As the use of radar and radar jammers increases, a radar device is likely to face interference from jammers or other radar devices. Traditional phase-coded pulse compression radar devices are widely used, but these tools struggle to overcome jamming and mutual interference. To solve this problem, we propose a novel chaotic-encrypted pulse compression radar signal based on radar identity (ID). Each radar has its own ID, which is encrypted with different chaotic binary sequences in every pulse period. The ambiguity function calculated for the coded radar signal is thumbtack-shaped, indicating that the signal has a good resolution. The received signal is used to range and decrypt in two channels: the range channel and the radar ID channel. The signals of the two channels are analyzed separately. Analyses of anti-barrage jamming and anti-mutual interference show that both channels perform well in terms of antijamming, while the antijamming ability is influenced by the processing gain, bit error rate (BER) and correlation function. In addition, the dual-channel antijamming method further improves the radar antijamming ability. The simulation result verifies the strong antijamming ability and high range resolution of the proposed radar signal, and the proposed antijamming method performs much better than the traditional phase-coded pulse compression radar signal in the antijamming scenario.

The fuze system is usually affected by jamming signals, especially the digital radio frequency memory (DRFM) based jamming signals. In this letter, we propose a recognition method for the fuze DRFM jamming signals based on hybrid attention module (HAM) and Transformer. Specifically, we first build a backbone network with the combination of the convolutional neural network (CNN) and the Transformer that can better extract the global features of the feature maps. To bridge the CNN and the Transformer, we design a convolutional embedding module (CEM). Moreover, a lightweight HAM is utilized to overcome the missing of the position information in the Transformer and the high complexity of the method. Simulation results show that the proposed recognition method achieves a better trade-off between accuracy and complexity.

The false target caused by interference obfuscates the real target detection of the radio frequency proximity sensor (RFPS). In this article, an adaptive false-target recognition method is proposed to classify the target echo and the false target. Two strategies are designed for the recognition of false targets caused by different interference. In the first target recognition strategy, each RFPS is assigned an identity (ID) with chaotic encryption. For a better performance against the false target caused by barrage interference, joint features of the range profiles and Fourier spectrum are extracted in the second strategy. The Naïve Bayesian classifier is applied and it performs well against barrage interference. Since the two strategies perform differently under different interference, the game model between the RFPS and the interference is established to improve the false-target recognition stability of the RFPS. Based on the game model, the payoffs of the two strategies are evaluated, and thus the false-target recognition strategies can be adaptively allocated according to their payoffs. The simulation result verifies the improved stability of the proposed method under different interference strategies and signal-to-interference-plus-noise ratios (SINRs). The false-target recognition performance of the RFPS will be significantly improved with the proposed method.