Anti-jamming Algorithm Research Articles

An efficient approach for anti-jamming in IRNSS receivers using improved PSO based parametric wavelet packet thresholding

The Indian Regional Navigation Satellite System provides accurate positioning service to the users within and around India, extending up to 1500 km. However, when a receiver encounters a Continuous Wave Interference, its positioning accuracy degrades, or sometimes it even fails to work. Wavelet Packet Transform (WPT) is the most widely used technique for anti-jamming in Global Navigation Satellite System receivers. But the conventional method suffers from threshold drifting and employs inflexible thresholding functions. So, to address these issues, an efficient approach using Improved Particle Swarm Optimization based Parametric Wavelet Packet Thresholding (IPSO-PWPT) is proposed. Firstly, a new parameter adaptive thresholding function is constructed. Then, a new form of inertia weight is presented to enhance the performance of PSO. Later, IPSO is used to optimize the key parameters of WPT. Finally, the implementation of the IPSO-PWPT anti-jamming algorithm is discussed. The performance of the proposed technique is evaluated for various performance metrics in four jamming environments. The evaluation results manifest the proposed method’s efficacy compared to the conventional WPT in terms of anti-jamming capability. Also, the results show the ability of the new thresholding function to process various signals effectively. Furthermore, the findings reveal that the improved PSO outperforms the variants of PSO.

Anti-Jamming Method and Implementation for GNSS Receiver Based on Array Antenna Rotation

Global navigation satellite system (GNSS) array antenna receivers are effective for suppressing wideband jamming. However, its anti-jamming performance decreases sharply when the number of wideband interference surpasses the number of array elements. Since a large number of jammers are often used in navigation countermeasures, it is crucial to keep array antenna receivers available in such conditions. Aiming at this issue, two main tasks were performed in this research and are presented in this paper: Firstly, the direction sensitivity of the sup-freedom anti-jamming performance is revealed and an anti-jamming method for array antenna receivers based on antenna rotation is proposed. Secondly, in order to determine the optimal rotation angle rapidly, a variable-step iteration algorithm based on gradient descent is proposed. Theoretical analysis and simulation show the effectiveness of the proposed anti-jamming method and the efficiency of the implementation algorithm. In a typical airborne scenario with a maximum azimuth difference of 90°, the anti-jamming ability of the proposed method improved by 21~26 dB and 5~10 dB for arrays adopting the PI (power inversion) and MVDR (minimum variance distortionless response) algorithms, respectively. The iterative efficiency improved by 78.35–99.63% in comparison with a traversal of 0.1° search resolution. The proposed method and algorithm are not limited to airborne scenarios and they might be influential to anti-jamming algorithms in the data domain.

Anti-Jamming Path Selection Method in a Wireless Communication Network Based on Dyna-Q

Aiming at efficiently establishing the optimal transmission path in a wireless communication network in a malicious jamming environment, this paper proposes an anti-jamming algorithm based on Dyna-Q. Based on previous observations of the environment, the algorithm selects the optimal sequential node by searching the Q table to reduce the packet loss rate. The algorithm can accelerate the updating of the Q table based on previous experience. The Q table converges to the optimal value quickly. This is beneficial for the optimal selection of subsequent nodes. Simulation results show that the proposed algorithm has the advantage of faster convergence speed compared with the model-free reinforcement learning algorithm.

High-Resolution Spectral Estimation for Continuous Wave Jamming Mitigation of GNSS Signals in Autonomous Vehicles

Future autonomous vehicles will rely mainly on global navigation satellite system (GNSS) receivers for positioning services. However, GNSS cannot maintain an accurate, continuous and reliable navigation solution in the presence of jamming. The widespread availability of in-car jammers or personal privacy devices (PPDs) made GNSS receivers an attractive target for signal jamming. Jammers not only jeopardize current positioning and timing services; they can indeed endanger the safety of the evolving intelligent transportation, autonomous road vehicles and critical infrastructure. The disruption of GNSS signal in some applications, especially safety-critical ones can lead to crucial consequences and risks such as loss of time synchronization and potential loss of life, to name a few. Thus, it is essential for future autonomous vehicles and transport service providers to deploy reliable GNSS anti-jamming techniques. This paper introduces a novel anti-jamming technique based on a high-resolution spectral estimation that utilizes fast orthogonal search (FOS) algorithm in which the jamming signal is modeled using a set of candidate functions and then eliminated from the received signal. The performance of the proposed method is assessed using experiments obtained from Spirent_TM GSS6700 simulation system. The jamming signal is either obtained from a real jammer or simulated using interference signal generator that is connected to Spirent_TM system. The results showed that the developed anti-jamming algorithm was able to successfully suppress the continuous wave (CW) interference signal, thus, the performance of the acquisition, tracking and navigation modules within a GPS software receiver were all enhanced.

A new blind anti‐jamming algorithm based on a novel antenna array design

The next-generation navigation tools are developed to enable precision and reliability. Military users are more likely to encounter jamming, so it is necessary to seek the technologies which can steadily provide high-precision positioning services in all-weather and harsh conditions. However, when users use an array antenna for interference suppression, it usually introduces pseudorange and carrier phase biases, which deteriorates the accuracy and integrity of positioning. To address this issue, the authors propose a blind anti-interference algorithm with biases suppression. Compared to the commonly used blind space–time adaptive anti-jamming algorithms, this algorithm can not only retain the advantages of the simple structure of the blind anti-jamming algorithm but also suppress pseudorange biases and carrier phase biases, both of which are crucial for users to obtain the availability of positioning. Moreover, the algorithm proposed can be applied easily and quickly to the actual high-precision positioning system with minor hardware changes in some cases. The relevant theoretical analysis and experiments have proved the effectiveness of the proposed algorithm: (1) code phase biases and carrier phase biases can almost be ignored and (2) it also can significantly improve the accuracy and integrity of positioning; the average positioning error is 1.6 mm, and it can keep the positioning accuracy better than 0.1 m for 97.5% of the time.

A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology

Wireless communications are increasingly vulnerable to malicious jamming attacks due to the inherent openness and broadcast nature. In this regard, a novel anti-jamming scheme based on knowledge graph is proposed in this paper, where the knowledge graph which contains the prior knowledge of the commonly-used jamming patterns is utilized to make the fast anti-jamming decision with low complexity. Specifically, for the known jamming patterns, the anti-jamming strategy is directly extracted based on the search from the knowledge graph by using the sensing information. On the other hand, for the unknown jamming patterns, a time-frequency domain feature extraction method is first proposed to extract the jamming feature and match the jamming patterns in the knowledge graph. As such, multiple anti-jamming strategies can be obtained by the knowledge graph search. Finally, the optimal strategy can be achieved by the two communication decision criteria-based strategy fusion. The simulation results showed that the time-frequency domain feature extraction method can well extract the time-frequency domain feature of the jamming pattern, and the proposed algorithm has high decision accuracy and robustness.

Conservative but Stable: A SARSA-Based Algorithm for Random Pulse Jamming in the Time Domain

As a dynamic jamming pattern, random pulse jamming is stochastic, sudden, and not easily perceived or addressed. Random pulse jamming can exert a negative influence on the quality of wireless communication. This paper models an anti-jamming communication under random pulse jamming as a Markov Decision Process and proposes a SARSA-based Time-domain Anti-jamming Algorithm (STAA). Differently from previous research, this study attempts to use the SARSA algorithm to counter random pulse jamming in the time domain. The proposed STAA can achieve high-quality transmission while maintaining small fluctuations in transmission performance when the risk of external interference is high.

An Integrated Beam Anti-Jamming Algorithm for Low-Orbit Navigation Augmentation

Satellite-based and ground-based navigation augmentation can help improve the anti-jamming capability for the global navigation satellite system (GNSS). However, for array anti-jamming terminals, the enhanced signal may be mistakenly regarded as interference and suppressed. In order to avoid this problem, an integrated beam anti-jamming scheme is proposed in this letter, and an optimal interval of the constraint configuration is given. Based on the correlation coefficient of space vectors, the array anti-jamming method can be adapted to the scene with enhanced signals by configuring the constraint radius and constraint dimension properly. The simulation results show that the proposed scheme can effectively suppress interference and avoid signal loss when the signal-to-noise ratio (SNR) is greater than 0 dB and up to 20 dB.

Marine SINS Self-Alignment Method in Complex Interference Environment Based on MEEMD Algorithm

It is difficult to rapidly and accurately achieve alignment under complicated interferential environmental conditions with a marine strapdown inertial navigation system (SINS) subject to angular swaying interference and linear motion interference. To solve this problem, an SINS self-alignment algorithm in a complex interference environment is presented in this article. For the angular motion disturbance problem, we used the attitude-updating algorithm in the coagulation inertial coordinate system to reflect the carrier’s attitude change in real time under the swaying interference so as to eliminate the influence of angular motion disturbance. To solve the problem of linear motion interference, we first use Fourier transform analysis (FFT) to analyze the frequency characteristics of linear motion interference signals. We then adaptively decompose the original signal into a series of signal components with different time scales and local characteristics according to the amplitude and frequency using the modified ensemble empirical mode decomposition (MEEMD) algorithm. Finally, we remove and reconstruct the disturbing signal components according to the frequency characteristics of the disturbing signal to filter the line motion disturbing signal. We use the MEEMD algorithm to conduct a ship alignment test. After pre-filtering by MEEMD, high accuracy can be achieved after 100 s of alignment, and the mean square error of the heading angle is 2.85′. The test results show that the MEEMD method has a good noise removal effect. The anti-jamming initial alignment algorithm based on the MEEMD algorithm has the ability to suppress the interference of angular motion and linear motion and achieve high-precision anti-jamming initial alignment under the complicated interferential environmental conditions with angular swaying interference and linear motion interference.

Entice to Trap: Enhanced Protection against a Rate-Aware Intelligent Jammer in Cognitive Radio Networks

Anti-jamming in cognitive radio networks (CRN) is mainly accomplished using machine learning techniques in the domains of frequency, coding, power and rate. Jamming is a major threat to CRN because it can cause severe performance damage such as network isolation, network application interruption and even physical damage to infrastructure simple radio devices. With the improvement in communication technologies, the capabilities of adversaries are also increased. The intelligent jammer knows the rate at which users transmit data, which is based on the attractiveness factor of each user. The higher the data rate for a secondary user, the more attractive it is to the rate-aware jammer. In this paper, we present a dummy user in the network as a honeypot of the jammer to get the jammer’s attention. A new anti-jamming deceiving theoretical method based on rate modifications is introduced to increase the bandwidth efficiency of the entire cognitive radio-based communication system. We employ a defensive anti-jamming deception mechanism of the Pseudo Secondary User (PSU) to as an entice to trap the attacker by providing thus enhanced protection for the rest of the network from the impact of the attacker. Our analytical simulation results show a significant improvement in performance using the proposed solution. The utility of the proposed intelligent anti-jamming algorithm lies in its applications to support the secondary wireless sensor nodes.

Research on Anti-Jamming Algorithm of Massive MIMO Communication System Based on Multi-User Game Theory

Research on Anti-Jamming Algorithm of Massive MIMO Communication System Based on Multi-User Game Theory

Waveform optimization technology of missile borne radar based on FSK-PSK composite modulation for anti repeater jamming

Aiming at the waveform of missile borne radar is simple and easy to be affected by the repeater jamming in the terminal guidance, an anti-jamming waveform optimization algorithm based on the frequency shift keying(FSK) and phase shift keying(PSK) composite modulation is proposed. The model function of FSK-PSK composite modulation signal is analyzed. Based on the orthogonality of sub pulses, an optimization algorithm of intra pulse PSK modulation based on the alternating direction multiplier method(ADMM) is designed. The simulation results show that comparing with PSK signal, the intra pulse sub pulse orthogonality of FSK-PSK signal optimized by modulation is improved by about 10 dB, which can effectively suppress the intra pulse forwarding interference.

Distributed Resource Management Framework for IoS Against Malicious Jamming

The internet of satellites (IoS), containing multiple low earth orbit (LEO) satellite constellations, can support tremendous traffic, massive connectivity, and vast coverage. But it also puts forward higher demands for resource management due to the high dynamics of the IoS networks, especially in the malicious jamming environment, where the jammers launch jamming attacks to reduce the efficiency and reliability. Thus, this paper investigates the problem of resource management in malicious jamming environment for IoS, which is divided into three sub-problems: traffic prediction problem, anti-jamming decision problem and resource matching problem. To solve these problems, we proposed a distributed resource management framework (DRMF), which consists of three sub-algorithms. Firstly, the traffic prediction algorithm (TPA) is proposed to deeply mine and accurately predict the traffic rule. Meanwhile, the dynamic anti-jamming algorithm (DAA) is developed to make anti-jamming decision autonomously. Then, based on the outputs obtained by TPA and DAA, the distributed resource matching algorithm (DRMA) is proposed for IoS, and the satellites with insufficient resource can apply for assistance from neighboring satellites with excess resource, thereby improving the safety and efficiency of the entire IoS network. Finally, experiment results and algorithm analysis verify the proposed scheme has better performance than the existing algorithms.

A hidden anti-jamming method based on deep reinforcement learning

Most of the current anti-jamming algorithms for wireless communications only consider how to avoid jamming attacks, but ignore that the communication waveform or frequency action may be obtained by the jammers. Although existing anti-jamming methods can guarantee temporary communication effects, the long-term performance of these anti-jamming methods may be depressed when intelligent jammers are capable of learning from historical communication activities. Aiming at this issue, a hidden anti-jamming method based on the idea of reducing the jammer's sense probability is proposed. Firstly, the sensing probability of the jammer is obtained by calculating the correlation between the actions of the jammer and the user. Later, a deep reinforcement learning framework is designed, which aims at not only maximizing the communication throughput but also minimizing the action's correlation between the jammer and the user. Finally, a hidden anti-jamming algorithm is proposed, which links the instantaneous return with the communication quality of users and the correlation between users and jammer. The simulation result shows that the proposed algorithm not only avoids being sensed by the jammer but also improves its anti-jamming performance compared to the current algorithm that only considers jamming avoidance.

A double network structure anti-jamming algorithm based on deep reinforcement learning

At this stage, when dealing with intelligent jammer with environmental cognition, most anti-jamming methods only consider how to maximize avoidance of it. However, the user’s previous waveform and frequency actions have been leaked. As the intelligent jammer continues to learn, the performance of anti-jamming will decline. Existing literature has proposed the idea of hidden anti-jamming. The user uses rational communication action-making to prevent the jammer from obtaining the information of the user so that the jammer cannot target the user for jamming, but its model is relatively simple and cannot cope with complex jamming types. This paper designs a kind of decision related judgment module between jammer and user based on Generative Adversarial Networks (GAN), which uses the environment state received by the user to reversely infer the user’s decision whether to avoid jammer’s sense. Specifically, the neural network is used to fit the environment state under known user information, and calculate the loss between it and the real environment state to evaluate whether the user’s decision is to avoid jammer’s sense. Then, this paper proposes a deep reinforcement learning algorithm with double-network structure, which can deal with various types of complex jamming while ensuring anti-jamming performance. The final simulation results show that the improved deep reinforcement learning algorithm proposed in this paper can improve the anti-jamming performance, achieve hidden anti-jamming, and have a wider range of applications.

A Design of Multi-element Anti-jamming GPS Antenna

Background: With the development of the application of GPS satellite, the requirement of anti-jamming and integration for navigation receiving terminal is gradually raised. In terms of anti-jamming performance, because the satellite signal arrives at the ground with weak intensity and is vulnerable to electromagnetic interference, the positioning results are not available. Objective: Therefore, in complex electromagnetic environment, the anti-jamming navigation receiving terminal is needed, and the interference nulling or beam pointing can be achieved by using multi- element antenna array with anti-jamming algorithm. Methods: Accordingly to requirements, the development trend of navigation receiving terminal is from single mode to multi-mode and miniaturization. A miniaturized 7-element anti-jamming antenna array is proposed. It mainly aims at GPS L1 and L2 frequency points. All elements adopt ceramic substrate microstrip antenna. The array elements adopt laminated design to effectively reduce the antenna aperture. The spacing between elements at two frequency points is not greater than half wavelength, which can ensure the back-end anti-jamming algorithm performance, and its performance and characteristics are verified through related experiments and simulations. Results: Anti-jamming algorithm performance, and its performance and characteristics are verified through related experiments and simulations. Conclusion: The designed multi-element antenna can be used in the anti-jamming application.

Exploring UAV's multi-domain joint anti-jamming intelligent decision algorithm

To understand the complex communication environment of a UAV in battlefield, its unknown channel statistics information and poor intelligent jamming and anti-jamming capability, the multi-domain anti-jamming problem is studied, and a multi-domain joint anti-jamming intelligent decision algorithm is proposed. First, the channel selection method is adopted to deal with jamming in the frequency domain. A multi-arm slot machine's channel selection model is established, and the channel interference level is judged. Secondly, the moderate interference channel is suppressed in the power domain, and the model of its Stackelberg game is established. The game equalization is solved to obtain the best transmission power and reduce the overhead caused by channel switching. The simulation results show that the long-term rewards of the intelligent decision algorithm are significantly higher than those of the traditional multi-arm slot machine's algorithm and the random selection algorithm and that the average throughput of the communication system of the UAV is improved, thus proving the superiority of the intelligent decision algorithm.

System anti-jamming technology in the design of intelligent single chip computer constant current source of field strength machine

With the development of the electric power industry, the technical level of automatic testing equipment for the reliability of electrical component circuit breakers in the transmission and distribution network is getting higher and higher. The stability and accuracy of the test power supply are the basis for ensuring the pass rate of the test product. Most of the electrical testing and testing equipment has defects such as inaccurate power supply current regulation, low power, and low level of intelligence, which are difficult to meet the testing requirements. Based on the theory of a closed-loop control system, this paper adopts embedded system design technology to realize a high-current, high-power, high-stability digital constant current source system for line detection. This paper studies the rule-based intelligent anti-jamming decision engine design and system anti-jamming performance analysis of NC-OFDM system. We give the design of an intelligent anti-jamming decision engine based on rule-based decision-making, and focus on two intelligent anti-jamming decision-making algorithms: Adaptive Modulation and Coding (AMC) algorithm based on signal-to-noise ratio difference and packet error rate and Adaptive Sub-Band Selection (ASBS) algorithm. Experimental test results show that the output current range is 200 mA to 2000 mA, the system has realized a microstep adjustment of±5 mA, and the absolute error of current measurement is less than 0.3%+4 mA. The system is stable and reliable, and has high practical value in the field of high precision and low power.

Intelligent Anti-Jamming Communication for Wireless Sensor Networks: A Multi-Agent Reinforcement Learning Approach

In this article, we investigate intelligent anti-jamming communication method for wireless sensor networks. The stochastic game framework is introduced to model and analyze the multi-user anti-jamming problem, and a joint multi-agent anti-jamming algorithm (JMAA) is proposed to obtain the optimal anti-jamming strategy. In intelligent multi-channel blocking jamming environment, the proposed JMAA adopts multi-agent reinforcement learning to make online channel selection, which can effectively tackle the external malicious jamming and avoid the internal mutual interference among sensor nodes. The simulation results show that, the proposed JMAA is superior to the frequency-hopping method, the sensing-based method and the independent reinforcement learning. Specifically, the proposed JMAA has the higher average packet receive ratio than both the frequency-hopping method and the sensing-based method. Compared with the independent reinforcement learning, JMAA has faster convergence rate when reaching the same performance of average packet receive ratio. In addition, since the JMAA does not need to model the jamming patterns, it can be widely used for combating other malicious jamming such as sweep jamming and probabilistic jamming.

Researchment of Mainlobe Anti-jamming Algorithms

Main lobe interferences usually cause the distortion and peak offset of main lobe and the heighten of side lobe level in adaptive beamformers. The block matrix algorithm utilized the direction vector which was determined by both the antenna arrays and the direction of interference to eliminate the main lobe interference. But this algorithms is sensitive to the error occurred in the interference arrive angle and would consume the array of degrees of freedom. In this paper a new algorithm is proposed which projects the receive signal into a space which is orthogonal to the signal subspace to achieve the goal. The space can get by three steps. Frist we obtain eigenvalues by the decomposition of the covariance matrix of the snapshots to evaluate the noise power. Then we get a new matrix by removing the noise from the covariance matrix of the snapshots. At last we get the target space by conducting the singular value decomposition to the new matrix. Performance is mainly assessed in terms of the beamforming gain and the signal to interference and noise (SINR) ratio. The computer simulation shows the effective of this method.