![]() The first strategy is the deceptive interference suppression strategy via the identity (ID) recognition of the PPDR. In the DISM, three interference suppression strategies are proposed for different interference strategies. Therefore, a dynamic interference suppression method (DISM)based on a game model is proposed. Unfortunately, the detection performance is seriously deteriorated by interference. The proximity pulse Doppler radar (PPDR) possesses high range resolution and has been widely used in short-range detection. The experimental results show that compared with the existing mainstream machine learning-based methods, the proposed method greatly shortens the training time on the premise of maintaining a high recognition accuracy. We validate our method on nine types of radar active deception jamming. Last, in the recognition stage, the Hamming distance is adopted to measure the similarity of samples. Second, to achieve a high accuracy and real time of recognition, we employ the hyperdimensional computing to map the input to hyperdimensional space for training and recognition. First, to address the high feature dimensions, we use the sparse representation of time–frequency diagrams as the input, which time–frequency diagrams are constructed by the jamming plus echo signals in multiple consecutive pulse periods. To address the accuracy and real-time of radar active jamming recognition in practical applications, we propose a fast recognition method of radar active deception jamming based on hyperdimensional computing. How to effectively recognize active deception jamming is a challenge of modern radar technology. Similarly, by changing the network input, the original signal is used to replace the echo signal, which improves the accuracy of the jamming recognition in the case of a low JNR.Īctive deception jamming is one of the common means to jam radar signals. Using a CNN to classify the time–frequency image has realized the recognition of a variety of common deceptive jamming techniques. This method fuses three short-time Fourier transform time–frequency graphs disturbed by three consecutive pulse periods into a new graph as the input of the convolutional neural network (CNN). This paper studies the input of jamming recognition networks and proposes an improved intelligent identification method for chirp radar deceptive jamming. The existing neural network-based jamming identification methods still follow the pattern of signal modulation-type identification, so there are fewer types of jamming that can be identified, and the identification accuracy is low in the case of low jamming-to-noise ratios (JNR). Traditional deceptive jamming recognition methods need to extract complex features and artificially set classification thresholds, which is inefficient. ![]() Radar active deceptive jamming based on digital radio frequency memory (DRFM) has a high coherence with the target echo, which confuses the information of the target echo and achieves the effect of hiding the real target. The perception of jamming types is very important for protecting our radar in complex electromagnetic environments. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |