Search Results (5)

A wireless communication intelligent anti-jamming decision algorithm based on Deep Reinforcement Learning (DRL) can gradually optimize communication anti-jamming strategies without prior knowledge by continuously interacting with the jamming environment. This has become one of the hottest research directions in the field of communication anti-jamming. In order to address the joint anti-jamming problem in scenarios with multiple users and without prior knowledge of jamming power, this paper proposes an intelligent anti-jamming decision algorithm for wireless communication based on Multi-Agent Proximal Policy Optimization (MAPPO). This algorithm combines centralized training and decentralized execution (CTDE), allowing each user to make independent decisions while fully leveraging the local information of all users during training. Specifically, the proposed algorithm shares all users’ perceptions, actions, and reward information during the learning phase to obtain a global state. Then, it calculates the value function and advantage function for each user based on this global state and optimizes each user’s independent policy. Each user can complete the anti-jamming decision based solely on local perception results and their independent policy. Meanwhile, MAPPO can handle continuous action spaces, allowing it to gradually approach the optimal value within the communication power range even without prior knowledge of jamming power. Simulation results show that the proposed algorithm exhibits significantly faster convergence speed and higher convergence values compared to Deep Q-Network (DQN), Q-Learning (QL), and random frequency hopping algorithms under frequency sweeping jamming and dynamic probabilistic jamming. Full article

In modern electronic warfare, radar intelligence has become increasingly crucial when dealing with complex interference environments. This paper combines radar agile frequency technology with reinforcement learning to achieve adaptive frequency hopping for radar anti-jamming. Unlike traditional reinforcement learning with Markov decision processes (MDPs), the interaction between radar and jammers occurs within the partially observable Markov decision processes (POMDPs). In this context, the partial observation information available to the agent does not strictly satisfy the Markov property. This paper uses multiple layers of historical observation information to solve this problem. Historical observations can be viewed as a time series, and time-sensitive networks are employed to extract the temporal information embedded within the observations. In addition, the reward function is optimized to facilitate the faster learning of the agent in the jammer sweep environment. This simulation shows that the optimization of the agent state, network structure, and reward function can effectively help the radar to resist jamming. Full article

Frequency modulation continuous wave (FMCW) radio fuze is widely used in military equipment, due to its excellent range and anti-jamming ability. However, the widespread use of radio fuze jammers on modern battlefields poses a serious threat to fuzes. In this study, a classification method of targeting and sweeping frequency jamming signals of FMCW radio fuze based on wavelet packet transform features is proposed, which improves the anti-jamming ability of fuze. The wavelet packet transform of the output signal of the radio fuze detector is used to form a feature vector, which is fed into a support vector machine for targeting and jamming signal classification. The experimental results of the measured data show that the proposed method can achieve a high accuracy rate of classification and identification of FMCW radio fuze targets and frequency sweeping jamming signals. The highest recognition accuracy reached is 98.81% ± 0.0037. The lowest false alarm probability is 0.57% ± 0.0043, which indicates its potential application values in the near future. Full article

Interference classification plays an important role in anti-jamming communication. Although the existing interference signal recognition methods based on deep learning have a higher accuracy than traditional methods, these have poor robustness while rejecting interference signals of unknown classes in interference open-set recognition (OSR). To ensure the classification accuracy of the known classes and the rejection rate of the unknown classes in interference OSR, we propose a new hollow convolution prototype learning (HCPL) in which the inner-dot-based cross-entropy loss (ICE) and the center loss are used to update prototypes to the periphery of the feature space so that the internal space is left for the unknown class samples, and the radius loss is used to reduce the impact of the prototype norm on the rejection rate of unknown classes. Then, a hybrid attention and feature reuse net (HAFRNet) for interference signal classification was designed, which contains a feature reuse structure and hybrid domain attention module (HDAM). A feature reuse structure is a simple DenseNet structure without a transition layer. An HDAM can recalibrate both time-wise and channel-wise feature responses by constructing a global attention matrix automatically. We also carried out simulation experiments on nine interference types, which include single-tone jamming, multitone jamming, periodic Gaussian pulse jamming, frequency hopping jamming, linear sweeping frequency jamming, second sweeping frequency jamming, BPSK modulation jamming, noise frequency modulation jamming and QPSK modulation jamming. The simulation results show that the proposed method has considerable classification accuracy of the known classes and rejection performance of the unknown classes. When the JNR is −10 dB, the classification accuracy of the known classes of the proposed method is 2–7% higher than other algorithms under different openness. When the openness is 0.030, the unknown class rejection performance plateau of the proposed method reaches 0.9883, while GCPL is 0.9403 and CG-Encoder is 0.9869; when the openness is 0.397, the proposed method is more than 0.89, while GCPL is 0.8102 and CG-Encoder is 0.9088. However, the rejection performance of unknown classes of CG-Encoder is much worse than that of the proposed method under low JNR. In addition, the proposed method requires less storage resources and has a lower computational complexity than CG-Encoder. Full article

Currently, frequency-modulated continuous-wave (FMCW) proximity sensors are widely used. However, they suffer from a serious sweep jamming problem, which significantly reduces the ranging performance. To improve its anti-jamming capability, this paper analyzed the response mechanism of a proximity sensor with the existence of real target echo signals and sweep jamming, respectively. Then, a multi-channel harmonic timing sequence detection method, using the spectrum components’ distribution difference between the real echo signals and sweep jamming, is proposed. Moreover, a novel fast Fourier transform (FFT)-based implementation was employed to extract multi-channel harmonic information. Compared with the traditional band-pass filter (BPF) implementation, this novel realization scheme only computes FFT once, in each transmission cycle, which significantly reduced hardware resource consumption and improved the real-time performance of the proximity sensors. The proposed method was implemented and proved to be feasible through the numerical simulations and prototype experiments. The results showed that the proximity sensor utilizing the proposed method had better anti-sweep jamming capability and ranging performance. Full article