Abstract
There has been growing interest in the classification of interference types in communication systems, especially under large samples and unknown interference, which severely restrict anti-jamming performance of the system. In this paper, we present two signal approximation algorithms for classification restricted different conditions, and the transform learning label consistency (TLLC) is embedded into the evaluation owing to the imperfect performance for classification and feature library. First, the interference signals are converted into the signal feature space, and then the interference processing and feature extraction are conducted based on the Hilbert signal space theory. Second, the projection approximation (PA) for signal approximation is used to approximate the unknown interference, and the restricted projection property is demonstrated as well. Furthermore, in order to ease the restrictions, the sparse approximation (SA) for interference signals is demonstrated. Moreover, an unsupervised learning method and the unknown interference classifier are proposed based on the self-organizing map (SOM) neural network. Based on l1 minimization functions, we improve the accuracy of TLLC with sparse approximation, which is more suitable for general interference signals. Finally, the simulation results demonstrate that, compared with the traditional classification method, the proposed method improves the classification accuracy of known interference by 3.44%. In this case, the overall accuracy is close to that of the supervised learning method, and the speed of processing interference is increased by more than 10 times. When the SNR reaches 5 dB, the accuracy of unknown interference classification exceeds 94%. Finally, yet importantly, owing to the imperfect performance for classification and feature library at present, we acquire the final accuracy for them at 92.23% by intervening measures, and the time availability also has been obtained advantages on signal processing.
Highlights
Tactical components are always in a complex battlefield environment, and it is highly vulnerable to malicious electromagnetic interference [1]
We chose to compare with support vector machine (SVM) and probabilistic neural network (PNN)
In order to satisfy the requirement for accuracy or the feature library in practice, the transform learning label consistency (TLLC) can gain effective improvement for unknown interference multi-classification
Summary
Tactical components are always in a complex battlefield environment, and it is highly vulnerable to malicious electromagnetic interference [1]. According to the characteristics of the interference signals, many researchers utilize intelligent methods [4]–[6] to autonomously perform feature detection and classification, whereas they mainly rely on the support of the original dataset and result in the limited processing speed in real-time applications. Improved technologies are used to analyze the sparsity features in some aspects, including sparse decomposition [16], [17] and matching pursuit [18], and achieve the accurate recovery of signals In these situations, the difficulty of hardware implementation is reduced to some extent by combining some simple classification methods. By utilizing the characteristics of the projection theorem (PT) and sparse approximation (SA), the improved SOM classification method based on signal feature space is proposed to process the unknown interference, and the specific processing flow of the classifier is designed. Where f0 is the center frequency, t is the delay, and k adjusts the frequency
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