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To address the problem of poor recognition success rate (RSR) of low probability of intercept (LPI) radar signals such as polyphase codes at low signal-to-noise ratio (SNR), In this paper, we propose a method of deep neural network with attention mechanism for modulation recognition of radar signals. The method first transforms the received radar signal into a two-dimensional time-frequency image(TFI), and then we obtain a binary time-frequency image by threshold segmentation, and resize TFIs through bicubic interpolation. While a deep residual network with Convolutional Block Attention Module (CBAM) is designed to recognize the radar signals. We take advantage of the attention mechanism to find the effective feature information in the noise and increase the effective information weight to further improve the recognition success rate of radar signals under low SNR. Finally, the average RSR of the method is nearly 96% for 7 signals at -6dB.

In recent years, unmanned ground vehicles (UGVs) have been widely used in urban environments. The satellite positioning system plays an irreplaceable role in UGVs. However, the tall buildings in cities may cause interference to the transmission of satellite signals, which results in positioning delays, thus reducing the positioning accuracy of UGVs. To address this issue, a compensation method for information delay based on factor graph model is proposed in this paper. In the factor graph model utilized, the corresponding GPS factor node will not be generated when the GPS signal delay occurs at the current fusion time. The subsequent factor graph model is directly generated by the combined navigation system of visual odometry (VO) and inertial measurement unit (IMU). When the delayed GPS signal arrives, the measurement data of VO and IMU are used to compensate the delayed GPS positioning information. The compensated GPS positioning information is used to generate an additional factor node at the moment when the GPS signal actually arrives. This additional factor node is connected into the factor graph model for data fusion so that the delayed GPS positioning information can still be used to correct the positioning error. The method proposed in this paper effectively reduces the positioning error while taking into account the real-time performance. Experimental simulations show that the proposed method can reduce the positioning error while GPS signal delay occurs.

In the wireless network, unmanned aerial vehicle (UAV)-enabled energy transmission and information collection can achieve flexible and effective data dissemination and operational guarantee. How to realize the scheduling of UAVs to accomplish the energy transmission and information collection tasks is an inevitable issue in the UAV-enabled wireless network. In this paper, the UAV-enabled wireless network model is firstly established. Then, consensus-based bundle algorithm is applied in the UAV-enabled wireless network to establish the feasible and conflict-free schedules for UAVs to execute parallel energy transmission and information collection tasks when maximizing the global score value. Simulations verify the validity of the proposed UAV scheduling algorithm for the UAV-enabled wireless energy transmission and information collection.

Electronic countermeasures are moving towards intelligence. Due to the constantly changing decision-making of both parties, traditional decision-making methods are no longer applicable. Introducing game theory into electronic confrontation decision-making can solve this problem and model the opponents as two-person zero-sum games. In order to improve the average return of the interferer, this paper proposes the feedback iterative-Brown algorithm to adjust the hybrid strategy. Simulation results prove that the algorithm proposed in this paper can effectively improve the average return of the interferer.

This paper aims to design an appropriate power control policy of the secondary user (SU) to share the spectrum with the primary user without harmful interference. With dynamic spectrum environment, we develop a power control policy based on deep reinforcement learning with Deep Q network (DQN) that the secondary can intelligently adjust his transmit power. And reward function is properly designed to avoid the sparse reward problem which may cause the secondary user cannot adjust to effective power in limited steps and finally fails to transmit. Our experiment result reveals that under the help of the proposed network and reward function, the secondary user can fast and efficiently adjust to effective power from any initial state.

The previous research on how to choose the anti-interference method is to build a profit matrix based on game theory, and select the anti-interference strategy with the largest profit value. This paper proposes a method of identifying anti-interference strategy selection based on the minimum loss criterion: transforming established profit matrix into a minimum loss decision matrix and iterating it enough times to get the best strategy. Compared with the previous methods, the proposed method in this study can be proved to achieve smaller losses.

In this paper, we investigate the resource allocation for device-to-device (D2D) communications with underlaying cellular networks. Specifically, in underlay mode, D2D communications optimize for maximizing the corresponding utility under cellular users' quality of service (QoS) requirement and transmit power constraints. To solve this challenging subchannel allocation problem, which is actually a nonconvex mixed-integer programming problem, we decompose the whole optimization problem into independent subproblems. Firstly, we find out the candidate D2D communications users who are permitted to share the same spectrum with cellular users in the admission control subproblem. Then, to solve the resource allocation subproblem, a novel fast and tradeoff subchannel allocation algorithm between utility and fairness is proposed with setting higher priority for unpopular D2D users. Simulation results validate our proposed novel fast allocation strategy achieves remarkable improvement with low complexity and fairness guaranteeing.

UAVs as mobile nodes have been introduced into wireless sensor network (WSN) to assist information transmission and reduce the burden of communication. To minimize the path cost of UAVs for information transmission, this paper focuses on the path planning of UAVs. A multi-UAVs path planning combined K-means clustering algorithm and improved MAXMIN ant system (MMAS) is proposed. This algorithm apply the K-means clustering algorithm to reduce the problem size and improve the search efficiency of subsequent path planning. By modifying the node search rules and proposing two optimal solution detection rules of the MMAS, the algorithm searching stagnation and failing into local optimal solution are effectively avoided.

The path planning of Unmanned Aerial Vehicle (UAV) is a critical component of rescue operation. As impacted by the continuity of the task space and the high dynamics of the aircraft, conventional approaches cannot find the optimal control strategy. Accordingly, in this study, a deep reinforcement learning (DRL)-based UAV path planning method is proposed, enabling the UAV to complete the path planning in a 3D continuous environment. The deep deterministic policy gradient (DDPG) algorithm is employed to enable UAV to autonomously make decisions. Besides, to avoid obstacles, the concepts of connected area and threat function are proposed and adopted in the reward shaping. Lastly, an environment with static obstacles is built, and the agent is trained using the proposed method. As has been proved by the experiments, the proposed algorithm can fit a range of scenarios.

In large monitoring area, sensors in the wireless sensor network (WSN) needs to send the environmental information back to distant base station. To solve the potential problems of information transmission quality and energy consumption in WSN, unmanned aerial vehicle (UAV) is adopted to collect the observation information from sensors and charge them. In this paper, a two-layer task assignment strategy is proposed to realize the information collection (IC) and energy charging (EC) in WSN. Firstly, the whole monitoring area of wireless sensor network is divided into multiple subregions. According to the task needs of each subregions, base station will send different multi-UAV teams to perform IC and EC tasks on these subregions. Then, to minimize the flight paths of multi-UAV team in each subregions, consensus-based bundle algorithm is used to generate corresponding task assignment schedules. Simulations demonstrate that the proposed two-layer task assignment strategy ensures that sensors in the WSN can upload observation promptly and have sufficient energy.

To meet the ever-increasing requirements of high data transmission rates by Internet of Things (IoT) users, ultradense heterogeneous network (HetNet) is introduced for its excellent performance in enhancing cell coverage and improving system throughput. However, the sophisticated structure in ultradense HetNet makes user association become a crucial problem. Thus, this paper focuses on the user association problem in ultradense HetNet, aiming at maximizing system throughput. Then a difference based matching algorithm is proposed, in which the achievable rate difference between the current request and the next request is taken into account when multiple IoT users compete for the access opportunity with the same low power points (LPPs). Simulation results show that the proposed algorithm can achieve both the highest system sum-throughput and the best fairness compared with the four comparison algorithms.

The rapid development of electronic technologies put forward higher requirement of electronic interference system continuously. How to allocate the various interference resources is a problem that the communication equipment needs to solve. This paper introduces the approach of interference resource allocation approach and proposes two different integer linear programming models. One of the models assigns interference sources to equipment that satisfy effective interference probability and minimize overinterference, the second model assigns interference resource to equipment that satisfy the desired effective interference probability and minimize the cost value for the given interference systems, equipment, and desired effective interference probability. What’s more, a serious consideration called deconfliction also be given to avoid the destruction of certain friendly structures. The proposed models are solved by a MATLAB program with YALMIP and CPLEX. Computational results show that large instances of the proposed models can be solved within a few seconds optimally.

Taking multiple unmanned aerial vehicles (UAVs) cooperative combat field as background, a systemic research on multiple UAVs cooperative mission assignment algorithm based on ant colony optimization method is given in this paper. Multi-UAV cooperative mission assignment is an important part of multi-UAV cooperative combat field, which is also the security guarantee for multi-UAV system to accomplish cooperative suppression of enemy air defences. The essence of multi-UAV cooperative mission assignment is a kind of complicatedly combined optimization problem that has the characteristics as multiple objectives, multiple constraints, non-linear, strong coupling, etc. Thus, as a multi-parameter, multi-constrained NP-hard problem, multi-UAV cooperative mission assignment area is similar to task assignment field and resource allocation category, which can be validly solved by intelligent optimization methods. To address this problem, we introduce ant colony optimization approach in this paper to establish a multi-UAV cooperative mission assignment strategy to meet the cooperative combat task requirements of multiUAV system. In the simulation part, using the example that multiple UAVs cooperatively attack multiple objectives as the application background, we give a comprehensively experimental analyses of multi-UAV cooperative mission assignment based on ant colony optimization method. Finally, the rationality and effectiveness of the proposed multi-UAV cooperative mission assignment algorithm are verified. Therefore, the certain significance of this paper has been proved.

In cognitive radio, cooperative spectrum sensing can significantly improve the performance of spectrum sensing. The performance of cooperative spectrum sensing will be impacted when malicious users send wrong data to the fusion center. To solve this problem, a SNR-based Particle Swarm Optimization (PSO) cooperative spectrum sensing algorithm is proposed. First, the OGK algorithm is proposed to solve the problem the estimate of the mean and variance are inaccurate when malicious data exists. Second, applying SNR to the initial position of PSO algorithm to make the assignment of cognitive users more reasonable. Simulation results show that the performance of the proposed algorithm is better than the standard PSO method in defending against SSDF attack.

This paper reports a mode-localized magnetometer with novel 3 degrees-of-freedom (DOF) weakly coupled resonators (WCRs) coupled by F-shaped coupling beams. In this coupling mechanism, the direct coupling between the two outer resonators can be effectively suppressed, which decreases the coupling coefficient and drastically increases the sensitivity. Utilizing these novel WCRs for Lorentz force sensing, a magnetometer is realized with the resolution of $6.9\ \text{nT}/\surd \text{Hz}$ in the range of 100 mT, which is an improvement of 234.78 times that of state-of-the-art mode-localized magnetometers ( $1.62\ \mu \mathrm{T}/\surd\text{Hz}$ ).

In this paper, we propose a distributed resource allocation algorithm based on game theory to solve the problem of cross-tier interference between device-to-device (D2D) communication and cellular users and the co-tier interference between D2D communication. We assume that the D2D user is forced to reuse the channel resources of multiple cellular users, while the resources of each cellular user can be reused by multiple D2D users. Moreover, the resource allocation problem between cellular users and D2D users is modeled as a non-cooperative game, and each participant is selfish and wants to maximize its utility. At the same time, D2D users coordinate the co-tier interference in a cooperative manner. The simulation results show that the proposed distributed algorithm has a significant improvement in term of throughput with guaranteeing the quality of service (QoS) requirement when compared with the existing algorithms.

Low Density Parity Check (LDPC) codes have been widely applied in recent years, and Extended Min-Sum (EMS) decoding algorithm is the most practical algorithm, which has a fairly low computational cost. Due to the great loss in error correction performance of EMS algorithm, in this paper, we establish constraints to obtain reliable nodes while decoding and evaluate the reliability of the nodes. The nodes and information transferred between nodes are processed and optimized according to the reliability of the nodes. High reliability nodes will not be updated in the decoding process while the information of the sub-reliability nodes will be optimized in different situations, thereby improving the decoding accuracy. This algorithm can accelerate the convergence of the algorithm, while the reduced number of update nodes can control the increase of the computation to a certain extent, and maintain the advantage of the low computation quantity of EMS algorithm. Through simulation results and computational analysis, we prove that the improved algorithm has significantly improved error correction performance compared with the traditional EMS algorithm. Under the same conditions, it has completely entered the waterfall area at 3.6dB, and the EMS algorithm has not entered the waterfall area. By comparing the average iteration times of decoding of the two algorithms, the convergence speed of the algorithm can be reflected. Compared with EMS algorithm, the improved algorithm has significantly improved the convergence speed in most areas, which indicates that the processing of sub-reliable nodes accelerates the convergence of the algorithm. Finally, the average update times of the check nodes and variable nodes of the two algorithms are compared respectively. Although the improved algorithm introduces extra processing flow, it speeds up the convergence of the algorithm, and the computational cost of decoding is less expensive.

To improve the mixing matrix estimation performance of frequency hopping (FH) signals under the underdetermined blind source separation (UBSS) model, a new estimation method is proposed in this paper. First, time frequency (TF) analysis is utilized to obtain sparse TF data. Then, remove the low-energy TF points to avoid the effect of noises and reduce the amount of calculation. Next, detect the single source points (SSPs) with the derived formula. Finally, the dynamic data field clustering method is utilized to estimate the mixing matrix. The results of simulation experiments indicate that the proposed algorithm has better performance than the compared algorithms.

The electromagnetic positioning system is a system that uses the spatial magnetic field strength to obtain the position and direction angle of the receiving sensor in real time, and is widely used in virtual reality and medical navigation. Due to its inherent characteristics, the electromagnetic positioning system receiving sensor is easy to lost the accuracy and stability of its positioning and tracking results due to external interference, resulting in a large positioning error, so the calibration work is very important for it. In this paper, the spatial sampling points are divided into tetrahedrons by Delaunay triangulation to interpolate and the Kriging interpolation algorithm is used to compensate the accuracy. Through experiment and simulation, the position and direction angle accuracy is significantly improved, and it is shown that the performance of proposed method is better than traditional polynomial fitting and traditional interpolation algorithms.

Wireless systems are vulnerable to various kinds of jamming attacks due to the development of complexity of electromagnetic environment. Intelligent anti-jamming decision for multi-domain, which is more flexible than traditional single-domain technique, has attracted much more attention. However, poor decision-making speed and robustness of decision-making algorithm lead to a non-optimal communication scheme in huge decision space. An anti-jamming decision method based on the mutation search artificial bee colony algorithm (MSABC) is proposed in this paper. Numerical investigations show that the proposed algorithm can find a better communication scheme that reduces the normalized transmitting power while maintaining the normalized rate.

A Unitized regenerative fuel cell complete energies transition between chemical and electricity in a unitized cell, and this characteristic makes mode switching operations inevitable in real applications. In this work, cell voltage dynamic response during mode switching in a URFC with parallel flow field is tested. Single flow direction in parallel flow field is not suitable for unitized regenerative fuel cells when operation modes are switched. Different flow directions for different modes makes switching operations more stable. In addition, comparing with the flux of purge gas, the time of purge increase is more efficient to push out the liquid water after electrolysis cell mode stopping for the startup fuel cell operation.

Blocked flow channel can enhance the reactants transfer process and improve the output performance of proton exchange membrane fuel cells. In waved-like blocked flow channels, the flow resistance at the windward side can be reduced because of the streamline shape design. However, the vortexes, which account for the power loss, can be formed at the leeward sides. In this work, a two-dimensional, two-phase, non-isothermal and steady state model is developed, and flow channels with different wave-like blocks are designed to investigate the effects of windward sides and leeward side on output performance of proton exchange membrane fuel cells. Simulation results show that: when the heights and weights of wave-like blocks are both fixed, longer leeward side of blocks facilitate to reduce the vortexes forming and decrease the pumping powers, in the meantime, the net powers and the efficiency are improved. However, when the leeward sides of blocks are shortened and windward sides are lengthened, vortexes forming in flow channels are strengthened and pumping powers are improved, and the net powers are reduced. Moreover, when the leeward side is prolonged, the liquid water in flow channels can be removed more easily.

In comparison to the binary LDPC codes, the nonbinary low-density parity-check (NB-LDPC) codes have the advantage of solving burst errors. In order to improve the accuracy of the decoding results, an improved extended min-sum (EMS) algorithm is proposed in this paper. By adopting the feedback idea of joint iterative detection algorithm and introducing the reliability and stability parameters, the new algorithm can mitigate the influence of Log-likelihood ratio (LLR) oscillation on the decoding result and adjust the update information of the check node properly, thereby improving the decoding accuracy. This method can effectively restrain the LLR oscillation and speed up the convergence rate, thus the final decision result can be amended. Simulation results show that, the proposed improved EMS decoding algorithm heightens the bit error rate performance and reduces average number of iterations, meanwhile the additional computations are within acceptable range.

In the modern information warfare, the requirements for the reliability and real-time performance of the communication signal recognition technology are getting more and more strict. Although a great number of studies have been conducted in the reliability of communication signal recognition, few studies have been performed in the speed of communication signal recognition. The purpose of this study is to explore an improved feature extraction methods based on extreme learning machine (ELM) which has the advantage of higher speed in communication signal recognition. The results of simulations show that the approach in this paper not only improves the speed of recognition and ensures a high reliability, but also reach an ideal recognition accuracy at a low SNR.

In this paper, the problem of inter cell interference suppression which under the multi cell heterogeneous network system model is studied. Game theory is used to build the model of frequency and timeslot selection. The analysis shows that the proposed resource reuse model is a potential game, which can improve the throughput of the network.

The sensing result of cooperative spectrum sensing in cognitive radio significantly depends on the reliability of local detection, and signal-to-noise ratio (SNR) difference among secondary users leads to disparate reliability in local sensing. A novel cooperative spectrum sensing algorithm based on node filtrating (NF-CSS) is proposed in this paper, avoiding low SNR nodes being involved in the collaboration by decreasing the number of nodes. Simulation results showed the algorithm eliminates the influence of low SNR nodes and effectively improves system sensing performance under uneven distribution of SNRs.

Weapon-Target Assignment (WTA) problem is the key of air defense command and control. Therefore, it is an urgent problem to complete the assignment quickly and efficiently. In this paper, an improved artificial fish swarm algorithm is proposed to improve assignment rate. Based on artificial fish swarm algorithm (AFSA), particle swarm optimization (PSO) is introduced to change the individual visual of artificial fish, and genetic operator is added to avoid the local extremum trap. The proposed algorithm is validated by the concrete cooperative air defense examples. The simulation results show that the algorithm improved the computational accuracy and the rate of convergence in solving weapon-target assignment problem in air defense.

The global positioning system (GPS) is widely used for military and civil applications due to its strong abilities of precise three-dimensional positioning and measurements of velocity and time. However, the GPS receiver is easy to be jammed by spoofing since the power of the receiver signal is very weak. Generally, the spoofing signal can be detected by the GPS information using Kalman filter. Unfortunately, this method appears to have little effect in the case of high dynamic positioning. In this paper, we propose an anti-spoofing algorithm based on adaptive Kalman filter for high dynamic positioning. According to the orthogonality of innovation in the Kalman filtering, the proposed method firstly uses the threshold determination to detect whether the spoofing jamming existed in the process of positioning solution of GPS. If there exists spoofing jamming, the gain coefficient will be modified by using the M-estimation in statistics, and the error variance will be reduced to ensure that the filter estimate is as close as possible to the true state of the system, meanwhile guaranteeing the filtering accuracy of the results. At the same time, the introduction of the fading factor reduces the proportion of the previous state information and reuses current measurement information, which ensures liable convergence filter and improves stability and further achieves the target of anti-spoofing in the process of high dynamic positioning. The simulation results show that in high dynamic environment, if spoofing jamming exists, the proposed method can guarantee the accuracy and stability of the positioning results at the same time.

Global Positioning System (GPS) has been widely used in electronic countermeasure field, like accurate command and precision guidance. Thus, it is necessary to find effective countermeasures to confront GPS. Radio Interference is one of the major methods in confronting the GPS in civilian and military applications. Interference signals are produced not only through all electronic systems but also illegal jammers. Among different types of interferences, continuous wave (CW) interference has strong adverse impacts on the quality of the received signal. In this paper, a system composed of receiver module, transmitter, channel module and CW interference module is established. The effects of CW interference is studied on the carrier-to-noise density ratio (C/N 0 ) of the received GPS signal as an indicator of the quality of that signal. It is analytically shown that the spectrum characteristics of the C/A code has a significant impact on the effects of CW interference. In addition, in this paper we make more detailed analysis for CW interference effects on the performance of GPS receivers. Based on the C/A code spectrum lines, the influence of CW interference on the acquisition performance of GPS receivers is further analysed and a conclusion is given as follow. When the CW interference signal has a particular frequency which is obtained according to C/A code spectrum lines, the acquisition performance is worse than other interference frequencies. This conclusion is supported by simulation results using GPS software receiver. The research on CW interference gives some theoretical gist and new thoughts on monitoring the radio noise environment and improving the anti-jamming ability of GPS receivers.

The rapid development of electronic technologies raise higher requirements on electronic jamming system and combat tactic continuously. In order to improve the survivability of battlefield, the power of interference source is limited to specified values. Under these circumstances, the collaborative interference of multiple tactical jamming sources towards multiple targets plays an ever increasing role in the approaching electronic warfare scenarios. The allocation of the various jamming resources becomes an urgent problem that needs to be resolved by the commander in charge. Considering that the impact of large power consumption of jammers leads to a quite low battlefield survivability, the research on jamming resource allocation attracts more attention in recent years. In order to obtain the optimal jamming effect by utilizing limited power of jamming resources, this paper proposes a jamming resource allocation method based on the simulated annealing algorithm. The proposed model takes into account the shortcomings of both the single interference mode and the fixed interference mode. The total interference power is used as the constraint condition in this algorithm, and the objective function for allocating jamming resources is built accordingly. The benefits of assigning power allocated to jammers are assumed to be finite, which applies to most real-world combat scenarios. Moreover, we consider the effect on bit error rate of different power allocation schemes, the target of which is to develop a model for jamming power allocation. And a scenario is defined by the information provided by the specific communication link. Computational results show that the proposed model can allocate jamming resource effectively and optimize the combat capability of electronic warfare systems. The research on simulant annealing algorithm provides some theoretical gist and new thoughts on jamming resource allocation and improves the decision-making ability of electronic warfare systems.

With the remarkable advantage in whole area coverage support and indoor cellar capacity improvement, femtocell is considered as an important component in next generation networks, which leads to a more complex interference scenery. In this paper, we consider a two layer LTE-A HetNets, which comprised with one MBSs in the first tier and some FBSs in the other tier. Under this architecture, how to improve the spectrum usage efficient and relieve the interference between different user equipments (UEs) at the same time remain a longstanding challenge. A series of sub-channel allocation heuristic algorithms, named hybrid clustering mixed Dastur (HD) series algorithm including T-HD, HD-FU and HD-MU algorithms, are developed via the graph-theoretic approaches to optimize the femtocell throughput in dense femtocells deployment scenarios, in which the optimal sub-band allocation problem is managed through a MAX-K cut problem management scheme. System level simulations indicates that the spectrum efficient is improved obviously and femtocell users (FUE) throughput has been boosted significantly without causing a visibly degradation in macrocell users (MUE) throughput.

In cognitive radio networks (CRNs), cooperative spectrum sensing performed by multiple users is able to improve the sensing performance of the cognitive system since available spectrum holes can be detected and utilized, thus increasing spectrum utilization efficiency. However, cognitive users are vulnerable to spectrum sensing data falsification (SSDF) attacks, which arises as one of the most serious security threats in CRNs. In the SSDF attack, attackers send erroneous local reports to the fusion center during cooperative spectrum sensing process to mislead the fusion center to make a false global decision. The security of the network guarantees that wireless communication can be carried out smoothly. Therefore, the research on secure spectrum sensing is necessary for resolving the problem of SSDF attacks and ensuring the security of the whole system. In this article, a dynamic trust mechanism based on Beta reputation system is proposed to resist SSDF attacks. We introduce the time proportion coefficient, which is set in line with the historical sensing behaviors of cognitive users, and the reputation value of users can be obtained accordingly, besides, the rise factor and fall factor are introduced as well. Cognitive users that are qualified to participate in the cooperative sensing can be selected in accordance with users' reputation values. The final global decision can be obtained by linear fusion method in the center data fusion. Simulation results show that, in the proposed reputation mechanism, the SSDF attack is effectively suppressed since the reputation of malicious cognitive users are significantly reduced, thus improving the accuracy of cooperative spectrum sensing.

For the future information warfare, a single jamming mode is not effective due to the complex electromagnetic environment. With the rapid development of cognitive radio technology, collaborative jamming based on cognitive radio, namely cognitive jamming, has attracted wide attention in the field of electronic warfare. In collaborative jamming, the problem of decision-making based on collaboration becomes one of the key issues. Traditional decision-making based on ant colony algorithm takes too many iterations, which cannot always ensure the probability of optimization. Against the issue, this paper proposes a cognitive cooperative-jamming decision method based on bee colony algorithm, which searches the global optimum solution according to the process of the honey bee group looking for high quality sources. In the case of lack of prior condition, the proposed algorithm generates fitness function according to jamming effect. Then by the rule of greed, jamming decision matrix is decided according to jammer task allocation form. The role of bees in the proposed algorithm are changed between collection and observation, which is benefit to improve the convergence speed. By this way, it can improve the speed of convergence and find an optimal decision making scheme in the scenario with multiple jammer and multiple radar. Simulation results show that the proposed decision-making method can find the optimal decision scheme with the best jamming effect and has a fast convergence speed comparing with decision-making based on ant colony algorithm. The application of bee colony algorithm in the field of cooperative-jamming decision provides a new idea for jamming decision making.