Abstract
Modern cognitive electronic reconnaissance methods for radar systems must contend with the complex electromagnetic environments arising from the deployment of multiple signal sources and radar countermeasures, which greatly limit access to the degree of prior information required to enable effective target recognition. The present work addresses this issue by proposing a multiperspective collaborative clustering method for sorting radiation sources based on the multiperspective information of radar signals. In contrast to conventional collaborative training approaches, which are suitable only for semisupervised learning, the proposed multiperspective collaborative clustering method performs unsupervised clustering, cluster label transfer, and dimensionality reduction by linear discriminant analysis iteratively based on the differences between the clustering results obtained from two signal perspectives radiation signal sorting can be conducted in a noncooperative context. The results of comparative experiments demonstrate that the proposed multiperspective sorting method can make full use of the difference information between basic signal characteristics and intrapulse features and thereby improve the accuracy of clustering-based radiation source sorting. Accordingly, the sorting ability of the proposed method is superior to those of other state-of-the-art clustering methods and that of the single-perspective clustering-based sorting method.
Highlights
The rapid development of information technology in recent years has made electronic warfare a key factor in victory at war [1]
The multiperspective collaborative clustering and sorting process was further analyzed by evaluating the changes in the clustering results of the radiation source data of segment 6 from its original distribution, which included 6 radiation sources, to the initial clustering results and those obtained over three successive iterations, whereas discussed above, data is learned from the two different perspectives alternately, and the difference between the learning results of the data from the two perspectives is employed to modify the clustering sorting model
The present study addressed the disadvantages of currently available cognitive electronic reconnaissance methods under the complex electromagnetic environments on modern battle fields by proposing a multiperspective collaborative clustering method for sorting radiation sources based on the multiperspective information of radar signals
Summary
The rapid development of information technology in recent years has made electronic warfare a key factor in victory at war [1]. The use of multiperspective clustering methods has been demonstrated to improve the signal sorting ability of modern cognitive electronic reconnaissance systems for radar signals with characteristics obtained from two perspectives, including basic signal characteristics and intrapulse features. The presently available collaborative training methods are only applicable for learning multiperspective data with partial labels These methods are only suitable for semisupervised learning, which greatly restricts the level of automation required for conducting cognitive electronic reconnaissance in complex electromagnetic environments. The present work addresses this issue by proposing a radar signal sorting and recognition method based on a multidimensional feature expression system and a multiperspective clustering method. Application of the multiperspective clustering method to actual radar signals in a realistically complex electromagnetic environment is demonstrated to improve the ability of cognitive electronic reconnaissance systems to sort radar signals
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