This paper tackles the formation-containment control problem of fixed-wing unmanned aerial vehicle (UAV) swarm with model uncertainties for dynamic target tracking in three-dimensional space in the faulty case of UAVs ’ actuator and sensor. The fixed-wing UAV swarm under consideration is organized as a “multi-leader-multi-follower” structure, in which only several leaders can obtain the dynamic target information while others only receive the neighbors’ information through the communication network. To simultaneously realize the formation, containment, and dynamic target tracking, a two-layer control framework is adopted to decouple the problem into two subproblems: reference trajectory generation and trajectory tracking. In the upper layer, a distributed finite-time estimator (DFTE) is proposed to generate each UAV ’s reference trajectory in accordance with the control objective. Subsequently, a distributed composite robust fault-tolerant trajectory tracking controller is developed in the lower layer, where a novel adaptive extended super-twisting (AESTW) algorithm with a finite-time extended state observer (FTESO) is involved in solving the robust trajectory tracking control problem under model uncertainties, actuator, and sensor faults. The proposed controller simultaneously guarantees rapidness and enhances the system ’s robustness with fewer chattering effects. Finally, corresponding simulations are carried out to demonstrate the effectiveness and competitiveness of the proposed two-layer fault-tolerant cooperative control scheme.

In the process of performing a task, autonomous unmanned systems face the problem of scene changing, which requires the ability of real-time decision-making under dynamically changing scenes. Therefore, taking the unmanned system coordinative region control operation as an example, this paper combines knowledge representation with probabilistic decision-making and proposes a role-based Bayesian decision model for autonomous unmanned systems that integrates scene cognition and individual preferences. Firstly, according to utility value decision theory, the role-based utility value decision model is proposed to realize task coordination according to the preference of the role that individual is assigned. Then, multi-entity Bayesian network is introduced for situation assessment, by which scenes and their uncertainty related to the operation are semantically described, so that the unmanned systems can conduct situation awareness in a set of scenes with uncertainty. Finally, the effectiveness of the proposed method is verified in a virtual task scenario. This research has important reference value for realizing scene cognition, improving cooperative decision-making ability under dynamic scenes, and achieving swarm level autonomy of unmanned systems.

This paper proposes the nonlinear direct data-driven control from theoretical analysis and practical engineering, i.e., unmanned aerial vehicle (UAV) formation flight system. Firstly, from the theoretical point of view, consider one nonlinear closed-loop system with a nonlinear plant and nonlinear feed-forward controller simultaneously. To avoid the complex identification process for that nonlinear plant, a nonlinear direct data-driven control strategy is proposed to design that nonlinear feed-forward controller only through the input-output measured data sequence directly, whose detailed explicit forms are model inverse method and approximated analysis method. Secondly, from the practical point of view, after reviewing the UAV formation flight system, nonlinear direct data-driven control is applied in designing the formation controller, so that the followers can track the leader’s desired trajectory during one small time instant only through solving one data fitting problem. Since most natural phenomena have nonlinear properties, the direct method must be the better one. Corresponding system identification and control algorithms are required to be proposed for those nonlinear systems, and the direct nonlinear controller design is the purpose of this paper.

This paper concerns minimum-energy leader-following formation design and analysis problems of distributed multi-agent systems (DMASs) subjected to randomly switching topologies and aperiodic communication pauses. The critical feature of this paper is that the energy consumption during the formation control process is restricted by the minimum-energy constraint in the sense of the linear matrix inequality. Firstly, the leader-following formation control protocol is proposed based on the relative state information of neighboring agents, where the total energy consumption is considered. Then, minimum-energy leader-following formation design and analysis criteria are presented in the form of the linear matrix inequality, which can be checked by the generalized eigenvalue method. Especially, the value of the minimum-energy constraint is determined. An illustrative simulation is provided to show the effectiveness of the main results.

With the rapid development of informatization, autonomy and intelligence, unmanned swarm formation intelligent operations will become the main combat mode of future wars. Typical unmanned swarm formations such as ground-based directed energy weapon formations, space-based kinetic energy weapon formations, and sea-based carrier-based formations have become the trump card for winning future wars. In a complex confrontation environment, these sophisticated weapon formation systems can precisely strike mobile threat group targets, making them extreme deterrents in joint combat applications. Based on this, first, this paper provides a comprehensive summary of the outstanding advantages, strategic position and combat style of unmanned clusters in joint warfare to highlight their important position in future warfare. Second, a detailed analysis of the technological breakthroughs in four key areas, situational awareness, heterogeneous coordination, mixed combat, and intelligent assessment of typical unmanned aerial vehicle (UAV) swarms in joint warfare, is presented. An in-depth analysis of the UAV swarm communication networking operating mechanism during joint warfare is provided to lay the theoretical foundation for subsequent cooperative tracking and control. Then, an in-depth analysis of the shut-in technology requirements of UAV clusters in joint warfare is provided to lay a theoretical foundation for subsequent cooperative tracking control. Finally, the technical requirements of UAV clusters in joint warfare are analysed in depth so the key technologies can form a closed-loop kill chain system and provide theoretical references for the study of intelligent command operations.

Air-to-air combat tactical decisions for multiple unmanned aerial vehicles (ACTDMU) are a key decision-making step in beyond visual range combat. Complex influencing factors, strong antagonism and real-time requirements need to be considered in the ACTDMU problem. In this paper, we propose a multicriteria game approach to ACTDMU. This approach consists of a multicriteria game model and a Pareto Nash equilibrium algorithm. In this model, we form the strategy profiles for the integration of air-to-air combat tactics and weapon target assignment strategies by considering the correlation between them, and we design the vector payoff functions based on predominance factors. We propose a algorithm of Pareto Nash equilibrium based on preference relations using threshold constraints (PNE-PRTC), and we prove that the solutions obtained by this algorithm are refinements of Pareto Nash equilibrium solutions. The numerical experiments indicate that PNE-PRTC algorithm is considerably faster than the baseline algorithms and the performance is better. Especially on large-scale instances, the Pareto Nash equilibrium solutions can be calculated by PNE-PRTC algorithm at the second level. The simulation experiments show that the multicriteria game approach is more effective than one-side decision approaches such as multiple-attribute decision-making and randomly chosen decisions.

Time synchronization is one of the base techniques in wireless sensor networks (WSNs). This paper proposes a novel time synchronization protocol which is a robust consensus-based algorithm in the existence of transmission delay and packet loss. It compensates for transmission delay and packet loss firstly, and then, estimates clock skew and clock offset in two steps. Simulation and experiment results show that the proposed protocol can keep synchronization error below 2 μs in the grid network of 10 nodes or the random network of 90 nodes. Moreover, the synchronization accuracy in the proposed protocol can keep constant when the WSN works up to a month.

The electric-controlled metasurface antenna array (ECMSAA) with ultra-wideband frequency reconfigurable reflection suppression is proposed and realized. Firstly, an electric- controlled metasurface with ultra-wideband frequency reconfigurable in-phase reflection characteristics is designed. The element of the ECMSAA is constructed by loading the single electric-controlled metasurface unit on the conventional patch antenna element. The radiation properties of the conventional patch antenna and the reflection performance of electric-controlled metasurface are maintained when the antenna and the metasurface are integrated. Thus, the ECMSAA elements have excellent radiation properties and ultra-wideband frequency reconfigurable in-phase reflection characteristics simultaneously. To take a further step, a 6×10 ECMSAA is realized based on the designed metasurface antenna element. Simulated and measured results prove that the reflection of the ECMSAA is dynamically suppressed in the P and L bands. Meanwhile, high-gain and multi-polarization radiation properties of the ECMSAA are achieved. This design method not only realizes the frequency reconfigurable reflection suppression of the antenna array in the ultra-wide frequency band but also provides a way to develop an intelligent low-scattering antenna.

In this paper, we propose an effective full array and sparse array adaptive beamforming scheme that can be applied for multiple desired signals based on the branch-and-bound algorithm. Adaptive beamforming for the multiple desired signals is realized by the improved Capon method. At the same time, the sidelobe constraint is added to reduce the sidelobe level. To reduce the pointing errors of multiple desired signals, the array response phase of the desired signal is firstly optimized by using auxilary variables while keeping the response amplitude unchanged. The whole design is formulated as a convex optimization problem solved by the branch-and-bound algorithm. In addition, the beamformer weight vector is penalized with the modified reweighted ${l_1}$-norm to achieve sparsity. Theoretical analysis and simulation results show that the proposed algorithm has lower sidelobe level, higher SINR, and less pointing error than the state-of-the-art methods in the case of a single expected signal and multiple desired signals.

The conformal array can make full use of the aperture, save space, meet the requirements of aerodynamics, and is sensitive to polarization information. It has broad application prospects in military, aerospace, and communication fields. The joint polarization and direction-of-arrival (DOA) estimation based on the conformal array and the theoretical analysis of its parameter estimation performance are the key factors to promote the engineering application of the conformal array. To solve these problems, this paper establishes the wave field signal model of the conformal array. Then, for the case of a single target, the cost function of the maximum likelihood (ML) estimator is rewritten with Rayleigh quotient from a problem of maximizing the ratio of quadratic forms into those of minimizing quadratic forms. On this basis, rapid parameter estimation is achieved with the idea of manifold separation technology (MST). Compared with the modified variable projection (MVP) algorithm, it reduces the computational complexity and improves the parameter estimation performance. Meanwhile, the MST is used to solve the partial derivative of the steering vector. Then, the theoretical performance of ML, the multiple signal classification (MUSIC) estimator and Cramer-Rao bound (CRB) based on the conformal array are derived respectively, which provides theoretical foundation for the engineering application of the conformal array. Finally, the simulation experiment verifies the effectiveness of the proposed method.

A terahertz (THz) wave transmitted through vegetation experiences both absorption and scattering caused by the air molecules and leaves. This paper presents the scattering attenuation characteristics of vegetation in a THz range. The theoretical path loss model near the vegetation yields the average attenuation of THz waves in a mixed channel composed of air and vegetation leaves. Furthermore, a simplified model of the vegetation structure is obtained for generic vegetation types based on a variety of parameters, such as leaf size, distribution, and moisture content. Finally, based on specific vegetation species and different levels of air humidity, the attenuation characteristics under different conditions are calculated, and the influence of different model parameters on the attenuation characteristics is obtained.

The joint resource block (RB) allocation and power optimization problem is studied to maximize the sum-rate of the vehicle-to-vehicle (V2V) links in the device-to-device (D2D)-enabled V2V communication system, where one feasible cellular user (FCU) can share its RB with multiple V2V pairs. The problem is first formulated as a nonconvex mixed-integer nonlinear programming (MINLP) problem with constraint of the maximum interference power in the FCU links. Using the game theory, two coalition formation algorithms are proposed to accomplish V2V link partitioning and FCU selection, where the transferable utility functions are introduced to minimize the interference among the V2V links and the FCU links for the optimal RB allocation. The successive convex approximation (SCA) is used to transform the original problem into a convex one and the Lagrangian dual method is further applied to obtain the optimal transmit power of the V2V links. Finally, numerical results demonstrate the efficiency of the proposed resource allocation algorithm in terms of the system sum-rate.

The optimal selection of radar clutter model is the premise of target detection, tracking, recognition, and cognitive waveform design in clutter background. Clutter characterization models are usually derived by mathematical simplification or empirical data fitting. However, the lack of standard model labels is a challenge in the optimal selection process. To solve this problem, a general three-level evaluation system for the model selection performance is proposed, including model selection accuracy index based on simulation data, fit goodness indexs based on the optimally selected model, and evaluation index based on the supporting performance to its third-party. The three-level evaluation system can more comprehensively and accurately describe the selection performance of the radar clutter model in different ways, and can be popularized and applied to the evaluation of other similar characterization model selection.

Link16 data link is the communication standard of the joint tactical information distribution system (JTIDS) used by the U.S. military and North Atlantic Treaty Organization, which is applied as the opportunistic illuminator for passive radar in this paper. The time-domain expression of the Link16 signal is established, and its ambiguity function expression is derived. The time-delay dimension and Doppler dimension side peaks of which lead to the appearance of the false target during target detection. To solve the problem, the time-delay dimension and Doppler dimension side peaks suppression methods are proposed. For the problem that the conventional mismatched filter (MMF) cannot suppress the time-delay dimension side peaks, a neighborhood MMF (NMMF) is proposed. Experimental results demonstrate the effectiveness of the proposed methods.

In the complex countermeasure environment, the pulse description words (PDWs) of the same type of multi-function radar emitters are similar in multiple dimensions. Therefore, it is difficult for conventional methods to deinterleave such emitters. In order to solve this problem, a pulse deinterleaving method based on implicit features is proposed in this paper. The proposed method introduces long short-term memory (LSTM) neural networks and statistical analysis to mine new features from similar PDW features, that is, the variation law (implicit features) of pulse sequences of different radiation sources over time. The multi-function radar emitter is deinterleaved based on the pulse sequence variation law. Statistical results show that the proposed method not only achieves satisfactory performance, but also has good robustness.

With the new development trend of multi-resource coordinated Earth observation and the new goal of Earth observation application of “short response time, high observation accuracy, and wide coverage”, space-aeronautics cooperative complex task planning problem has become an urgent problem to be solved. The focus of this problem is to use multiple resources to perform collaborative observations on complex tasks. By analyzing the process from task assignment to receiving task observation results, we propose a multi-layer interactive task planning framework which is composed of a preprocessing method for complex tasks, a task allocation layer, a task planning layer, and a task coordination layer. According to the characteristics of the framework, a hybrid genetic parallel tabu (HGPT) algorithm is proposed on this basis. The algorithm uses genetic annealing algorithm (GAA), parallel tabu (PT) algorithm, and heuristic rules to achieve task allocation, task planning, and task coordination. At the same time, coding improvements, operator design, annealing operations, and parallel calculations are added to the algorithm. In order to verify the effectiveness of the algorithm, simulation experiments under complex task scenarios of different scales are carried out. Experimental results show that this method can effectively solve the problems of observing complex tasks. Meanwhile, the optimization effect and convergence speed of the HGPT is better than that of the related algorithms.

This paper studies a special defense game using unmanned aerial vehicle (UAV) swarm against a fast intruder. The fast intruder applies an offensive strategy based on the artificial potential field method and Apollonius circle to scout a certain destination. As defenders, the UAVs are arranged into three layers: the forward layer, the midfield layer and the back layer. The co-defense mechanism, including the role derivation method of UAV swarm and a guidance law based on the co-defense front point, is introduced for UAV swarm to co-detect the intruder. Besides, five formations are designed for comparative analysis when ten UAVs are applied. Through Monte Carlo experiments and ablation experiment, the effectiveness of the proposed co-defense method has been verified.

Due to people’s increasing dependence on social networks, it is essential to develop a consensus model considering not only their own factors but also the interaction between people. Both external trust relationship among experts and the internal reliability of experts are important factors in decision-making. This paper focuses on improving the scientificity and effectiveness of decision-making and presents a consensus model combining trust relationship among experts and expert reliability in social network group decision-making (SN-GDM). A concept named matching degree is proposed to measure expert reliability. Meanwhile, linguistic information is applied to manage the imprecise and vague information. Matching degree is expressed by a 2-tuple linguistic model, and experts’ preferences are measured by a probabilistic linguistic term set (PLTS). Subsequently, a hybrid weight is explored to weigh experts ’ importance in a group. Then a consensus measure is introduced and a feedback mechanism is developed to produce some personalized recommendations with higher group consensus. Finally, a comparative example is provided to prove the scientificity and effectiveness of the proposed consensus model.

In the field of model-based system assessment, mathematical models are used to interpret the system behaviors. However, the industrial systems in this intelligent era will be more manageable. Various management operations will be dynamically set, and the system will be no longer static as it is initially designed. Thus, the static model generated by the traditional model-based safety assessment (MBSA) approach cannot be used to accurately assess the dependability. There mainly exists three problems. Complex: huge and complex behaviors make the modeling to be trivial manual; Dynamic: though there are thousands of states and transitions, the previous model must be resubmitted to assess whenever new management arrives; Unreusable: as for different systems, the model must be resubmitted by reconsidering both the management and the system itself at the same time though the management is the same. Motivated by solving the above problems, this research studies a formal management specifying approach with the advantages of agility modeling, dynamic modeling, and specification design that can be re-suable. Finally, three typical managements are specified in a series-parallel system as a demonstration to show the potential.

Some attributes are uncertain for evaluation work because of incomplete or limited information and knowledge. It leads to uncertainty in evaluation results. To that end, an evaluation method, uncertainty entropy-based exploratory evaluation (UEEE), is proposed to guide the evaluation activities, which can iteratively and gradually reduce uncertainty in evaluation results. Uncertainty entropy (UE) is proposed to measure the extent of uncertainty. First, the belief degree distributions are assumed to characterize the uncertainty in attributes. Then the belief degree distribution of the evaluation result can be calculated by using uncertainty theory. The obtained result is then checked based on UE to see if it could meet the requirements of decision-making. If its uncertainty level is high, more information needs to be introduced to reduce uncertainty. An algorithm based on the UE is proposed to find which attribute can mostly affect the uncertainty in results. Thus, efforts can be invested in key attribute(s), and the evaluation results can be updated accordingly. This update should be repeated until the evaluation result meets the requirements. Finally, as a case study, the effectiveness of ballistic missiles with uncertain attributes is evaluated by UEEE. The evaluation results show that the target is believed to be destroyed.

Aimed at a multiple traveling salesman problem (MTSP) with multiple depots and closed paths, this paper proposes a k-means clustering donkey and a smuggler algorithm (K-DSA). The algorithm first uses the k-means clustering method to divide all cities into several categories based on the center of various samples; the large-scale MTSP is divided into multiple separate traveling salesman problems (TSPs), and the TSP is solved through the DSA. The proposed algorithm adopts a solution strategy of clustering first and then carrying out, which can not only greatly reduce the search space of the algorithm but also make the search space more fully explored so that the optimal solution of the problem can be more quickly obtained. The experimental results from solving several test cases in the TSPLIB database show that compared with other related intelligent algorithms, the K-DSA has good solving performance and computational efficiency in MTSPs of different scales, especially with large-scale MTSP and when the convergence speed is faster; thus, the advantages of this algorithm are more obvious compared to other algorithms.

As one of the most important part of weapon system of systems (WSoS), quantitative evaluation of reconnaissance satellite system (RSS) is indispensable during its construction and application. Aiming at the problem of nonlinear effectiveness evaluation under small sample conditions, we propose an evaluation method based on support vector regression (SVR) to effectively address the defects of traditional methods. Considering the performance of SVR is influenced by the penalty factor, kernel type, and other parameters deeply, the improved grey wolf optimizer (IGWO) is employed for parameter optimization. In the proposed IGWO algorithm, the opposition-based learning strategy is adopted to increase the probability of avoiding the local optima, the mutation operator is used to escape from premature convergence and differential convergence factors are applied to increase the rate of convergence. Numerical experiments of 14 test functions validate the applicability of IGWO algorithm dealing with global optimization. The index system and evaluation method are constructed based on the characteristics of RSS. To validate the proposed IGWO-SVR evaluation method, eight benchmark data sets and combat simulation are employed to estimate the evaluation accuracy, convergence performance and computational complexity. According to the experimental results, the proposed method outperforms several prediction based evaluation methods, verifies the superiority and effectiveness in RSS operational effectiveness evaluation.