Jamming Resource Research Articles

Cooperative Jamming Resource Allocation with Joint Multi-Domain Information Using Evolutionary Reinforcement Learning

Cooperative Jamming Resource Allocation with Joint Multi-Domain Information Using Evolutionary Reinforcement Learning

A study of jamming resource allocation based on a hyperheuristic framework

There are massive data and rapidly changing battlefield situations in modern electronic warfare, which is a challenge to jamming resource allocation. It is difficult for the existing optimization algorithms to balance optimization capability and calculation speed at the same time. Above all, this study designs a fuzzy multiattribute evaluation model based on behavioral characteristics. The model comprehensively integrates the characteristics of signal behavior, motion behavior, and external intelligence and can effectively deal with unknown threat signals. Then, this study proposes an improved genetic selection electronic warfare operator (EWO) hyperheuristic (GAEWHH) algorithm. As an emergent optimization algorithm, the HH framework has not previously been applied to the problem of jamming resource allocation. This is a two-level algorithm framework that can isolate problem domains. The high level uses an improved genetic algorithm to search the heuristic space, and four EWOs based on the problem domain are designed for the low level to search the solution space. Combining different EWOs can change the population diversity, evolution direction, and algorithm complexity of the GAEWHH algorithm, which improves the algorithm performance to meet battlefield situation requirements. The experiment shows that for large-scale problems, the GAEWHH algorithm is better than the mainstream evolutionary algorithm in terms of optimization capability and better than Google OR-Tools in terms of calculation speed. In this way, the GAEWHH algorithm achieves a balance between optimization capability and calculation speed.

Efficient Jamming Resource Allocation Against Frequency-Hopping Spread Spectrum in WSNs With Asynchronous Deep Reinforcement Learning

Efficient Jamming Resource Allocation Against Frequency-Hopping Spread Spectrum in WSNs With Asynchronous Deep Reinforcement Learning

Anti-deception Jamming Resource Scheduling for Multi-target Tracking in Distributed Radar Networks

Anti-deception Jamming Resource Scheduling for Multi-target Tracking in Distributed Radar Networks

Fast Adaptive Jamming Resource Allocation Against Frequency-Hopping Spread Spectrum in Wireless Sensor Networks via Meta Deep Reinforcement Learning

Fast Adaptive Jamming Resource Allocation Against Frequency-Hopping Spread Spectrum in Wireless Sensor Networks via Meta Deep Reinforcement Learning

Optimization of jamming formation of USV offboard active decoy clusters based on an improved PSO algorithm

Offboard active decoys (OADs) can effectively jam monopulse radars. However, for missiles approaching from a particular direction and distance, the OAD should be placed at a specific location, posing high requirements for timing and deployment. To improve the response speed and jamming effect, a cluster of OADs based on an unmanned surface vehicle (USV) is proposed. The formation of the cluster determines the effectiveness of jamming. First, based on the mechanism of OAD jamming, critical conditions are identified, and a method for assessing the jamming effect is proposed. Then, for the optimization of the cluster formation, a mathematical model is built, and a multi-tribe adaptive particle swarm optimization algorithm based on mutation strategy and Metropolis criterion (3M-APSO) is designed. Finally, the formation optimization problem is solved and analyzed using the 3M-APSO algorithm under specific scenarios. The results show that the improved algorithm has a faster convergence rate and superior performance as compared to the standard Adaptive-PSO algorithm. Compared with a single OAD, the optimal formation of USV-OAD cluster effectively fills the blind area and maximizes the use of jamming resources.

A Joint Allocation Method of Multi-Jammer Cooperative Jamming Resources Based on Suppression Effectiveness

This paper studies the resource allocation problem when multiple jammers follow the aircraft formation to support ground penetration. A joint optimization allocation method of multi-jammer beam-power based on the improved artificial bee colony (IABC) algorithm is proposed. The air-to-ground “many-to-many” assault of the multi-jammer cooperative suppression jamming model is given. The constant false alarm probability detection model of the networked radar is used to evaluate the suppression effect, and a coordinated control model of multi-jammer jamming beams and emitting power is established. The optimal allocation scheme under different combat scenarios is solved by using the IABC algorithm. The search efficiency of the ABC algorithm is improved by cross mutation operation and the replacement of the worst nectar source, and the search performance of the algorithm is enhanced by the random key encoding. Due to the infeasible solution generated by the special random key encoding method, the feasible adjustment strategy is adopted. By changing the jamming parameters, the effect on the detection probability of the radar network is analyzed. Compared to the GWO, SCA, BBO and ABC algorithms, the jamming resource allocation scheme obtained by the proposed IABC algorithm makes the radar detection probability lower. The IABC algorithm has better global search capability and robustness.

A DRL-Based Intelligent Jamming Approach for Joint Channel and Power Optimization

The traditional jamming methods mainly focus on the optimization of a single domain, such as frequency domain. However, in the practical wireless communication, the jamming methods in single dimension domain are limited and difficult to cope with multidomain scenarios. To overcome these issues, this paper investigates the problem of joint decision-making for jamming channel and power in a dynamic spectrum environment. Firstly, the Markov decision process (MDP) is used to formulate the jamming channel and power joint selection problem. Then, a deep reinforcement learning- (DRL-) based jamming algorithm is proposed with the function of parallel learning and joint decision-making. Specially, to accelerate the learning speed of the algorithm, the prioritized experience reply (PER) technology is introduced. Finally, a practical jamming testbed is built to evaluate the proposed algorithm. The simulation results and the testbed results demonstrate the effectiveness of the proposed algorithm, which can guarantee the jamming effect and maximize the utilization of jamming resources.

Joint Optimization of Jamming Link and Power Control in Communication Countermeasures: A Multiagent Deep Reinforcement Learning Approach

Due to the nonconvexity feature of optimal controlling such as jamming link selection and jamming power allocation issues, obtaining the optimal resource allocation strategy in communication countermeasures scenarios is challenging. Thus, we propose a novel decentralized jamming resource allocation algorithm based on multiagent deep reinforcement learning (MADRL) to improve the efficiency of jamming resource allocation in battlefield communication countermeasures. We first model the communication jamming resource allocation problem as a fully cooperative multiagent task, considering the cooperative interrelationship of jamming equipment (JE). Then, to alleviate the nonstationarity feature and high decision dimensions in the multiagent system, we introduce a centralized training with decentralized execution framework (CTDE), which means all JEs are trained with global information and rely on their local observations only while making decisions. Each JE obtains a decentralized policy after the training process. Subsequently, we develop the multiagent soft actor-critic (MASAC) algorithm to enhance the exploration capability of agents and accelerate the learning of cooperative policies among agents by leveraging the maximum policy entropy criterion. Finally, the simulation results are presented to demonstrate that the proposed MASAC algorithm outperforms the existing centralized allocation benchmark algorithms.

Cooperative jamming resource allocation model and algorithm for netted radar

In this letter, the jamming resource allocation problem of distributed jammers cooperatively jamming netted radar system is investigated. A well-constructed jamming resource allocation model considering jamming beams, jamming power and other influencing factors is established. Random keys are used in this letter to improve the coding mode of genetic algorithm. Simulation results show that in the case of limited jamming resources, the model and algorithm proposed can achieve effective jamming allocation schemes facing a netted radar with any number of radar nodes.

Adaptive allocation strategy for cooperatively jamming netted radar system based on improved cuckoo search algorithm

The jamming resource allocation problem of the aircraft formation cooperatively jamming netted radar system is investigated. An adaptive allocation strategy based on dynamic adaptive discrete cuckoo search algorithm (DADCS) is proposed, whose core is to adjust allocation scheme of limited jamming resource of aircraft formation in real time to maintain the best jamming effectiveness against netted radar system. Firstly, considering the information fusion rules and different working modes of the netted radar system, a two-factor jamming effectiveness evaluation function is constructed, detection probability and aiming probability are adopted to characterize jamming effectiveness against netted radar system in searching and tracking mode, respectively. Then a nonconvex optimization model for cooperatively jamming netted radar system is established. Finally, a dynamic adaptive discrete cuckoo search algorithm (DADCS) is constructed by improving path update strategies and introducing a global learning mechanism, and a three-step solution method is proposed subsequently. Simulation results are provided to demonstrate the advantages of the proposed optimization strategy and the effectiveness of the improved algorithm.

Optimal distributed cooperative jamming resource allocation for multi‐missile threat scenario

Jamming resources allocation (JRA) is an essential task in clustered combat environments. Especially in cases with multiple threats, efficient JRA is imperative for protecting key assets. In this study, multiple distributed outboard jammers protecting multiple targets against a multi-missile threat are considered, where the function of JRA includes jamming object selection and jammer power allocation. To address this problem, first, a scheme to evaluate the jamming effectiveness of the radar seeker under cooperative jamming from multiple jammers is proposed. Considering power allocation, a mixed-integer programing model for the optimisation of JRA is further established. A two-track simultaneous update binary particle swarm algorithm (TS-BPSO) is then devised to obtain the solutions of the JRA. Finally, the effectiveness of the proposed method for multiple jammers in the multiple missiles scenario is evaluated through simulations. The simulation results suggest that the distributed cooperative jamming resource allocation is affected by the incoming orientation of the missile and the relative position of the jammers and the targets. The findings of the study enable evaluating the use of jammer formations and provide insights into developing effective jamming strategies.

Research on Jamming Resource Allocation Technology Based on Improved GAPSO Algorithm

In this paper, firstly, the influence factors of different electronic loads on radar are analyzed from various angles, and a resource allocation model is built for penetration scenarios. Then, the evaluation indexes of radar in different working stages are explained and fused to construct a scientific and reasonable objective function. Finally, a genetic particle swarm(GAPSO) algorithm based on two-dimensional parallel coding is creatively proposed, and the parameters of the algorithm are adjusted adaptively to improve the performance of the algorithm. The simulation results show that the improved GAPSO algorithm has obvious effectiveness and superiority in solving the resource allocation problem in process of jammers shielding target penetration.

Multi-efficiency optimization method of jamming resource based on multi-objective grey wolf optimizer

Multi-efficiency optimization method of jamming resource based on multi-objective grey wolf optimizer

A cooperative interference resource allocation method based on improved firefly algorithm

To deal with the radio frequency threat posed by modern complex radar networks to aircraft, we researched the unmanned aerial vehicle (UAV) formations radar countermeasures, aiming at the solution of radar jamming resource allocation under system countermeasures. A jamming resource allocation method based on an improved firefly algorithm (FA) is proposed. Firstly, the comprehensive factors affecting the level of threat and interference efficiency of radiation source are quantified by a fuzzy comprehensive evaluation. Besides, the interference efficiency matrix and the objective function of the allocation model are determined to establish the interference resource allocation model. Finally, A mutation operator and an adaptive heuristic are integtated into the FA algorithm, which searches an interference resource allocation scheme. The simulation results show that the improved FA algorithm can compensate for the deficiencies of the FA algorithm. The improved FA algorithm provides a more scientific and reasonable decision-making plan for aircraft mission allocation and can effectively deal with the battlefield threats of the enemy radar network. Moreover, in terms of convergence accuracy and speed as well as algorithm stability, the improved FA algorithm is superior to the simulated annealing algorithm (SA), the niche genetic algorithm (NGA), the improved discrete cuckoo algorithm (IDCS), the mutant firefly algorithm (MFA), the cuckoo search and fireflies algorithm (CSFA), and the best neighbor firefly algorithm (BNFA).

Ground threat evaluation and jamming allocation model with Markov chain for aircraft

This study proposes a ground-based threat probability model based on a Markov chain to evaluate multiple ground threats faced by the platform during flight. On this basis, a new threat evaluation and jamming allocation system is constructed by combining the model with jamming resource allocation methods. In the threat evaluation phase, the interaction relationship between the platform and radar is represented by the transfer intensity among radar states according to the domain expert knowledge. Then, the potential state probability of radars in each time period is deduced and converted into the danger value as the objective to be optimised in the next stage. In the jamming allocation phase, the system considers some effects such as jamming technologies, multi-channel, the uncertainty of the threatening environment and other factors, and simulates the process of ground threats. There is traversing all jamming combinations at each period to select the best jamming strategy. This study also designs a multi-threat environment to verify the effectiveness of the system and test its relative performance improvement. Compared with the original threat evaluation and jamming allocation system, the new system has proved its feasibility and better application value.

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.

Distribution method of jamming resource based on IFS and IPSO algorithm

Distribution method of jamming resource based on IFS and IPSO algorithm

Optimal allocation of cooperative jamming resource based on hybrid quantum‐behaved particle swarm optimisation and genetic algorithm

The multi-dimension jamming resources allocation (JRA) problem is studied to enhance the jamming effectiveness for the cooperative jammers formation against radar net. The allocation strategies include the methods of allocating jamming resources and the mode selection for the jamming signal. First, jamming resource optimum allocation model is established based on the detection probability of the netted radar fusion centre. Second, a hybrid quantum-behaved particle swarm optimisation and self-adjustable genetic algorithm (HQPSOGA) is proposed to optimise the deployment of the jamming resource innovatively with multi-constrained conditions. Finally, the HQPSOGA is compared with the integer-value genetic algorithm, standard particle swarm optimisation (PSO) and quantum-behaved PSO in JRA problem regarding the solution quality, robustness, convergence rate and reliability by a general Monte Carlo simulation. Simulation results show that the proposed method is capable of developing better overall interference capacity efficiently for the jammers formation than any other tested algorithm.

Research on Jamming Resource Scheduling Based on Multi-Target and Fuzzy Multi-Stage

For radar and electronic warfare integrated system, resource scheduling play a big role in operational effectiveness. At first, introduced the composition and technical performance advantage of radar and electronic warfare integrated system. According to radar jamming equation, the characteristics of array, combined with jamming time-window, jamming efficiency ratio, jamming satisfaction, established multi-target fuzzy multi-stage jamming resource scheduling model with time-window, through the simulation, proved the scientificity and rationality of the method.