The network performance and the unmanned aerial vehicle (UAV) number are important objectives when UAVs are placed as communication relays to enhance the multi-agent information exchange. The problem is a non-deterministic polynomial hard (NP-hard) multi-objective optimization problem, instead of generating a Pareto solution, this work focuses on considering both objectives at the same level so as to achieve a balanced solution between them. Based on the property that agents connected to the same UAV are a cluster, two clustering-based algorithms, M-K-means (MKM) and modified fast search and find density of peaks (MFSFDP) methods, are first proposed. Since the former algorithm requires too much computational time and the latter one requires too many relays, an algorithm for the balanced network performance and relay number (BPN) is proposed by discretizing the area to avoid missing the optimal relay positions and defining a new local density function to reflect the network performance metric. Simulation results demonstrate that the proposed algorithms are feasible and effective. Comparisons between these algorithms show that the BPN algorithm uses fewer relay UAVs than the MFSFDP and classic set-covering based algorithm, and its computational time is far less than the MKM algorithm.

In cognitive radio networks, spectrum sensing is one of the most important functions to identify available spectrum for improving the spectrum utilization. Due to the open characteristic of the wireless electromagnetic environment, the wireless network is vulnerable to be attacked by malicious users (MUs), and spectrum sensing data falsification (SSDF) attack is one of the most harmful attacks on spectrum sensing performance. In this article, an algorithm based on the evidence theory and fuzzy entropy is proposed to resist SSDF attacks. In this algorithm, secondary users (SUs) obtain the corresponding degree of membership function and basic probability assignment function based on the local energy detection result. The new conflicting coefficient is calculated based on the evidence distance and classical conflicting coefficient, and the conflicting weight of the evidence is obtained. The fuzzy weight is calculated by the fuzzy entropy. The credibility weight is obtained by updating the credibility. On this basis, the probability assignment function of the evidence is corrected, and the final result is obtained by using the fusion formula. Simulation results show that the proposed algorithm has a higher detection probability and lower false alarm probability than other algorithms. It can effectively defend against SSDF attacks and improve the performance of spectrum sensing.

This paper proposes an efficient scheme to reduce the pre-correlation bandwidth effect in the global navigation satellite system (GNSS) receiver filtering process. It is mainly based on the application of a spectral transformation to the satellite-emitted signal that effectively reduces its band. At the receiver's end, this operation causes the spreading of noise over a much wider band than that used by the radio frequency stage. Consequently, the resulting auto-correlation function in the acquisition process acquires properties that enhance considerably the performance of the receiver in the presence of the multipath and noise disturbing phenomena. The simulation results demonstrate that the proposed method is a plausible solution for both multipath and noise problems in the GNSS applications for any limited value of the pre-correlation bandwidth in the receiver filter.

Color image super-resolution reconstruction based on the sparse representation model usually adopts the regularization norm (e.g., $L_{1}$ or $L_{2})$. These methods have limited ability to keep image texture detail to some extent and are easy to cause the problem of blurring details and color artifacts in color reconstructed images. This paper presents a color super-resolution reconstruction method combining the $L_{2 / 3}$ sparse regularization model with color channel constraints. The method converts the low-resolution color image from RGB to YCbCr. The $L_{2 / 3}$ sparse regularization model is designed to reconstruct the brightness channel of the input low-resolution color image. Then the color channel-constraint method is adopted to remove artifacts of the reconstructed high-resolution image. The method not only ensures the reconstruction quality of the color image details, but also improves the removal ability of color artifacts. The experimental results on natural images validate that our method has improved both subjective and objective evaluation.

Automatic video mosaicking is a challenging task in computer vision. Current researches consider either panoramic or mapping tasks on short videos. In this paper, an automatic mosaicking algorithm is proposed for both mapping and panoramic tasks based on the adapted key-frame on videos of any length. The speeded up robust features (SURF) and the grid motion statistic (GMS) algorithm are used for feature extraction and matching between consecutive frames, which are used to compute the transformation. In order to reduce the influence of the accumulated error during image stitching, an evaluation metric is put forward for the transformation matrix. Besides, a self-growth method is employed to stitch the global image for long videos. The algorithm is evaluated by using aerial-view and panoramic videos respectively on the graphic processing unit (GPU) device, which can satisfy the real-time requirement. The experimental results demonstrate that the proposed algorithm is able to achieve a better performance than the state-of-art.

The multi-agent system is the optimal solution to complex intelligent problems. In accordance with the game theory, the concept of loyalty is introduced to analyze the relationship between agents' individual income and global benefits and build the logical architecture of the multi-agent system. Besides, to verify the feasibility of the method, the cyclic neural network is optimized, the bi-directional coordination network is built as the training network for deep learning, and specific training scenes are simulated as the training background. After a certain number of training iterations, the model can learn simple strategies autonomously. Also, as the training time increases, the complexity of learning strategies rises gradually. Strategies such as obstacle avoidance, firepower distribution and collaborative cover are adopted to demonstrate the achievability of the model. The model is verified to be realizable by the examples of obstacle avoidance, fire distribution and cooperative cover. Under the same resource background, the model exhibits better convergence than other deep learning training networks, and it is not easy to fall into the local endless loop. Furthermore, the ability of the learning strategy is stronger than that of the training model based on rules, which is of great practical values.

Due to the requirement of anti-interception and the limitation of processing capability of the fusion center, the subarray selection is very important for the distributed multiple-input multiple-output (MIMO) radar system, especially in the hostile environment. In such conditions, an efficient subarray selection strategy is proposed for MIMO radar performing tasks of target tracking and detection. The goal of the proposed strategy is to minimize the worst-case predicted posterior Cramer-Rao lower bound (PCRLB) while maximizing the detection probability for a certain region. It is shown that the subarray selection problem is NP-hard, and a modified particle swarm optimization (MPSO) algorithm is developed as the solution strategy. A large number of simulations verify that the MPSO can provide close performance to the exhaustive search (ES) algorithm. Furthermore, the MPSO has the advantages of simpler structure and lower computational complexity than the multi-start local search algorithm.

As a core part of the electronic warfare (EW) system, de-interleaving is used to separate interleaved radar signals. The de-interleaving algorithm based on the fuzzy adaptive resonance theory (fuzzy ART) is plagued by the problems of premature saturation and performance improving dilemma. This study proposes a dual fuzzy vigilance ART (DFV-ART) algorithm to address these problems and make the following improvements. Firstly, a correction method is introduced to prevent the network from prematurely saturating; then, the fuzzy vigilance models (FVM) are constructed to replace the conventional vigilance parameter, reducing the error probability in the overlapping region; finally, a dual vigilance mechanism is introduced to solve the performance improving dilemma. Simulation results show that the proposed algorithm could improve the clustering accuracy (quantization error dropped 60%) and the de-interleaving performance (clustering quality increased by 10%) while suppressing the excessive proliferation of categories.

The range and angle information of the frequency diverse array (FDA) cannot be exclusively determined at the output of the array because of the range-angle coupled transmit beampattern. The best decoupling approach is to form a dot-shaped beampattern rather than the S-shaped beampattern of the basic FDA. Considering the degradation of the output signal-to-interference-plus-noise ratio (SINR) caused by the coupled beampattern, we propose a dot-shaped beamforming method based on the analyzed overlapping subarray-based using a logarithmic offset and a subarray-based planar FDA using a logarithmically increasing frequency offset, with elements transmitting at multiple frequencies. Several simulation results demonstrate the effectiveness of the proposed method in transmit energy focusing, sidelobe suppression and array resolution.

In the imaging observation system, imaging task scheduling is an important topic. Most scholars study the imaging task scheduling from the perspective of static priority, and only a few from the perspective of dynamic priority. However, the priority of the imaging task is dynamic in actual engineering. To supplement the research on imaging observation, this paper proposes the task priority model, dynamic scheduling strategy and Heuristic algorithm. At first, this paper analyzes the relevant theoretical basis of imaging observation, decomposes the task priority into four parts, including target priority, imaging task priority, track, telemetry & control (TT&C) requirement priority and data transmission requirement priority, summarizes the attribute factors that affect the above four types of priority in detail, and designs the corresponding priority model. Then, this paper takes the emergency tasks scheduling problem as the background, proposes the dynamic scheduling strategy and heuristic algorithm. Finally, the task priority model, dynamic scheduling strategy and heuristic algorithm are verified by experiments.

In the case of the given design variables and constraint functions, this paper is concerned with the rapid overall parameters design of trajectory, propulsion and aerodynamics for long-range ballistic missiles based on the index of the minimum take-off mass. In contrast to the traditional subsystem independent design, this paper adopts the research idea of the combination of the subsystem independent design and the multisystem integration design. Firstly, the trajectory, propulsion and aerodynamics of the subsystem are separately designed by the engineering design, including the design of the minimum energy trajectory, the computation of propulsion system parameters, and the calculation of aerodynamic coefficient and dynamic derivative of the missile by employing the software of missile DATCOM. Then, the uniform design method is used to simplify the constraint conditions and the design variables through the integration design, and the accurate design of the optimized variables would be accomplished by adopting the uniform particle swarm optimization (PSO) algorithm. Finally, the automation design software is written for the three-stage solid ballistic missile. The take-off mass of 29~850 kg is derived by the subsystem independent design, and 20 constraints are reduced by employing the uniform design on the basis of 29 design variables and 32 constraints, and the take-off mass is dropped by 1~850 kg by applying the combination of the uniform design and PSO. The simulation results demonstrate the effectiveness and feasibility of the proposed hybrid optimization technique.

The single machine scheduling problem which involves uncertain job due dates is one of the most important issues in the real make-to-order environment. To deal with the uncertainty, this paper establishes a robust optimization model by minimizing the maximum tardiness in the worst case scenario over all jobs. Unlike the traditional stochastic programming model which requires exact distributions, our model only needs the information of due date intervals. The worst case scenario for a given sequence that belongs to a set containing only $n$ scenarios is proved, where $n$ is the number of jobs. Then, the model is simplified and reformulated as an equivalent mixed 0-1 integer linear programming (MILP) problem. To solve the MILP problems efficiently, a heuristic approach is proposed based on a robust dominance rule. The experimental results show that the proposed method has the advantages of robustness and high calculating efficiency, and it is feasible for large-scale problems.

This study aims to reflect the information coverage grey number and the interaction between attributes in grey relational decision making. Therefore, a multi-attribute decision method based on the grey information coverage interaction relational degree (GIRD) is proposed. Firstly, this paper defines the information coverage grey number, and establishes the GIRD model by using the Choquet fuzzy integral and grey relational principle. It proves that the proposed model not only is the general and unified form of the point relational degree, interval relational degree, mixed relational degree and grey fuzzy integral relational degree, but also can effectively deal with the interaction between attributes. Further, a decision making example of evaluating the industrial operation quality for 14 cities in Hunan province of China is provided to highlight the implementation, availability, and feasibility of the proposed decision model.

This paper proposes partially norm-preserving filtering for a class of spacecraft in the presence of time-varying, but constant-magnitude maneuver. The augmented state Kalman filter (ASKF) is commonly used to track the maneuvering spacecraft with unknown constant propulsion; however, if the maneuver varies via time, the estimation performance will be degraded. To promote the tracking performance of the ASKF in case of time-invariant, constant-magnitude disturbance, the partially norm-preserving ASKF is developed by applying the norm constraint on the unknown maneuver. The proposed estimator, which is decomposed into two partial estimators and iteratively propagated in turns, projects the unconstrained maneuver estimation onto the Euclidian surface spanned by the norm constraint. The illustrative numerical example is provided to show the efficiency of the proposed method.

In order to accelerate the convergence speed of iterative learning control (ILC), taking the P-type learning algorithm as an example, a correction algorithm with kernel-based auto-associative is proposed for the linear system. The learning mechanism of human brain associative memory is introduced to the traditional ILC. The control value of the subsequent time is pre-corrected with the current time information by association in each iterative learning process. The learning efficiency of the whole system is improved significantly with the proposed algorithm. Through the rigorous analysis, it shows that under this new designed ILC scheme, the uniform convergence of the state tracking error is guaranteed. Numerical simulations illustrate the effectiveness of the proposed associative control scheme and the validity of the conclusion.

In this paper, a practical decoupling control scheme for fighter aircraft is proposed to achieve high angle of attack (AOA) tracking and super maneuver action by utilizing the thrust vector technology. Firstly, a six degree-of-freedom (DOF) nonlinear model with 12 variables is given. Due to low sufficiency of the aerodynamic actuators at high AOA, a thrust vector model with rotatable engine nozzles is derived. Secondly, the active disturbance rejection control (ADRC) is used to realize a three-channel decoupling control such that a strong coupling between different channels can be treated as total disturbance, which is estimated by the designed extended state observer. The control surface allocation is implemented by the traditional daisy chain method. Finally, the effectiveness of the presented control strategy is demonstrated by some numerical simulation results.

This paper proposes a multivariable fixed-time leaderfollower formation control method for a group of nonholonomic mobile robots, which has the ability to estimate multiple uncertainties. Firstly, based on the state space model of the leader-follower formation, a multivariable fixed-time formation kinematics controller is designed. Secondly, to overcome uncertainties existing in the nonholonomic mobile robot system, such as load change, friction, external disturbance, a multivariable fixed-time torque controller based on the fixed-time disturbance observer at the dynamic level is designed. The designed torque controller is cascaded with the formation controller and finally realizes accurate estimation of the uncertain part of the system, the follower tracking of reference velocity and the desired formation of the leader and the follower in a fixed-time. The fixed-time upper bound is completely determined by the controller parameters, which is independent of the initial state of the system. The multivariable fixed-time control theory and the Lyapunov method are adopted to ensure the system stability. Finally, the effectiveness of the proposed algorithm is verified by the experimental simulation.

The value range of the failure threshold will generate an uncertain influence on the prediction results for the remaining useful life (RUL) of equipment. Most of the existing studies on the RUL prediction assume that the failure threshold is a fixed value, as they have difficulty in reflecting the random variation of the failure threshold. In connection with the inadequacies of the existing research, an in-depth analysis is carried out to study the effect of the random failure threshold (RFT) on the prediction results for the RUL. First, a nonlinear degradation model with unit-to-unit variability and measurement error is established based on the nonlinear Wiener process. Second, the expectation-maximization (EM) algorithm is used to solve the estimated values of the parameters of the prior degradation model, and the Bayesian method is used to iteratively update the posterior distribution of the random coefficients. Then, the effects of three types of RFT constraint conditions on the prediction results for the RUL are analyzed, and the probability density function (PDF) of the RUL is derived. Finally, the degradation data of aero-turbofan engines are used to verify the correctness and advantages of the method.

Due to the limitations of the existing fault detection methods in the embryonic cellular array (ECA), the fault detection coverage cannot reach 100%. In order to evaluate the reliability of the ECA more accurately, embryonic cell and its input and output (I/O) resources are considered as a whole, named functional unit (FU). The FU fault detection coverage parameter is introduced to ECA reliability analysis, and a new ECA reliability evaluation method based on the Markov status graph model is proposed. Simulation experiment results indicate that the proposed ECA reliability evaluation method can evaluate the ECA reliability more effectively and accurately. Based on the proposed reliability evaluation method, the influence of parameters change on the ECA reliability is studied, and simulation experiment results show that ECA reliability can be improved by increasing the FU fault detection coverage and reducing the FU failure rate. In addition, by increasing the scale of the ECA, the reliability increases to the maximum first, and then it will decrease continuously. ECA reliability variation rules can not only provide theoretical guidance for the ECA optimization design, but also point out the direction for further research.