem are introduced.Then,for the problem of poor performance of traditional machine learning algorithms in predicting minority classes,the BRF algorithm is studied further,the BRF algorithm was creatively introduced into the radar source sorting task,and a radar source sorting method based on BRF was proposed.Finally,the effects of support vector machine,K-nearest neighbor method and BRF applied to radar radiation source sorting are analyzed by experiments.It shows that the sorting method based on BRF can not only significantly improve the sorting effect of radar radiation sources in minority class,but also improve the overall sorting performance under the condition of class imbalance.This thesis continues to study the sorting of unknown radar radiation sources under the condition of lack of prior knowledge,and proposes a sorting method based on density-based spatial clustering of applications with noise(DBSCAN).Firstly,different types of clustering algorithms are introduced,and their advantages,disadvantages,and scope of application are analyzed.The DBSCAN algorithm based on density clustering is studied in detail,and the unknown radar source sorting method based on DBSCAN is proposed.The effects of K-means clustering,balanced iterative reducing and clustering using hierarchies,spectral clustering,and DBSCAN on the sorting of unknown radar radiation sources are analyzed and compared through experiments.It shows that the sorting method based on DBSCAN has an excellent sorting effect for radar radiation sources of different modulation methods,and has a high sorting accuracy.