Thinning of antenna arrays has been a popular topic for the last several decades. With increasing computational power, this optimization task acquired a new hue. This paper suggests a genetic algorithm as an instrument for antenna array thinning. The algorithm with a deliberately chosen fitness function allows synthesizing thinned linear antenna arrays with low peak sidelobe level (SLL) while maintaining the half-power beamwidth (HPBW) of a full linear antenna array. Based on results from existing papers in the field and known approaches to antenna array thinning, a classification of thinning types is introduced. The optimal thinning type for a linear thinned antenna array is determined on the basis of a maximum attainable SLL. The effect of thinning coefficient on main directional pattern characteristics, such as peak SLL and HPBW, is discussed for a number of amplitude distributions.
Most of the existing direction of arrival (DOA) estimation algorithms are applied under the assumption that the array manifold is ideal. In practical engineering applications, the existence of non-ideal conditions such as mutual coupling between array elements, array amplitude and phase errors, and array element position errors leads to defects in the array manifold, which makes the performance of the algorithm decline rapidly or even fail. In order to solve the problem of DOA estimation in the presence of amplitude and phase errors and array element position errors, this paper introduces the first-order Taylor expansion equivalent model of the received signal under the uniform linear array from the Bayesian point of view. In the solution, the amplitude and phase error parameters and the array element position error parameters are regarded as random variables obeying the Gaussian distribution. At the same time, the expectation-maximization algorithm is used to update the probability distribution parameters, and then the two error parameters are solved alternately to obtain more accurate DOA estimation results. Finally, the effectiveness of the proposed algorithm is verified by simulation and experiment.
According to the measurement principle of the traditional interferometer, a narrowband signal model is established and used, however, for wideband signals or multiple signals, this model is invalid. For the problems of direction finding with interferometer for wideband signals and multiple signals scene, a frequency domain phase interferometer is proposed and the concrete implementation scheme is given. The proposed method computes the phase difference in frequency domain, and finds multi-target results with judging the spectrum amplitude changing, and uses the frequency phase difference to compute the arrival angle. Theoretical analysis and simulation results show that the proposed method effectively solves the problem of the angle estimation with phase interferometer for wideband signals, and has good performance in multiple signals scene with non-overlapping spectrum or partially overlapping. In addition, the wider the signal bandwidth, the better direction finding performance of this algorithm.
In this paper, we study the orthogonal time frequency space signal transmission over multi-path channel in the presence of phase noise (PHN) at both sides of millimeter wave (mmWave) communication links. The statistics characteristics of the PHN-induced common phase error and inter-Doppler interference are investigated. Then, a column-shaped pilot structure is designed, and training pilots are used to realize linear-complexity PHN tracking and compensation. Numerical results demonstrate that the proposed scheme enables the signal to noise ratio loss to be restrained within 1 dB in contrast to the no PHN case.
Fog computing has emerged as an important technology which can improve the performance of computation-intensive and latency-critical communication networks. Nevertheless, the fog computing Internet-of-Things (IoT) systems are susceptible to malicious eavesdropping attacks during the information transmission, and this issue has not been adequately addressed. In this paper, we propose a physical-layer secure fog computing IoT system model, which is able to improve the physical layer security of fog computing IoT networks against the malicious eavesdropping of multiple eavesdroppers. The secrecy rate of the proposed model is analyzed, and the quantum galaxy–based search algorithm (QGSA) is proposed to solve the hybrid task scheduling and resource management problem of the network. The computational complexity and convergence of the proposed algorithm are analyzed. Simulation results validate the efficiency of the proposed model and reveal the influence of various environmental parameters on fog computing IoT networks. Moreover, the simulation results demonstrate that the proposed hybrid task scheduling and resource management scheme can effectively enhance secrecy performance across different communication scenarios.
In this paper, the newly-derived maximum correntropy Kalman filter (MCKF) is re-derived from the M-estimation perspective, where the MCKF can be viewed as a special case of the M-estimations and the Gaussian kernel function is a special case of many robust cost functions. Based on the derivation process, a unified form for the robust Gaussian filters (RGF) based on M-estimation is proposed to suppress the outliers and non-Gaussian noise in the measurement. The RGF provides a unified form for one Gaussian filter with different cost functions and a unified form for one robust filter with different approximating methods for the involved Gaussian integrals. Simulation results show that RGF with different weighting functions and different Gaussian integral approximation methods has robust anti-jamming performance.
In order to obtain better inverse synthetic aperture radar (ISAR) image, a novel structure-enhanced spatial spectrum is proposed for estimating the incoherence parameters and fusing multiband. The proposed method takes full advantage of the original electromagnetic scattering data and its conjugated form by combining them with the novel covariance matrices. To analyse the superiority of the modified algorithm, the mathematical expression of equivalent signal to noise ratio (SNR) is derived, which can validate our proposed algorithm theoretically. In addition, compared with the conventional matrix pencil (MP) algorithm and the conventional root-multiple signal classification (Root-MUSIC) algorithm, the proposed algorithm has better parameter estimation performance and more accurate multiband fusion results at the same SNR situations. Validity and effectiveness of the proposed algorithm is demonstrated by simulation data and real radar data.
Solar radio burst (SRB) is one of the main natural interference sources of Global Positioning System (GPS) signals and can reduce the signal-to-noise ratio (SNR), directly affecting the tracking performance of GPS receivers. In this paper, a tracking algorithm based on the adaptive Kalman filter (AKF) with carrier-to-noise ratio estimation is proposed and compared with the conventional second-order phase-locked loop tracking algorithms and the improved Sage-Husa adaptive Kalman filter (SHAKF) algorithm. It is discovered that when the SRBs occur, the improved SHAKF and the AKF with carrier-to-noise ratio estimation enable stable tracking to loop signals. The conventional second-order phase-locked loop tracking algorithms fail to track the receiver signal. The standard deviation of the carrier phase error of the AKF with carrier-to-noise ratio estimation outperforms 50.51% of the improved SHAKF algorithm, showing less fluctuation and better stability. The proposed algorithm is proven to show more excellent adaptability in the severe environment caused by the SRB occurrence and has better tracking performance.
Ant colony optimization (ACO) is a random search algorithm based on probability calculation. However, the uninformed search strategy has a slow convergence speed. The Bayesian algorithm uses the historical information of the searched point to determine the next search point during the search process, reducing the uncertainty in the random search process. Due to the ability of the Bayesian algorithm to reduce uncertainty, a Bayesian ACO algorithm is proposed in this paper to increase the convergence speed of the conventional ACO algorithm for image edge detection. In addition, this paper has the following two innovations on the basis of the classical algorithm, one of which is to add random perturbations after completing the pheromone update. The second is the use of adaptive pheromone heuristics. Experimental results illustrate that the proposed Bayesian ACO algorithm has faster convergence and higher precision and recall than the traditional ant colony algorithm, due to the improvement of the pheromone utilization rate. Moreover, Bayesian ACO algorithm outperforms the other comparative methods in edge detection task.
The fifth-generation (5G) communication requires a highly accurate estimation of the channel state information (CSI) to take advantage of the massive multiple-input multiple-output (MIMO) system. However, traditional channel estimation methods do not always yield reliable estimates. The methodology of this paper consists of deep residual shrinkage network (DRSN) neural network-based method that is used to solve this problem. Thus, the channel estimation approach, based on DRSN with its learning ability of noise-containing data, is first introduced. Then, the DRSN is used to train the noise reduction process based on the results of the least square (LS) channel estimation while applying the pilot frequency subcarriers, where the initially estimated subcarrier channel matrix is considered as a three-dimensional tensor of the DRSN input. Afterward, a mixed signal to noise ratio (SNR) training data strategy is proposed based on the learning ability of DRSN under different SNRs. Moreover, a joint mixed scenario training strategy is carried out to test the multi scenarios robustness of DRSN. As for the findings, the numerical results indicate that the DRSN method outperforms the spatial-frequency-temporal convolutional neural networks (SF-CNN) with similar computational complexity and achieves better advantages in the full SNR range than the minimum mean squared error (MMSE) estimator with a limited dataset. Moreover, the DRSN approach shows robustness in different propagation environments.
This paper considers the short-range sensing implementation in continuous-wave (CW) phased array systems. We specifically address this CW short-range sensing challenges stemming from the self-interference cancellation (SIC) operation and synthesis requirement of arbitrary beampatterns for the sensing purpose, which has rarely been researched before. In this paper, unlike the only existed work that exploits the heuristic method and shares no analytical solution, an SIC pattern synthesis design is presented with a closed-form solution. By utilizing the null-space projection (NSP) method, the proposed method effectively mitigates the self-interference to enable the in-band full-duplex operation of the array system. Subsequently, the NSP design will be innovatively embedded in a singular value decomposition (SVD) based weighted alternating reserve projection (WARP) approach to efficiently synthesize an arbitrary desired pattern by solving a unique rank-deficient weighted least mean square problem. Numerical results validate the effectiveness of the proposed method in terms of beampattern, SIC performance, and sensing performance.
In low Earth orbit (LEO) satellite networks, on-board energy resources of each satellite are extremely limited. And with the increase of the node number and the traffic transmission pressure, the energy consumption in the networks presents uneven distribution. To achieve energy balance in networks, an energy consumption balancing optimization algorithm of LEO networks based on distance energy factor (DEF) is proposed. The DEF is defined as the function of the inter-satellite link distance and the cumulative network energy consumption ratio. According to the minimum sum of DEF on inter-satellite links, an energy consumption balancing algorithm based on DEF is proposed, which can realize dynamic traffic transmission optimization of multiple traffic services. It can effectively reduce the energy consumption pressure of core nodes with high energy consumption in the network, make full use of idle nodes with low energy consumption, and optimize the energy consumption distribution of the whole network according to the continuous iterations of each traffic service flow. Simulation results show that, compared with the traditional shortest path algorithm, the proposed method can improve the balancing performance of nodes by 75% under certain traffic pressure, and realize the optimization of energy consumption balancing of the whole network.
In the field of deep space exploration, the rapid development of terahertz spectrometer has put forward higher requirements to the back-end chirp transform spectrometer (CTS) system. In order to simultaneously meet the measurement requirements of wide bandwidth and high accuracy spectral lines, we built a CTS system with an analysis bandwidth of 1 GHz and a frequency resolution of 100 kHz around the surface acoustic wave (SAW) chirp filter with a bandwidth of 1 GHz. In this paper, the relationship between the CTS nonlinear phase error shift model and the basic measurement parameters is studied, and the effect of CTS phase mismatch on the pulse compression waveform is analyzed by simulation. And the expander error optimization method is proposed for the problem that the large nonlinear error of the expander leads to the unbalanced response of the CTS system and the serious distortion of the compressed pulse waveform under large bandwidth. It is verified through simulation and experiment that the method is effective for reducing the root mean square error (RMSE) of the phase of the expander from 18.75° to 6.65°, reducing the in-band standard deviation of the CTS frequency resolution index from 8.43 kHz to 4.72 kHz, solving the problem of serious distortion of the compressed pulse waveform, and improving the uneven CTS response under large bandwidth.
Using the existing positioning technology can easily obtain high-precision positioning information, which can save resources and reduce complexity when used in the communication field. In this paper, we propose a location-based user scheduling and beamforming scheme for the downlink of a massive multi-user input-output system. Specifically, we combine an analog outer beamformer with a digital inner beamformer. An outer beamformer can be selected from a codebook formed by antenna steering vectors, and then a reduced-complexity inner beamformer based on iterative orthogonal matrices and right triangular matrices (QR) decomposition is applied to cancel inter-user interference. Then, we propose a low-complexity user selection algorithm using location information in this paper. We first derive the geometric angle between channel matrices, which represent the correlation between users. Furthermore, we derive the asymptotic signal to interference-plus-noise ratio (SINR) of the system in the context of two-stage beamforming using random matrix theory (RMT), taking into account inter-channel correlations and energies. Simulation results show that the algorithm can achieve higher system and speed while reducing computational complexity.
A spacecraft attitude estimation method based on electromagnetic vector sensors (EMVS) array is proposed, which employs the orthogonally constrained parallel factor (PARAFAC) algorithm and makes use of measurements of the two-dimensional direction-of-arrival (2D-DOA) and polarization angles, aiming to address the issues of incomplete, asynchronous, and inaccurate third-party reference used for attitude estimation in spacecraft docking missions by employing the electromagnetic wave’s three-dimensional (3D) wave structure as a complete third-party reference. Comparative analysis with state-of-the-art algorithms shows significant improvements in estimation accuracy and computational efficiency with this algorithm. Numerical simulations have verified the effectiveness and superiority of this method. A high-precision, reliable, and cost-effective method for rapid spacecraft attitude estimation is provided in this paper.
An improved estimation of distribution algorithm (IEDA) is proposed in this paper for efficient design of metamaterial absorbers. This algorithm establishes a probability model through the selected dominant groups and samples from the model to obtain the next generation, avoiding the problem of building-blocks destruction caused by crossover and mutation. Neighboring search from artificial bee colony algorithm (ABCA) is introduced to enhance the local optimization ability and improved to raise the speed of convergence. The probability model is modified by boundary correction and loss correction to enhance the robustness of the algorithm. The proposed IEDA is compared with other intelligent algorithms in relevant references. The results show that the proposed IEDA has faster convergence speed and stronger optimization ability, proving the feasibility and effectiveness of the algorithm.
The syndrome a posteriori probability of the log-likelihood ratio of intercepted codewords is used to develop an algorithm that recognizes the polar code length and generator matrix of the underlying polar code. Based on the encoding structure, three theorems are proved, two related to the relationship between the length and rate of the polar code, and one related to the relationship between frozen-bit positions, information-bit positions, and codewords. With these three theorems, polar codes can be quickly reconstruced. In addition, to detect the dual vectors of codewords, the statistical characteristics of the log-likelihood ratio are analyzed, and then the information- and frozen-bit positions are distinguished based on the minimum-error decision criterion. The bit rate is obtained. The correctness of the theorems and effectiveness of the proposed algorithm are validated through simulations. The proposed algorithm exhibits robustness to noise and a reasonable computational complexity.
Nowadays, wireless communication devices turn out to be transportable owing to the execution of the current technologies. The antenna is the most important component deployed for communication purposes. The antenna plays an imperative role in receiving and transmitting the signals for any sensor network. Among varied antennas, micro strip fractal antenna (MFA) significantly contributes to increasing antenna gain. This study employs a hybrid optimization method known as the elephant clan updated grey wolf algorithm to introduce an optimized MFA design. This method optimizes antenna characteristics, including directivity and gain. Here, the factors, including length, width, ground plane length, height, and feed offset-X and feed offset-Y, are taken into account to achieve the best performance of gain and directivity. Ultimately, the superiority of the suggested technique over state-of-the-art strategies is calculated for various metrics such as cost and gain. The adopted model converges to a minimal value of 0.2872. Further, the spider monkey optimization (SMO) model accomplishes the worst performance over all other existing models like elephant herding optimization (EHO), grey wolf optimization (GWO), lion algorithm (LA), support vector regressor (SVR), bacterial foraging–particle swarm optimization (BF-PSO) and shark smell optimization (SSO). Effective MFA design is obtained using the suggested strategy regarding various parameters.
A generalized multiple-mode prolate spherical wave functions (PSWFs) multi-carrier with index modulation approach is proposed with the purpose of improving the spectral efficiency of PSWFs multi-carrier systems. The proposed method, based on the optimized multi-index modulation, does not limit the number of signals in the first and second constellations and abandons the concept of limiting the number of signals in different constellations. It successfully increases the spectrum efficiency of the system while expanding the number of modulation symbol combinations and the index dimension of PSWFs signals. The proposed method outperforms the PSWFs multi-carrier index modulation method based on optimized multiple indexes in terms of spectrum efficiency, but at the expense of system computational complexity and bit error performance. For example, with $n $=10 subcarriers and a bit error rate of 1×10?5, spectral efficiency can be raised by roughly 12.4%.
In this paper, the reactive splitter network and metasurface are proposed to radiate the wide-beam isolated element pattern and suppress mutual coupling (MC) of the low-profile phased array with the triangular lattice, respectively. Thus, broadband wide-angle impedance matching (WAIM) is implemented to promote two-dimensional (2D) wide scanning. For the isolated element, to radiate the wide-beam patterns approximating to the cosine form, two identical slots backed on one substrate integrated cavity are excited by the feeding network consisting of a reactive splitter and two striplines connected with splitter output paths. For adjacent elements staggered with each other, with the metasurface superstrate, the even-mode coupling voltages on the reactive splitter are cancelled out, yielding reduced MC. With the suppression of MC and the compensation of isolated element patterns, WAIM is realized to achieve 2D wide-angle beam steering up to ± 65° in E-plane, ± 45° in H-plane and ± 60° in D-plane from 4.9 GHz to 5.85 GHz.
Extensive experiments suggest that kurtosis-based fingerprint features are effective for specific emitter identification (SEI). Nevertheless, the lack of mechanistic explanation restricts the use of fingerprint features to a data-driven technique and further reduces the adaptability of the technique to other datasets. To address this issue, the mechanism how the phase noise of high-frequency oscillators and the nonlinearity of power amplifiers affect the kurtosis of communication signals is investigated. Mathematical models are derived for intentional modulation (IM) and unintentional modulation (UIM). Analysis indicates that the phase noise of high-frequency oscillators and the nonlinearity of power amplifiers affect the kurtosis frequency and amplitude, respectively. A novel SEI method based on frequency and amplitude of the signal kurtosis (FA-SK) is further proposed. Simulation and real-world experiments validate theoretical analysis and also confirm the efficiency and effectiveness of the proposed method.
The Ocean 4A scatterometer, expected to be launched in 2024, is poised to be the world’s first spaceborne microwave scatterometer utilizing a digital beamforming system. To ensure high-precision measurements and performance stability across diverse environments, stringent requirements are placed on the dynamic range of its receiving system. This paper provides a detailed exposition of a field-programmable gate array (FPGA)-based automatic gain control (AGC) design for the spaceborne scatterometer. Implemented on an FPGA, the algorithm harnesses its parallel processing capabilities and high-speed performance to monitor the received echo signals in real time. Employing an adaptive AGC algorithm, the system generates gain control codes applicable to the intermediate frequency variable attenuator, enabling rapid and stable adjustment of signal amplitudes from the intermediate frequency amplifier to an optimal range. By adopting a purely digital processing approach, experimental results demonstrate that the AGC algorithm exhibits several advantages, including fast convergence, strong flexibility, high precision, and outstanding stability. This innovative design lays a solid foundation for the high-precision measurements of the Ocean 4A scatterometer, with potential implications for the future of spaceborne microwave scatterometers.
To tackle the challenges of intractable parameter tuning, significant computational expenditure and imprecise model-driven sparse-based direction of arrival (DOA) estimation with array error (AE), this paper proposes a deep unfolded amplitude-phase error self-calibration network. Firstly, a sparse-based DOA model with an array convex error restriction is established, which gets resolved via an alternating iterative minimization (AIM) algorithm. The algorithm is then unrolled to a deep network known as AE-AIM Network (AE-AIM-Net), where all parameters are optimized through multi-task learning using the constructed complete dataset. The results of the simulation and theoretical analysis suggest that the proposed unfolded network achieves lower computational costs compared to typical sparse recovery methods. Furthermore, it maintains excellent estimation performance even in the presence of array magnitude-phase errors.
A millimeter-wave (mmW) broadband dual circularly polarized (dual-CP) antenna with high port isolation is proposed in this paper. The dual-CP performance is realized based on the symmetrical septum circular polarizer based on the gap waveguide (GWG) technology. Two sets of symmetrical septum circular polarizers are used for common aperture combination, achieving the broadband dual-CP characteristics. Taking advantage of GWG structure without good electrical contact, the antenna can also be fabricated and assembled easily in the mmW band. The principle analysis of the antenna is given, and the antenna is simulated and fabricated. The measured results show that the bandwidth for S11 lower than ?10.7 dB and the axial ratio (AR) lower than 2.90 dB in 75?110 GHz, with realative bandwidth of 38%. Over the frequency band, the gain is higher than 9.16 dBic, and the dual-CP port isolation is greater than 32 dB. The proposed antenna with dual-CP and highly isolated in a wide bandwidth range has broad application prospects in the field of mmW communication.
The acquisition, analysis, and prediction of the radar cross section (RCS) of a target have extremely important strategic significance in the military. However, the RCS values at all azimuths are hardly accessible for non-cooperative targets, due to the limitations of radar observation azimuth and detection resources. Despite their efforts to predict the azimuth-dimensional RCS value, traditional methods based on statistical theory fails to achieve the desired results because of the azimuth sensitivity of the target RCS. To address this problem, an improved neural basis expansion analysis for interpretable time series forecasting (N-BEATS) network considering the physical model prior is proposed to predict the azimuth-dimensional RCS value accurately. Concretely, physical model-based constraints are imposed on the network by constructing a scattering-center module based on the target scattering-center model. Besides, a superimposed seasonality module is involved to better capture high-frequency information, and augmenting the training set provides complementary information for learning predictions. Extensive simulations and experimental results are provided to validate the effectiveness of the proposed method.
In this paper, a feature selection method for determining input parameters in antenna modeling is proposed. In antenna modeling, the input feature of artificial neural network (ANN) is geometric parameters. The selection criteria contain correlation and sensitivity between the geometric parameter and the electromagnetic (EM) response. Maximal information coefficient (MIC), an exploratory data mining tool, is introduced to evaluate both linear and nonlinear correlations. The EM response range is utilized to evaluate the sensitivity. The wide response range corresponding to varying values of a parameter implies the parameter is highly sensitive and the narrow response range suggests the parameter is insensitive. Only the parameter which is highly correlative and sensitive is selected as the input of ANN, and the sampling space of the model is highly reduced. The modeling of a wideband and circularly polarized antenna is studied as an example to verify the effectiveness of the proposed method. The number of input parameters decreases from 8 to 4. The testing errors of |S11| and axis ratio are reduced by 8.74% and 8.95%, respectively, compared with the ANN with no feature selection.
Low-frequency signals have been proven valuable in the fields of target detection and geological exploration. Nevertheless, the practical implementation of these signals is hindered by large antenna diameters, limiting their potential applications. Therefore, it is imperative to study the creation of low-frequency signals using antennas with suitable dimensions. In contrast to conventional mechanical antenna techniques, our study generates low-frequency signals in the spatial domain utilizing the principle of the Doppler effect. We also defines the antenna array architecture, the timing sequency, and the radiating element signal waveform, and provides experimental prototypes including 8/64 antennas based on earlier research. In the conducted experiments, 121 MHz, 40 MHz, and 10 kHz composite signals are generated by 156 MHz radiating element signals. The composite signal spectrum matches the simulations, proving our low-frequency signal generating method works. This holds significant implications for research on generating low-frequency signals with small-sized antennas.
Low Earth orbit (LEO) satellite networks exhibit distinct characteristics, e.g., limited resources of individual satellite nodes and dynamic network topology, which have brought many challenges for routing algorithms. To satisfy quality of service (QoS) requirements of various users, it is critical to research efficient routing strategies to fully utilize satellite resources. This paper proposes a multi-QoS information optimized routing algorithm based on reinforcement learning for LEO satellite networks, which guarantees high level assurance demand services to be prioritized under limited satellite resources while considering the load balancing performance of the satellite networks for low level assurance demand services to ensure the full and effective utilization of satellite resources. An auxiliary path search algorithm is proposed to accelerate the convergence of satellite routing algorithm. Simulation results show that the generated routing strategy can timely process and fully meet the QoS demands of high assurance services while effectively improving the load balancing performance of the link.
Piezo actuators are widely used in ultra-precision fields because of their high response and nano-scale step length. However, their hysteresis characteristics seriously affect the accuracy and stability of piezo actuators. Existing methods for fitting hysteresis loops include operator class, differential equation class, and machine learning class. The modeling cost of operator class and differential equation class methods is high, the model complexity is high, and the process of machine learning, such as neural network calculation, is opaque. The physical model framework cannot be directly extracted. Therefore, the sparse identification of nonlinear dynamics (SINDy) algorithm is proposed to fit hysteresis loops. Furthermore, the SINDy algorithm is improved. While the SINDy algorithm builds an orthogonal candidate database for modeling, the sparse regression model is simplified, and the Relay operator is introduced for piecewise fitting to solve the distortion problem of the SINDy algorithm fitting singularities. The Relay-SINDy algorithm proposed in this paper is applied to fitting hysteresis loops. Good performance is obtained with the experimental results of open and closed loops. Compared with the existing methods, the modeling cost and model complexity are reduced, and the modeling accuracy of the hysteresis loop is improved.
Anti-jamming performance evaluation has recently received significant attention. For Link-16, the anti-jamming performance evaluation and selection of the optimal anti-jamming technologies are urgent problems to be solved. A comprehensive evaluation method is proposed, which combines grey relational analysis (GRA) and cloud model, to evaluate the anti-jamming performances of Link-16. Firstly, on the basis of establishing the anti-jamming performance evaluation indicator system of Link-16, the linear combination of analytic hierarchy process (AHP) and entropy weight method (EWM) are used to calculate the combined weight. Secondly, the qualitative and quantitative concept transformation model, i.e., the cloud model, is introduced to evaluate the anti-jamming abilities of Link-16 under each jamming scheme. In addition, GRA calculates the correlation degree between evaluation indicators and the anti-jamming performance of Link-16, and assesses the best anti-jamming technology. Finally, simulation results prove that the proposed evaluation model can achieve the objective of feasible and practical evaluation, which opens up a novel way for the research of anti-jamming performance evaluations of Link-16.
