Unknown Radar Signal Research Articles

An reconstruction bidirectional recurrent neural network ‐based deinterleaving method for known radar signals in open‐set scenarios

AbstractIn electronic warfare, radar signal deinterleaving is a critical task. While many researchers have applied deep learning and utilised known radar classes to construct interleaved pulse sequences training sets for deinterleaving models, these models face challenges in distinguishing between known and unknown radar classes in open‐set scenarios. To address this challenge, the authors propose a novel model, the Reconstruction Bidirectional Recurrent Neural Network (RBi‐RNN). RBi‐RNN utilises input reconstruction and employs a joint training strategy incorporating cross‐entropy loss, reconstruction loss, and centre loss. These strategies aim to maximise inter‐class latent representation distances while minimising intra‐class disparities. By incorporating an open‐set recognition method based on extreme value theory, RBi‐RNN adapts to open‐set scenarios. Simulation results demonstrate the superiority of RBi‐RNN over conventional models in both closed‐set and open‐set scenarios. In open‐set scenarios, it successfully discriminates between known and unknown radar signals within interleaved pulse sequences, deinterleaving known radar classes with high stability. The authors lay the foundation for future unsupervised deinterleaving methods designed specifically for unknown radar pulses.

Jamming Selection Method against Unknown Radar Signals based on Deep Learning using Co-occurrence Matrix

최근 전자전에서 딥 러닝을 적용하여 재밍 기법을 선정하는 방법에 대해 연구가 진행되었다. 대부분의 연구는 알고 있는 신호에 대해서 효과적이지만 미상 신호에 대해서는 부적합하며 임베디드시스템에 적용하기에도 부적합 경우가 많다. 이러한 제한 사항을 극복하기 위해 코어커런스 매트릭스를 이용한 딥 러닝 기반의 미상신호에 대한 재밍기법 선정 방법을 제안한다. 제안한 방법은 미상의 신호의 특성을 PDW로부터 코어커런스 매트릭스를 발생하는 특징 추출 알고리즘을 사용한다. 심층 신뢰 신경망을 사용하는 딥 러닝 기법을 적용하였다. 성능 시험은 9개 폴드는 학습과 검증에 사용하고 나머지 1개의 폴드를 미상의 신호로 테스트하는 것을 순차적으로 10번 수행하여 평균하였다. 기존 방법으로는 미상 신호에 대해 재밍 기법을 생성하지 못하지만 제안한 방법은 미상 신호에 대해 98% 이상의 확률로 적절한 재밍기법을 생성하는 성능을 보였다.

Unknown Radar Waveform Recognition Based on Transferred Deep Learning

Radar signals are emerging constantly for urgent task because of its complex patterns and rich working modes. For some radar waveforms with known modulation methods, they can be identified by correlation between radar prior knowledge and the received signals by the reconnaissance receiver. As for the unknown radar signals, how to identify unknown radar waveforms under the condition of limited samples and low signal-to-noise ratio is a challenging problem. Aiming at the learning ability of the deep features of the image by the convolutional neural network (CNN), the reconstructed features of the time-frequency image (TFI) of the known and unknown radar waveform signals have been excavated. A decision fusion unknown radar signal identification model based on transfer deep learning and linear weight decision fusion is designed in this paper. Firstly, the CNN is trained using the known radar signals; Then, based on the transfer learning, the neurons obtained from the multiple underlying the CNN are used to represent the reconstruction feature; Finally, the performance of the single random forest classifier of the original TFI and short- time autocorrelation features images (SAFI)are fused, the identification decision of unknown signals is realized by setting linear weight to the two databases. The recognition rate of unknown new classes for small samples exceeds 80.31%, and the classification accuracy rate for known radar waveform reach more than 99.15%.

The Sorting Methods of Support Vector Clustering Based on Boundary Extraction and Category Utility

According to the problems of low accuracy and high computational complexity in the classification of unknown radar signals, a method of unsupervised Support Vector Clustering (SVC) based on boundary extraction and Category Utility (CU) of unknown radar signals is studied. By analyzing the principle of SVC, only the boundary data of data sets contribute to the support vector extracted. Thus firstly, for reducing the data set, at the same time reducing the computational complexity, the algorithm is designed to extract the boundary data through local normal vector. Then using CU select the optimal parameters. At last distinguish different categories and get the sorting results by Cone Cluster Labelling (CCL) and Depth-First Search (DFS). Through comparing the simulation results, the proposed method which is based on boundary extraction and CU is proved to have turned out quite good time effectiveness, which not only improves the accuracy of classification, but also reduces the computational complexity greatly.

Hierarchical classification of dynamically varying radar pulse repetition interval modulation patterns

The central purpose of passive signal intercept receivers is to perform automatic categorization of unknown radar signals. Currently, there is an urgent need to develop intelligent classification algorithms for these devices due to emerging complexity of radar waveforms. Especially multifunction radars (MFRs) capable of performing several simultaneous tasks by utilizing complex, dynamically varying scheduled waveforms are a major challenge for automatic pattern classification systems. To assist recognition of complex radar emissions in modern intercept receivers, we have developed a novel method to recognize dynamically varying pulse repetition interval (PRI) modulation patterns emitted by MFRs. We use robust feature extraction and classifier design techniques to assist recognition in unpredictable real-world signal environments. We classify received pulse trains hierarchically which allows unambiguous detection of the subpatterns using a sliding window. Accuracy, robustness and reliability of the technique are demonstrated with extensive simulations using both static and dynamically varying PRI modulation patterns.