Single Jammer Research Articles

Mainlobe deceptive jammer suppression with DEPC-MIMO radar with joint transmit–receive design

This paper investigates the problem of mainlobe deceptive jammer suppression via joint transmit–receive design in a multiple-input multiple-output (MIMO) radar. At the design stage, a discrete element-pulse coding (DEPC) phase is implemented in both the slow-time pulses and transmit array antennas. After decoding and compensation of the delayed pulses, the true and false targets can be identified in the joint transmit–receive (JTR) spatial frequency domain. Furthermore, to eliminate the false targets, a data-dependent beamforming technique is adopted, where an optimization problem is constructed to maximize the output signal-to-interference-plus-noise ratio (SINR) by optimizing the DEPC coefficients and the weight vector of the receive filter in an alternating way. Specifically, two methods, including the Single Jammer Suppression (SJS)-Alternating Direction Method of Multipliers (ADMM) and Multiple Jammers Suppression (MJS)-Gradient Descent (GD) methods are developed for the cases of a single jammer and multiple jammers, respectively. A series of numerical results are utilized to verify the effectiveness of the proposed methods for mainlobe deceptive jammer suppression.

GNSS blanket jamming classification algorithm based on spatial attention mechanism and residual shrinkage neural network

In the context of an increasingly complex electromagnetic environment, satellite navigation systems have become highly susceptible to jamming. Detecting and classifying jamming has thus become crucial for taking effective anti-jamming measures. This paper addresses the issue that the classification accuracy of blanket jamming declines drastically in low jamming-to-noise ratio (JNR) scenarios. To tackle this challenge, a novel algorithm is proposed that combines the spatial attention mechanism with a residual shrinkage neural network (RSN-SA) to classify ten types of blanket jamming, ranging from single jamming to convolutional compound jamming. Specifically, the proposed algorithm first employs the Fourier Synchrosqueezed Transform to extract time-frequency (TF) domain features from the original jamming signal, generating corresponding TF images. Then, the RSN-SA is employed to identify and classify these images effectively while minimizing the impact of noise-related features. This allows the main parts of the TF images to be focused on, resulting in higher recognition accuracy. Simulation results demonstrate that RSN-SA achieves close to 100% accuracy for six single blanket jamming signals. Moreover, compared with the other five algorithms, RSN-SA effectively enhances the classification accuracy of convolutional compound jamming signals in low JNR scenarios and improves the recognition stability in high JNR scenarios. Overall, the proposed algorithm provides a promising solution for classifying blanket jamming in satellite navigation systems with high accuracy and robustness.

Learning-Based Multi-Domain Anti-Jamming Communication with Unknown Information

Due to the open nature of the wireless channel, wireless networks are vulnerable to jamming attacks. In this paper, we try to solve the anti-jamming problem caused by smart jammers, which can adaptively adjust the jamming channel and the jamming power. The interaction between the legitimate transmitter and the jammers is modeled as a non-zero-sum game. Considering that it is challenging for the transmitter and the jammers to acquire each other’s information, we propose two anti-jamming communication schemes based on the Deep Q-Network (DQN) algorithm and hierarchical learning (HL) algorithm to solve the non-zero-sum game. Specifically, the DQN-based scheme aims to solve the anti-jamming strategies in the frequency domain and the power domain directly, while the HL-based scheme tries to find the optimal mixed strategies for the Nash equilibrium. Simulation results are presented to validate the effectiveness of the proposed schemes. It is shown that the HL-based scheme has a better convergence performance and the DQN-based scheme has a higher converged utility of the transmitter. In the case of a single jammer, the DQN-based scheme achieves 80% of the transmitter’s utility of the no-jamming case, while the HL-based scheme achieves 63%.

A Cognitive Electronic Jamming Decision-Making Method Based on Q-Learning and Ant Colony Fusion Algorithm

In order to improve the efficiency and adaptability of cognitive radar jamming decision-making, a fusion algorithm (Ant-QL) based on ant colony and Q-Learning is proposed in this paper. The algorithm does not rely on a priori information and enhances adaptability through real-time interactions between the jammer and the target radar. At the same time, it can be applied to single jammer and multiple jammer countermeasure scenarios with high jamming effects. First, traditional Q-Learning and DQN algorithms are discussed, and a radar jamming decision-making model is built for the simulation verification of each algorithm. Then, an improved Q-Learning algorithm is proposed to address the shortcomings of both algorithms. By introducing the pheromone mechanism of ant colony algorithms in Q-Learning and using the ε-greedy algorithm to balance the contradictory relationship between exploration and exploitation, the algorithm greatly avoids falling into a local optimum, thus accelerating the convergence speed of the algorithm with good stability and robustness in the convergence process. In order to better adapt to the cluster countermeasure environment in future battlefields, the algorithm and model are extended to cluster cooperative jamming decision-making. We map each jammer in the cluster to an intelligent ant searching for the optimal path, and multiple jammers interact with each other to obtain information. During the process of confrontation, the method greatly improves the convergence speed and stability and reduces the need for hardware and power resources of the jammer. Assuming that the number of jammers is three, the experimental simulation results of the convergence speed of the Ant-QL algorithm improve by 85.4%, 80.56% and 72% compared with the Q-Learning, DQN and improved Q-Learning algorithms, respectively. During the convergence process, the Ant-QL algorithm is very stable and efficient, and the algorithm complexity is low. After the algorithms converge, the average response times of the four algorithms are 6.99 × 10−4 s, 2.234 × 10−3 s, 2.21 × 10−4 s and 1.7 × 10−4 s, respectively. The results show that the improved Q-Learning algorithm and Ant-QL algorithm also have more advantages in terms of average response time after convergence.

Recurrent-Neural-Network-Based Anti-Jamming Framework for Defense Against Multiple Jamming Policies

Conventional anti-jamming methods mainly focus on preventing single jammer attacks with an invariant jamming policy or jamming attacks from multiple jammers with similar jamming policies. These anti-jamming methods are ineffective against a single jammer following several different jamming policies or multiple jammers with distinct policies. Therefore, this paper proposes an anti-jamming method that can adapt its policy to the current jamming attack. Moreover, for the multiple jammers scenario, an anti-jamming method that estimates the future occupied channels using the jammers’ occupied channels in previous time slots is proposed. In both single and multiple jammers scenarios, the interaction between the users and jammers is modeled using recurrent neural networks (RNN)s. The performance of the proposed anti-jamming methods is evaluated by calculating the users’ successful transmission rate (STR) and ergodic rate (ER), and compared to a baseline based on deep Q-learning (DQL). Simulation results show that for the single jammer scenario, all the considered jamming policies are perfectly detected and a high STR and ER are maintained. Moreover, when 70% of the spectrum is under jamming attacks from multiple jammers, the proposed method achieves a STR and ER greater than 75% and 80%, respectively. These values reach 90% when 30% of the spectrum is under jamming attacks. In addition, the proposed anti-jamming methods significantly outperform the DQL method for all the considered jamming scenarios.

An Electronic Jamming Method Based on a Distributed Information Sharing Mechanism

In an electronic jamming system, the ability to adequately perceive information determines the effectiveness of an electronic countermeasures strategy. This paper proposes a new method based on the combination of a multi-agent electronic jammer and an information sharing mechanism. With the development of intelligent technology and deep learning, these technologies have been applied in electronic countermeasure game systems. Introducing intelligent technology into the electronic confrontation system can greatly improve decision-making efficiency. At the same time, a multi-agent electronic countermeasure cooperative system based on the information sharing method can break through the limited information perception capabilities of a single agent, thereby greatly improving the survivability of jamming systems in electronic warfare. Experimental results show that our method requires a lower jamming-to-signal ratio than the single jammer method to achieve effective electronic jamming. In addition, the electronic jamming parameters can be updated automatically as the external electromagnetic environment changes quickly, realizing a more intelligent electronic jamming system.

A resource allocation algorithm for communication interference

Aiming at the problem of interference resource allocation in communication, an interference resource allocation algorithm based on Breadth First Search (BFS) is proposed in this paper. In the jamming problem, the jammer can interfere with the target on a number of frequencies. Affected by some factors, the jamming frequency of a single jammer needs to be concentrated as much as possible. The frequency point set is divided into multiple frequency bands. The signals in the same frequency band are considered to be close to each other. The optimization problem is transformed into allocating the least jamming frequency band of jammer. The optimal interference resource allocation method of the model can be obtained by BFS. Simulation results show that the algorithm has significant index advantages over the manual selection of methods.

Deception electronic counter‐countermeasure applicable to multiple jammer sources in distributed multiple‐radar system

Deception jamming poses a fatal threat to the radar performance, and its jamming performance has been greatly improved through networking. However, available deception electronic counter-countermeasure (ECCM) methods are mostly developed in the scenario of a single jamming source. Therefore, it is of great importance to develop deception ECCM methods that can counter multiple jammers. This work proposes a deception jamming discrimination method applicable to multiple jammer sources in distributed multiple-radar system, based on the correlation coefficient between different target-received signal vectors. The false targets generated by one jammer are highly correlated, while a radar target is uncorrelated with either other radar targets or false targets. For the estimated correlation coefficient, the Fisher z-transformation is applied to determine the correlation between targets with the Z-test. Since the jammer usually generates multiple false targets at once to enhance its deceptions, a target can be judged as a false target when multiple targets are highly correlated with it; otherwise a radar target is declared. More importantly, compared with existing methods, no prior information is required here. Simulation results corroborate the feasibility of the proposed discrimination method and its performance due to the influence of jamming-to-noise ratio. The spatial correlation of radar targets and geometry conditions are covered.

AIGSA-based multi-jammer localization in wireless networks

Jamming attacks, which exploit the shared and openness nature of the wireless mediums, are one of the major threats to wireless networks since they may significantly degrade the performance of or even corrupt wireless networks by continuously preventing wireless channels from serving intended users. To facilitate conducting anti-jamming countermeasures, a few single jammer localization algorithms have been introduced and evaluated. In contrast, only few efforts have been made for locating multiple jammers, for instance X-ray and Cluster algorithms. They are developed based on the assumption that a jammer’s affected area is roughly a circle; however, their localization accuracy is unsatisfactory if multiple jammers’ affected areas are overlapped. In this backdrop, a Multi-jammer Localization Algorithm based on Alternating Iteration and Gravitational Search Algorithm (MLA-AIGSA) is put forward in this paper. On the contrary to existing proposals, MLA-AIGSA neither relies on the mapping between the received signal power and two nodes’ mutual distance nor counts on the ideal assumption about the shape of a jammer’s affected area. MLA-AIGSA contains two steps, i.e. estimating the number of jammers and Multi-jammer localization. At first, Region Growth Algorithm (RGA) is adopted to derive the number of jammers based on collected Received Jamming Signal Strength (RJSS) values. Then, Alternating Iteration (AIt) and GSA are combined to estimate and refine multiple jammers’ positions in an iterative way. The overall time complexity of MLA-AIGSA is in quadratic time to the number of the particles and is in linear time to the number of AIt’s and GSA’s iterations. Extensive experiments are conducted to validate MLA-AIGSA’s correctness and effectiveness, and the results show that MLA-AIGSA’s average localization error (around 3 m) is nearly 70% lower than state-of-the-art proposals, including X-ray (17 m) and Cluster (10 m) algorithms.

JRNet: Jamming Recognition Networks for Radar Compound Suppression Jamming Signals

As electromagnetic environments in battlefields are more and more complex, there are more kinds of suppression jamming noise including both single jamming signals and compound jamming signals. Thus, suppression jamming recognition especially for compound jamming signals is becoming vital and challenging. Whereas conventional methods are prone to owning the low recognition accuracy and the high computational complexity, especially under low jamming-to-noise ratio (JNR) conditions. In this paper, a novel jamming recognition network (JRNet) based on robust power-spectrum features is proposed to recognize ten kinds of suppression jamming signals including four single jamming patterns and six compound jamming patterns. The proposed method combines the significant representative power of the JRNet and distinguished power-spectrum features of jamming signals to promote the recognition performance. By integrating residual blocks and asymmetric convolution blocks, the JRNet is capable to address the degradation problem and enhance the recognition ability for subtle features. The simulation results show that the overall recognition accuracy of the proposed method is more than 90% even when the JNR is −18 dB and is close to 100% at −6 dB. Compared with five comparison methods in recent literatures, the proposed JRNet achieves better and stable recognition performance especially under low JNR conditions with relatively less storage source and a bit more FLOPs and inference time.

Enhanced Secrecy Performance of Multihop IoT Networks With Cooperative Hybrid-Duplex Jamming

As the number of connected devices is exponentially increasing, security in Internet of Things (IoT) networks presents a major challenge. Accordingly, in this work we investigate the secrecy performance of multihop IoT networks assuming that each node is equipped with only two antennas, and can operate in both Half-Duplex (HD) and Full-Duplex (FD) modes. Moreover, we propose an FD Cooperative Jamming (CJ) scheme to provide higher security against randomly located eavesdroppers, where each information symbol is protected with two jamming signals by its two neighbouring nodes, one of which is the FD receiver. We demonstrate that under a total power constraint, the proposed FD-CJ scheme significantly outperforms the conventional FD Single Jamming (FD-SJ) approach, where only the receiving node acts as a jammer, especially when the number of hops is larger than two. Moreover, when the Channel State Information (CSI) is available at the transmitter, and transmit beamforming is applied, our results demonstrate that at low Signal-to-Noise Ratio (SNR), higher secrecy performance is obtained if the receiving node operates in HD and allocates both antennas for data reception, leaving only a single jammer active; while at high SNR, a significant secrecy enhancement can be achieved with FD jamming. Our proposed FD-CJ scheme is found to demonstrate a great resilience over multihop networks, as only a marginal performance loss is experienced as the number of hops increases. For each case, an integral closed-form expression is derived for the secrecy outage probability, and verified by Monte Carlo simulations.

Deceptive jamming against multi‐channel SAR‐GMTI

Aimed at the problem that the deceptive jamming against synthetic aperture radar ground moving target indication generated by single jammer cannot work effectively in multi-channel SAR, a novel deceptive jamming against multi-channel SAR-GMTI based on two jammers is studied. First, signal model of the novel deceptive jamming is built. Then, based on three-channel clutter suppression and interferometry processing, the amplitude and interferometric phase of deceptive moving target generated by the novel deceptive jamming are derived. Last, simulation experiment validates that the novel deceptive jamming can generate false moving targets with the controllable radial velocity and located azimuth position in the multi-channel SAR.

Locating Multiple GPS Jammers Using Networked UAVs

Recent technologies, such as, Internet of Things and cloud services, increases the usage of small and low-cost networked unmanned aerial vehicles (UAVs), which needs to be robust against malicious global positioning system (GPS) attacks. Due to the availability of low-cost GPS jammers in the commercial market, there has been a rising risk of multiple jammers and not just one. However, it is challenging to locate multiple jammers because the traditional jammer localization via multilateration is applicable for only a single jammer case. Also, during a jamming attack, the positioning capability of an on-board GPS receiver is compromised given its inability to track GPS signals. We propose a simultaneous localization of multiple jammers and receivers (SLMR) algorithm by analyzing the variation in the front-end signal power recorded by the GPS receivers on-board a network of UAVs. Our algorithm not only locates multiple jammers but also utilizes these malicious sources as additional navigation signals for positioning the UAVs. We design a Gaussian mixture probability hypothesis density filter over a graph framework, which is optimized using a Levenberg–Marquardt minimizer. Using a simulated experimental setup, we validate the convergence and localization accuracy of our SLMR algorithm for various cases, including attacks with a single jammer, multiple jammers, and a varying number of jammers. We also demonstrate that our SLMR algorithm is able to simultaneously locate multiple jammers and UAVs, even for a larger transmitted power of the jammers.

A Barrage Noise Jamming Method Based on Double Jammers Against Three Channel SAR GMTI

Many jamming methods have been proposed against displaced phase center antenna (DPCA) process, which is a type of three-channel synthetic aperture radar (SAR) ground moving target indication. However, two important problems faced by the jammer are why the jamming based on a single jammer is suppressed and how to generate noise in the whole DPCA image while keeping its easy realization. By analyzing a jamming geometric model based on a single jammer, the reason is that the phase of the jamming image in different antennas is only relative to the jamming geometric model, which contains the azimuth position of the jammer and jamming in the SAR image. Then, the jamming area is classified as an enhanced area and a weakened area after DPCA processing. Moreover, the jamming in the weakened area will be suppressed. To overcome this disadvantage, a barrage noise jamming method based on double jammers is proposed. By controlling the azimuth distance between the jammers, the noise can cover the whole DPCA image. Compared with the deception jamming method based on multiple coherent jammers, the phase and amplitude of the jamming signal do not need to be accurately controlled. Finally, the validity of the proposed method is verified by simulation experiments.

Multiobjective Cognitive Cooperative Jamming Decision-Making Method Based on Tabu Search-Artificial Bee Colony Algorithm

For the future information confrontation, a single jamming mode is not effective due to the complex electromagnetic environment. Selecting the appropriate jamming decision to coordinately allocate the jamming resources is the development direction of the electronic countermeasures. Most of the existing studies about jamming decision only pay attention to the jamming benefits, while ignoring the jamming cost. In addition, the conventional artificial bee colony algorithm takes too many iterations, and the improved ant colony (IAC) algorithm is easy to fall into the local optimal solution. Against the issue, this paper introduces the concept of jamming cost in the cognitive collaborative jamming decision model and refines it as a multiobjective one. Furthermore, this paper proposes a tabu search-artificial bee colony (TSABC) algorithm to cognitive cooperative-jamming decision. It introduces the tabu list into the artificial bee colony (ABC) algorithm and stores the solution that has not been updated after a certain number of searches into the tabu list to avoid meeting them when generating a new solution, so that this algorithm reduces the unnecessary iterative process, and it is not easy to fall into a local optimum. Simulation results show that the search ability and probability of finding the optimal solution of the new algorithm are better than the other two. It has better robustness, which is better in the “one-to-many” jamming mode.

ECCM Scheme against Interrupted Sampling Repeater Jammer Based on Parameter-Adjusted Waveform Design.

Interrupted sampling repeater jamming (ISRJ) is an effective way of deceiving coherent radar sensors, especially for linear frequency modulated (LFM) radar. In this paper, for a simplified scenario with a single jammer, we propose a dynamic electronic counter-counter measure (ECCM) scheme based on jammer parameter estimation and transmitted signal design. Firstly, the LFM waveform is transmitted to estimate the main jamming parameters by investigating the discontinuousness of the ISRJ’s time-frequency (TF) characteristics. Then, a parameter-adjusted intra-pulse frequency coded signal, whose ISRJ signal after matched filtering only forms a single false target, is designed adaptively according to the estimated parameters, i.e., sampling interval, sampling duration and repeater times. Ultimately, for typical jamming scenes with different jamming signal ratio (JSR) and duty cycle, we propose two particular ISRJ suppression approaches. Simulation results validate the effective performance of the proposed scheme for countering the ISRJ, and the trade-off relationship between the two approaches is demonstrated.

Study on the Nonstationary jammer Suppression for DSSS Receiver

Using TVAR(Time Varying AR) model and adaptive notch filter is a new method for the non-stationary jammer suppression in DSSS(Direct Sequence Spread Spectrum). The performance of TVAR model for IF (Instantaneous Frequency) estimation will be affected by some factors such as basis functions. The DSSS (Direct Sequence Spread Spectrum) is sensitive to non-stationary jammer, especially the LFM (Frequency Modulation) jammer. Focusing on this problem, the optimal basis function of TVAR model for the IF estimation of the LFM signal is obtained in this paper. Besides the depth and width of notching, the phase properties of notch filter affect the SINR of correlation output to the narrow band jammer suppression in DSSS, in response to the problem the closed solution of correlation output SINR improvement has been derived when a single frequency jammer passes through direct IIR notch filter, and its performance has been compared with those of five coefficient FIR filters. Later, a novel method for LFM jammer suppression based on Fourier basis TVAR model and direct IIR notch filter is proposed. The simulation results show the effectiveness of the proposed method. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4733

Collaborative Anti-jamming in Cognitive Radio Networks Using Minimax-Q Learning

Cognitive radio is an efficient technique for realization of dynamic spectrum access. Since in the cognitive radio network (CRN) environment, the secondary users (SUs) are susceptible to the random jammers, the security issue of the SU's channel access becomes crucial for the CRN framework. The rapidly varying spectrum dynamics of CRN along with the jammer's actions leads to challenging scenario. Stochastic zero-sum game and Markov decision process (MDP) are generally used to model the scenario concerned. To learn the channel dynamics and the jammer's strategy the SUs use reinforcement learning (RL) algorithms, like Minimax-Q learning. In this paper, we have proposed the multi-agent multi-band collaborative anti-jamming among the SUs to combat single jammer using the Minimax-Q learning algorithm. The SUs collaborate via sharing the policies or episodes. Here, we have shown that the sharing of the learned policies or episodes enhances the learning probability of SUs about the jammer's strategies but reward reduces as the cost of communication increases. Simulation results show improvement in learning probability of SU by using collaborative anti-jamming using Minimax-Q learning over single SU fighting the jammer scenario.

Secure Transmission in Downlink Cellular Network with a Cooperative Jammer

In this paper, a novel transmission scheme is proposed to improve the security of downlink cellular network. The confidential message intended to one of K mobile users (MUs) should be securely kept from the undesired recipients. In this work, the K-1 remaining users are regarded as potential eavesdroppers and called as internal eavesdroppers. For the security enhancement, we propose an adaptation of a single cooperative jammer (CJ) to increase the ambiguity at all malicious users by distracting them with artificial interference. With the help of CJ, we derive the optimal joint transmission scheme by beamforming solution to maximize the secrecy rate of the intended user. Numerical results show the performance improvement of the proposed scheme.

Wireless Secrecy Regions With Friendly Jamming

Inspired by recent results on information-theoretic security, we consider the transmission of confidential messages over wireless networks, in which the legitimate communication partners are aided by friendly jammers. We characterize the security level of a confined region in a quasi-static fading environment by computing the probability of secrecy outage in connection with two new measures of physical-layer security: the jamming coverage and the jamming efficiency. Our analysis for various jamming strategies based on different levels of channel state information provides insight into the design of optimal jamming configurations and shows that a single jammer is not sufficient to maximize both figures of merit simultaneously. Moreover, a single jammer requires full channel state information to provide security gains in the vicinity of the legitimate receiver.