Rich semantic information in natural language increases team efficiency in human collaboration, reduces dependence on high precision data information, and improves adaptability to dynamic environment. We propose a semantic centered cloud control framework for cooperative multi-unmanned ground vehicle (UGV) system. Firstly, semantic modeling of task and environment is implemented by ontology to build a unified conceptual architecture, and secondly, a scene semantic information extraction method combining deep learning and semantic web rule language (SWRL) rules is used to realize the scene understanding and task-level cloud task cooperation. Finally, simulation results show that the framework is a feasible way to enable autonomous unmanned systems to conduct cooperative tasks.

New energy vehicles (NEVs) are gaining wider acceptance as the transportation sector is developing more environmentally friendly and sustainable technology. To solve problems of complex application scenarios and multi-sources heterogenous data for new energy vehicles and weak platform scalability, the framework of an intelligent decision support platform is proposed in this paper. The principle of software and hardware system is introduced. Hadoop is adopted as the software system architecture of the platform. Master-standby redundancy and dual-line redundancy ensure the reliability of the hardware system. In addition, the applications on the intelligent decision support platform in usage patterns recognition, energy consumption, battery state of health and battery safety analysis are also described.

With the rapid development of cloud computing and control theory, a new paradigm of networked control systems called cloud control systems is proposed to meet the requirements of large-scale and complex applications. Currently, cloud control systems are mainly built by using a centralized architecture. The centralized system is overly dependent on the central control plane and has huge challenges in large-scale heterogeneous node systems. In this paper, we propose a decentralized approach to establish cloud control systems by proposing a distributed point-to-point task routing method. A considerable number of tasks in the system will not rely on the central plane and will be directly routed to the target devices through the point-to-point routing method, which improves the horizontal scalability of the cloud control system. The point-to-point routing method directly gives a unique address to every task, making inter-task communication more efficient in a complex heterogeneous and busy cloud control systems. Finally, we experimentally demonstrate that the distributed point-to-point task routing approach is compatible against the state-of-the-art central systems in large-scale task situations.

In the cloud era, the control objects are becoming larger and the information processing is more complex, and it is difficult for traditional control systems to process massive data in a timely manner. In view of the difficulty of data processing in the cloud era, it is extremely important to perform massive data operations through cloud servers. Unmanned aeriel vehicle (UAV) control is the representative of the intelligent field. Based on the ant colony algorithm and incorporating the potential field method, an improved potential field ant colony algorithm is designed. To deal with the path planning problem of UAVs, the potential field ant colony algorithm shortens the optimal path distance by 6.7%, increases the algorithm running time by 39.3%, and increases the maximum distance by 24.1% compared with the previous improvement. The cloud server is used to process the path problem of the UAV and feedback the calculation results in real time. Simulation experiments verify the effectiveness of the new algorithm in the cloud environment.

With the advantage of fast calculation and map resources on cloud control system (CCS), cloud-based predictive cruise control (CPCC) for heavy trucks has great potential to improve energy efficiency, which is significant to achieve the goal of national carbon neutrality. However, most investigations focus on the on-board predictive cruise control (PCC) system, lack of research on CPCC architecture under CCS. Besides, the current PCC algorithms have the problems of a single control target and high computational complexity, which hinders the improvement of the control effect. In this paper, a layered architecture based on CCS is proposed to effectively address the real-time computing of CPCC system and the deployment of its algorithm on vehicle-cloud. In addition, based on the dynamic programming principle and the proposed road point segmentation method (RPSM), a PCC algorithm is designed to optimize the speed and gear of heavy trucks with slope information. Simulation results show that the CPCC system can adaptively control vehicle driving through the slope prediction, with fuel-saving rate of 6.17% in comparison with the constant cruise control. Also, compared with other similar algorithms, the PCC algorithm can make the engine operate more in the efficient zone by cooperatively optimizing the gear and speed. Moreover, the RPSM algorithm can reconfigure the road in advance, with a 91% roadpoint reduction rate, significantly reducing algorithm complexity. Therefore, this study has essential research significance for the economic driving of heavy trucks and the promotion of the CPCC system.

In this paper, stochastic stabilization is investigated by max-plus algebra for a Markovian jump cloud control system with a reference signal. For the Markovian jump cloud control system, there exists framework adjustment whose evolution is satisfied with a Markov chain. Using max-plus algebra, a max-plus stochastic system is used to describe the Markovian jump cloud control system. A causal feedback matrix is obtained by exponential stability analysis for a causal feedback controller of the Markovian jump cloud control system. A sufficient condition is given to ensure existence on the causal feedback matrix of the causal feedback controller. Based on the causal feedback controller, stochastic stabilization in probability is analyzed for the Markovian jump cloud control system with a reference signal. Simulation results are given to show effectiveness of the causal feedback controller for the Markovian jump cloud control system.

This paper introduces the time-frequency analyzed long short-term memory (TF-LSTM) neural network method for jamming signal recognition over the Global Navigation Satellite System (GNSS) receiver. The method introduces the long short-term memory (LSTM) neural network into the recognition algorithm and combines the time-frequency (TF) analysis for signal preprocessing. Five kinds of navigation jamming signals including white Gaussian noise (WGN), pulse jamming, sweep jamming, audio jamming, and spread spectrum jamming are used as input for training and recognition. Since the signal parameters and quantity are unknown in the actual scenario, this work builds a data set containing multiple kinds and parameters jamming to train the TF-LSTM. The performance of this method is evaluated by simulations and experiments. The method has higher recognition accuracy and better robustness than the existing methods, such as LSTM and the convolutional neural network (CNN).

In this paper, an importance sampling maximum likelihood (ISML) estimator for direction-of-arrival (DOA) of incoherently distributed (ID) sources is proposed. Starting from the maximum likelihood estimation description of the uniform linear array (ULA), a decoupled concentrated likelihood function (CLF) is presented. A new objective function based on CLF which can obtain a closed-form solution of global maximum is constructed according to Pincus theorem. To obtain the optimal value of the objective function which is a complex high-dimensional integral, we propose an importance sampling approach based on Monte Carlo random calculation. Next, an importance function is derived, which can simplify the problem of generating random vector from a high-dimensional probability density function (PDF) to generate random variable from a one-dimensional PDF. Compared with the existing maximum likelihood (ML) algorithms for DOA estimation of ID sources, the proposed algorithm does not require initial estimates, and its performance is closer to Cramer-Rao lower bound (CRLB). The proposed algorithm performs better than the existing methods when the interval between sources to be estimated is small and in low signal to noise ratio (SNR) scenarios.

To improve the error correction performance, an innovative encoding structure with tail-biting for spinal codes is designed. Furthermore, an adaptive forward stack decoding (A-FSD) algorithm with lower complexity for spinal codes is proposed. In the A-FSD algorithm, a flexible threshold parameter is set by a variable channel state to narrow the scale of nodes accessed. On this basis, a new decoding method of AFSD with early termination (AFSD-ET) is further proposed. The AFSD-ET decoder not only has the ability of dynamically modifying the number of stored nodes, but also adopts the early termination criterion to curtail complexity. The complexity and related parameters are verified through a series of simulations. The simulation results show that the proposed spinal codes with tail-biting and the AFSD-ET decoding algorithms can reduce the complexity and improve the decoding rate without sacrificing correct decoding performance.

With the development of technology, the relevant performance of unmanned aerial vehicles (UAVs) has been greatly improved, and various highly maneuverable UAVs have been developed, which puts forward higher requirements on target tracking technology. Strong maneuvering refers to relatively instantaneous and dramatic changes in target acceleration or movement patterns, as well as continuous changes in speed, angle, and acceleration. However, the traditional UAV tracking algorithm model has poor adaptability and large amount of calculation. This paper applies support vector regression (SVR) to the interacting multiple model (IMM) algorithm. The simulation results show that the improved algorithm has higher tracking accuracy for highly maneuverable targets than the original algorithm, and can adjust parameters adaptively, making it more adaptable.

This paper presents a co-time co-frequency full-duplex (CCFD) massive multiple-input multiple-output (MIMO) system to meet high spectrum efficiency requirements for beyond the fifth-generation (5G) and the forthcoming the sixth-generation (6G) networks. To achieve equilibrium of energy consumption, system resource utilization, and overall transmission capacity, an energy-efficient resource management strategy concerning power allocation and antenna selection is designed. A continuous quantum-inspired termite colony optimization (CQTCO) algorithm is proposed as a solution to the resource management considering the communication reliability while promoting energy conservation for the CCFD massive MIMO system. The effectiveness of CQTCO compared with other algorithms is evaluated through simulations. The results reveal that the proposed resource management scheme under CQTCO can obtain a superior performance in different communication scenarios, which can be considered as an eco-friendly solution for promoting reliable and efficient communication in future wireless networks.

An approach for joint direction of arrival (DOA) angle and frequency estimation for a linear array is investigated in this paper. Specifically, we make the utmost of the autocorrelation and cross-correlation information to propose an extended DOA-matrix (EDOAM) method. Subsequently, we obtain the auto-paired angle and frequency estimates by the eigenvalues and the corresponding eigenvectors of the novel DOA matrix. Furthermore, the proposed method surpasses the DOA-matrix method which partly ignores the autocorrelation and cross-correlation information. Finally, the proposed method works well for both uniform and non-uniform linear arrays. The simulation consequences indicate the superiority of our proposed approach.

Constrained by complex imaging mechanism and extraordinary visual appearance, change detection with synthetic aperture radar (SAR) images has been a difficult research topic, especially in urban areas. Although existing studies have extended from bi-temporal data pair to multi-temporal datasets to derive more plentiful information, there are still two problems to be solved in practical applications. First, change indicators constructed from incoherent feature only cannot characterize the change objects accurately. Second, the results of pixel-level methods are usually presented in the form of the noisy binary map, making the spatial change not intuitive and the temporal change of a single pixel meaningless. In this study, we propose an unsupervised man-made objects change detection framework using both coherent and incoherent features derived from multi-temporal SAR images. The coefficients of variation in time-series incoherent features and the man-made object index (MOI) defined with coherent features are first combined to identify the initial change pixels. Afterwards, an improved spatiotemporal clustering algorithm is developed based on density-based spatial clustering of applications with noise (DBSCAN) and dynamic time warping (DTW), which can transform the initial results into noiseless object-level patches, and take the cluster center as a representative of the man-made object to determine the change pattern of each patch. An experiment with a stack of 10 TerraSAR-X images in Stripmap mode demonstrated that this method is effective in urban scenes and has the potential applicability to wide area change detection.

Ground-based interferometric synthetic aperture radar (GB-InSAR) can take deformation measurement with a high accuracy. Partition of the GB-InSAR deformation map benefits analyzing the deformation state of the monitoring scene better. Existing partition methods rely on labelled datasets or single deformation feature, and they cannot be effectively utilized in GB-InSAR applications. This paper proposes an improved partition method of the GB-InSAR deformation map based on dynamic time warping (DTW) and k-means. The DTW similarities between a reference point and all the measurement points are calculated based on their time-series deformations. Then the DTW similarity and cumulative deformation are taken as two partition features. With the k-means algorithm and the score based on multi evaluation indexes, a deformation map can be partitioned into an appropriate number of classes. Experimental datasets of West Copper Mine are processed to validate the effectiveness of the proposed method, whose measurement points are divided into seven classes with a score of 0.315 1.

With the continuous improvement of radar intelligence, it is difficult for traditional countermeasures to achieve ideal results. In order to deal with complex, changeable, and unknown threat signals in the complex electromagnetic environment, a waveform intelligent optimization model based on intelligent optimization algorithm is proposed. By virtue of the universality and fast running speed of the intelligent optimization algorithm, the model can optimize the parameters used to synthesize the countermeasure waveform according to different external signals, so as to improve the countermeasure performance. Genetic algorithm (GA) and particle swarm optimization (PSO) are used to simulate the intelligent optimization of interrupted-sampling and phase-modulation repeater waveform. The experimental results under different radar signal conditions show that the scheme is feasible. The performance comparison between the algorithms and some problems in the experimental results also provide a certain reference for the follow-up work.

With the rapid development of digital and intelligent information systems, display of radar situation interface has become an important challenge in the field of human-computer interaction. We propose a method for the optimization of radar situation interface from error-cognition through the mapping of information characteristics. A mapping method of matrix description is adopted to analyze the association properties between error-cognition sets and design information sets. Based on the mapping relationship between the domain of error-cognition and the domain of design information, a cross-correlational analysis is carried out between error-cognition and design information. We obtain the relationship matrix between the error-cognition of correlation between design information and the degree of importance among design information. Taking the task interface of a warfare navigation display as an example, error factors and the features of design information are extracted. Based on the results, we also propose an optimization design scheme for the radar situation interface.

In recent years, ground-based micro-deformation monitoring radar has attracted much attention due to its excellent monitoring capability. By controlling the repeated campaigns of the radar antenna on a fixed track, ground-based micro-deformation monitoring radar can accomplish repeat-pass interferometry without a space baseline and thus obtain high-precision deformation data of a large scene at one time. However, it is difficult to guarantee absolute stable installation position in every campaign. If the installation position is unstable, the stability of the radar track will be affected randomly, resulting in time-varying baseline error. In this study, a correction method for this error is developed by analyzing the error distribution law while the spatial baseline is unknown. In practice, the error data are first identified by frequency components, then the data of each one-dimensional array (in azimuth direction or range direction) are grouped based on numerical distribution period, and finally the error is corrected by the nonlinear model established with each group. This method is verified with measured data from a slope in southern China, and the results show that the method can effectively correct the time-varying baseline error caused by rail instability and effectively improve the monitoring data accuracy of ground-based micro-deformation radar in short term and long term.

In recent years, high-altitude aerostats have been increasingly developed in the direction of multi-functionality and large size. Due to the large size and the high flexibility, new challenges for large aerostats have appeared in the configuration test and the deformation analysis. The methods of the configuration test and the deformation analysis for large airship have been researched and discussed. A tested method of the configuration, named internal scanning, is established to quickly obtain the spatial information of all surfaces for the large airship by the three-dimensional (3D) laser scanning technology. By using the surface wrap method, the configuration parameters of the large airship are calculated. According to the test data of the configuration, the structural dimensions such as the distances between the characteristic sections are measured. The method of the deformation analysis for the airship contains the algorithm of non-uniform rational B-splines (NURBS) and the finite element (FE) method. The algorithm of NURBS is used to obtain the reconfiguration model of the large airship. The seams are considered and the seam areas are divided. The FE model of the middle part of the large airship is established. The distributions of the stress and the strain for the large airship are obtained by the FE method. The position of the larger deformation for the airship is found.

The multilayer satellite network has high spatial spectrum utilization, flexible networking, strong survivability, and diversified functions. The inter-satellite links (ISLs) and cross-layer ISLs (CLISLs) enable direct communication paths between satellites, which improves the spatial autonomy of the constellation. Due to the existence of perturbation, ISLs are affected for a long time, which impacts reliable inter-satellite transmission. The stability and complexity of ISL establishment are related to the static and dynamic characteristics of range and azimuth. This paper presents a model of ISLs in a perturbed multilayer constellation. Series of theoretical derivation, simulation, and numerical calculation are carried out. A more comprehensive multilayer constellation ISL model is obtained. The work of this paper provides some theoretical foundations for constellation networking research.

In order to improve our military’s level of intelligent accusation decision-making in future intelligent joint warfare, this paper studies operation loop recommendation methods for kill web based on the fundamental combat form of the future, i.e., “web-based kill,” and the operation loop theory. Firstly, we pioneer the operation loop recommendation problem with operation ring quality as the objective and closed-loop time as the constraint, and construct the corresponding planning model. Secondly, considering the case where there are multiple decision objectives for the combat ring recommendation problem, we propose for the first time a multi-objective optimization algorithm, the multi-objective ant colony evolutionary algorithm based on decomposition (MOACEA/D), which integrates the multi-objective evolutionary algorithm based on decomposition (MOEA/D) with the ant colony algorithm. The MOACEA/D can converge the optimal solutions of multiple single objectives non-dominated solution set for the multi-objective problem. Finally, compared with other classical multi-objective optimization algorithms, the MOACEA/D is superior to other algorithms superior in terms of the hyper volume (HV), which verifies the effectiveness of the method and greatly improves the quality and efficiency of commanders’ decision-making.

The threat sequencing of multiple unmanned combat air vehicles (UCAVs) is a multi-attribute decision-making (MADM) problem. In the threat sequencing process of multiple UCAVs, due to the strong confrontation and high dynamics of the air combat environment, the weight coefficients of the threat indicators are usually time-varying. Moreover, the air combat data is difficult to be obtained accurately. In this study, a threat sequencing method of multiple UCAVs is proposed based on game theory by considering the incomplete information. Firstly, a zero-sum game model of decision maker ( $\mathcal{D}$ ) and nature ( $\mathcal{N}$ ) with fuzzy payoffs is established to obtain the uncertain parameters which are the weight coefficient parameters of the threat indicators and the interval parameters of the threat matrix. Then, the established zero-sum game with fuzzy payoffs is transformed into a zero-sum game with crisp payoffs (matrix game) to solve. Moreover, a decision rule is addressed for the threat sequencing problem of multiple UCAVs based on the obtained uncertain parameters. Finally, numerical simulation results are presented to show the effectiveness of the proposed approach.

This paper addresses the open vehicle routing problem with time window (OVRPTW), where each vehicle does not need to return to the depot after completing the delivery task. The optimization objective is to minimize the total distance. This problem exists widely in real-life logistics distribution process. We propose a hybrid column generation algorithm (HCGA) for the OVRPTW, embedding both exact algorithm and metaheuristic. In HCGA, a label setting algorithm and an intelligent algorithm are designed to select columns from small and large subproblems, respectively. Moreover, a branch strategy is devised to generate the final feasible solution for the OVRPTW. The computational results show that the proposed algorithm has faster speed and can obtain the approximate optimal solution of the problem with 100 customers in a reasonable time.

Anti-ship missile coordinated attack mission planning is a complex multi-objective optimization problem with multiple combinations of platforms, strong decision-making constraints, and tightly coupled links. To avoid the coupling disorder between path planning and firepower distribution and improve the efficiency of coordinated attack mission planning, a firepower distribution model under the conditions of path planning is established from the perspective of decoupling optimization and the algorithm is implemented. First, we establish reference coordinate system of firepower distribution to clarify the reference direction of firepower distribution and divide the area of firepower distribution; then, we construct an index table of membership of firepower distribution to obtain alternative firepower distribution plans; finally, the fitness function of firepower distribution is established based on damage income, missile loss, ratio of efficiency and cost of firepower distribution, and the mean square deviation of the number of missiles used, and the alternatives are sorted to obtain the optimal firepower distribution plan. According to two simulation experiments, the method in this paper can effectively solve the many-to-many firepower distribution problem of coupled path planning. Under the premise of ensuring that no path crossing occurs, the optimal global solution can be obtained, and the operability and timeliness are good.