Affected by the natural environmental and human activity factors, significant seasonal differences appear on the regional scattering characteristic and ground deformation of saline soil. Interferometric decorrelation due to season replacement limits the conventional multi-temporal interferometric synthetic aperture radar (MT-InSAR) technique and its application in such areas. To extend the monitoring capability in the salt desert area, we select a vast basin of saline soil around Howz-e-Soltan Salt Lake of Iran as the study area and present an improved MT-InSAR for experimental research. Based on 131 C-band Sentinel-1A images collected between October 2014 to July 2020,1896 refined interferograms in total are selected from all interferogram candidates. Interferometric coherence analysis shows that the coherence in the saline soil area has an apparent seasonal variation, and the soil moisture affected by the precipitation may be the main factor that leads to the seasonal variation. Subsequently, the deformation characteristics of saline soil under different environmental conditions and human activity factors are compared and analyzed in detail. Related deformation mechanisms of different saline soil types are initially revealed by combining interferometric coherence, meteorological data, and engineering geological characteristics of saline soil. Related results would provide reference for the large-scale infrastructure construction engineering in similar saline soil areas.

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.

Channel estimation has been considered as a key issue in the millimeter-wave (mmWave) massive multi-input multi-output (MIMO) communication systems, which becomes more challenging with a large number of antennas. In this paper, we propose a deep learning (DL)-based fast channel estimation method for mmWave massive MIMO systems. The proposed method can directly and effectively estimate channel state information (CSI) from received data without performing pilot signals estimate in advance, which simplifies the estimation process. Specifically, we develop a convolutional neural network (CNN)-based channel estimation network for the case of dimensional mismatch of input and output data, subsequently denoted as channel (H) neural network (HNN). It can quickly estimate the channel information by learning the inherent characteristics of the received data and the relationship between the received data and the channel, while the dimension of the received data is much smaller than the channel matrix. Simulation results show that the proposed HNN can gain better channel estimation accuracy compared with existing schemes.

Cooperative search-attack is an important application of unmanned aerial vehicle (UAV) swarm in military field. The coupling between path planning and task allocation, the heterogeneity of UAVs, and the dynamic nature of task environment greatly increase the complexity and difficulty of the UAV swarm cooperative search-attack mission planning problem. Inspired by the collaborative hunting behavior of wolf pack, a distributed self-organizing method for UAV swarm search-attack mission planning is proposed. First, to solve the multi-target search problem in unknown environments, a wolf scouting behavior-inspired cooperative search algorithm for UAV swarm is designed. Second, a distributed self-organizing task allocation algorithm for UAV swarm cooperative attacking of targets is proposed by analyzing the flexible labor division behavior of wolves. By abstracting the UAV as a simple artificial wolf agent, the flexible motion planning and group task coordinating for UAV swarm can be realized by self-organizing. The effectiveness of the proposed method is verified by a set of simulation experiments, the stability and scalability are evaluated, and the integrated solution for the coupled path planning and task allocation problems for the UAV swarm cooperative search-attack task can be well performed.

The planetary reducer is a common type of transmission mechanism, which can provide high transmission accuracy and has been widely used, and it is usually required with high reliability of transmission characteristics in practice. During the manufacturing and usage stages of planetary reducers, uncertainties are ubiquitous and wear is inevitable, which affect the transmission characteristics and the reliability of planetary reducers. In this paper, belief reliability modeling and analysis considering multi-uncertainties and wear are proposed for planetary reducers. Firstly, based on the functional principle and the influence of wear, the performance margin degradation model is established using the hysteresis error as the key performance parameter, where the degradation is mainly caused by the accumulated wear. After that, multi-source uncertainties are analyzed and quantified separately, including manufacturing errors, uncertainties in operational and environmental conditions, and uncertainties in performance thresholds. Finally, the belief reliability model is established based on the performance margin degradation model. A case study of a planetary reducer is applied and the reliability sensitivity analysis is implemented to show the practicability of the proposed method. The results show that the proposed method can provide some suggestions to the design and manufacturing phases of the planetary reducer.

Synthetic aperture radar (SAR) is able to detect surface changes in urban areas with a short revisit time, showing its capability in disaster assessment and urbanization monitoring. Most presented change detection methods are conducted using couples of SAR amplitude images. However, a prior date of surface change is required to select a feasible image pair. We propose an automatic spatio-temporal change detection method by identifying the temporary coherent scatterers. Based on amplitude time series, ${\chi ^2} $ -test and iterative single pixel change detection are proposed to identify all step-times: the moments of the surface change. Then the parameters, e.g., deformation velocity and relative height, are estimated and corresponding coherent periods are identified by using interferometric phase time series. With identified temporary coherent scatterers, different types of temporal surface changes can be classified using the location of the coherent periods and spatial significant changes are identified combining point density and F values. The main advantage of our method is automatically detecting spatio-temporal surface changes without prior information. Experimental results by the proposed method show that both appearing and disappearing buildings with their step-times are successfully identified and results by ascending and descending SAR images show a good agreement.

In order to improve the autonomous ability of unmanned aerial vehicles (UAV) to implement air combat mission, many artificial intelligence-based autonomous air combat maneuver decision-making studies have been carried out, but these studies are often aimed at individual decision-making in 1v1 scenarios which rarely happen in actual air combat. Based on the research of the 1v1 autonomous air combat maneuver decision, this paper builds a multi-UAV cooperative air combat maneuver decision model based on multi-agent reinforcement learning. Firstly, a bidirectional recurrent neural network (BRNN) is used to achieve communication between UAV individuals, and the multi-UAV cooperative air combat maneuver decision model under the actor-critic architecture is established. Secondly, through combining with target allocation and air combat situation assessment, the tactical goal of the formation is merged with the reinforcement learning goal of every UAV, and a cooperative tactical maneuver policy is generated. The simulation results prove that the multi-UAV cooperative air combat maneuver decision model established in this paper can obtain the cooperative maneuver policy through reinforcement learning, the cooperative maneuver policy can guide UAVs to obtain the overall situational advantage and defeat the opponents under tactical cooperation.

The evolution of airborne tactical networks (ATNs) is impeded by the network ossification problem. As a solution, network virtualization (NV) can provide a flexible and scalable architecture where virtual network embedding (VNE) is a key part. However, existing VNE algorithms cannot be optimally adopted in the virtualization of ATN due to the complex interference in air-combat field. In this context, a highly reliable VNE algorithm based on the transmission rate for ATN virtualization (TR-ATVNE) is proposed to adapt well to the specific electromagnetic environment of ATN. Our algorithm coordinates node and link mapping. In the node mapping, transmission-rate resource is firstly defined to effectively evaluate the ranking value of substrate nodes under the interference of both environmental noises and enemy attacks. Meanwhile, a feasible splitting rule is proposed for path splitting in the link mapping, considering the interference between wireless links. Simulation results reveal that our algorithm is able to improve the acceptance ratio of virtual network requests while maintaining a high revenue-to-cost ratio under the complex electromagnetic interference.

Sliding mode control (SMC) becomes a common tool in designing robust nonlinear control systems, due to its inherent characteristics such as insensitivity to system uncertainties and fast dynamic response. Two modes are involved in the SMC operation, namely reaching mode and sliding mode. In the reaching mode, the system state is forced to reach the sliding surface in a finite time. The major drawback of the SMC approach is the occurrence of chattering in the sliding mode, which is undesirable in most applications. Generally, the trade-off between chattering reduction and fast reaching time must be considered in the conventional SMC design. This paper proposes SMC design with a novel reaching law called the exponential rate reaching law (ERRL) to reduce chattering, and the control structure of the converter is designed based on the multi-input SMC that is applied to a three-phase AC/DC power converter. The simulation and experimental results show the effectiveness of the proposed technique.

In the field of satellite imagery, remote sensing image captioning (RSIC) is a hot topic with the challenge of overfitting and difficulty of image and text alignment. To address these issues, this paper proposes a vision-language aligning paradigm for RSIC to jointly represent vision and language. First, a new RSIC dataset DIOR-Captions is built for augmenting object detection in optical remote (DIOR) sensing images dataset with manually annotated Chinese and English contents. Second, a Vision-Language aligning model with Cross-modal Attention (VLCA) is presented to generate accurate and abundant bilingual descriptions for remote sensing images. Third, a cross-modal learning network is introduced to address the problem of visual-lingual alignment. Notably, VLCA is also applied to end-to-end Chinese captions generation by using the pre-training language model of Chinese. The experiments are carried out with various baselines to validate VLCA on the proposed dataset. The results demonstrate that the proposed algorithm is more descriptive and informative than existing algorithms in producing captions.

How to make use of limited onboard resources for complex and heavy space tasks has attracted much attention. With the continuous improvement on satellite payload capacity and the increasing complexity of observation requirements, the importance of satellite autonomous task scheduling research has gradually increased. This article first gives the problem description and mathematical model for the satellite autonomous task scheduling and then follows the steps of “satellite autonomous task scheduling, centralized autonomous collaborative task scheduling architecture, distributed autonomous collaborative task scheduling architecture, solution algorithm". Finally, facing the complex and changeable environment situation, this article proposes the future direction of satellite autonomous task scheduling.

Current successes in artificial intelligence domain have revitalized interest in neural networks and demonstrated their potential in solving spacecraft trajectory optimization problems. This paper presents a data-free deep neural network (DNN) based trajectory optimization method for intercepting non-cooperative maneuvering spacecraft, in a continuous low-thrust scenario. Firstly, the problem is formulated as a standard constrained optimization problem through differential game theory and minimax principle. Secondly, a new DNN is designed to integrate interception dynamic model into the network and involve it in the process of gradient descent, which makes the network endowed with the knowledge of physical constraints and reduces the learning burden of the network. Thus, a DNN based method is proposed, which completely eliminates the demand of training datasets and improves the generalization capacity. Finally, numerical results demonstrate the feasibility and efficiency of our proposed method.

Nonuniform linear arrays, such as coprime array and nested array, have received great attentions because of the increased degrees of freedom (DOFs) and weakened mutual coupling. In this paper, inspired by the existing coprime array, we propose a high-order extended coprime array (HoECA) for improved direction of arrival (DOA) estimation. We first derive the closed-form expressions for the range of consecutive lags. Then, by changing the inter-element spacing of a uniform linear array (ULA), three cases are proposed and discussed. It is indicated that the HoECA can obtain the largest number of consecutive lags when the spacing takes the maximum value. Finally, by comparing it with the other sparse arrays, the optimized HoECA enjoys a larger number of consecutive lags with mitigating mutual coupling. Simulation results are shown to evaluate the superiority of HoECA over the others in terms of DOF, mutual coupling leakage and estimation accuracy.

Unauthorized operations referred to as “black flights” of unmanned aerial vehicles (UAVs) pose a significant danger to public safety, and existing low-attitude object detection algorithms encounter difficulties in balancing detection precision and speed. Additionally, their accuracy is insufficient, particularly for small objects in complex environments. To solve these problems, we propose a lightweight feature-enhanced convolutional neural network able to perform detection with high precision detection for low-attitude flying objects in real time to provide guidance information to suppress black-flying UAVs. The proposed network consists of three modules. A lightweight and stable feature extraction module is used to reduce the computational load and stably extract more low-level feature, an enhanced feature processing module significantly improves the feature extraction ability of the model, and an accurate detection module integrates low-level and advanced features to improve the multiscale detection accuracy in complex environments, particularly for small objects. The proposed method achieves a detection speed of 147 frames per second (FPS) and a mean average precision (mAP) of 90.97% for a dataset composed of flying objects, indicating its potential for low-altitude object detection. Furthermore, evaluation results based on microsoft common objects in context (MS COCO) indicate that the proposed method is also applicable to object detection in general.

This paper investigates the guidance method based on reinforcement learning (RL) for the coplanar orbital interception in a continuous low-thrust scenario. The problem is formulated into a Markov decision process (MDP) model, then a well-designed RL algorithm, experience based deep deterministic policy gradient (EBDDPG), is proposed to solve it. By taking the advantage of prior information generated through the optimal control model, the proposed algorithm not only resolves the convergence problem of the common RL algorithm, but also successfully trains an efficient deep neural network (DNN) controller for the chaser spacecraft to generate the control sequence. Numerical simulation results show that the proposed algorithm is feasible and the trained DNN controller significantly improves the efficiency over traditional optimization methods by roughly two orders of magnitude.

Fiber optical gyroscope (FOG) is a highly reliable navigation element, and the degradation trajectories of its two accuracy indexes are monotonic and non-monotonic respectively. In this paper, a flexible accelerated degradation testing (ADT) model is used for analyzing the bivariate dependent degradation process of FOG. The time-varying copulas are employed to consider the dynamic dependency structure between two marginal degradation processes as the Wiener process and the inverse Gaussian process. The statistical inference is implemented by utilizing an inference function for the margins (IFM) approach. It is demonstrated that the proposed method is powerful in modeling the joint distribution with various margins.

A multi-objective optimization based robust beamforming (BF) scheme is proposed to realize secure transmission in a cognitive satellite and unmanned aerial vehicle (UAV) network. Since the satellite network coexists with the UAV network, we first consider both achievable secrecy rate maximization and total transmit power minimization, and formulate a multi-objective optimization problem (MOOP) using the weighted Tchebycheff approach. Then, by supposing that only imperfect channel state information based on the angular information is available, we propose a method combining angular discretization with Taylor approximation to transform the non-convex objective function and constraints to the convex ones. Next, we adopt semi-definite programming together with randomization technology to solve the original MOOP and obtain the BF weight vector. Finally, simulation results illustrate that the Pareto optimal trade-off can be achieved, and the superiority of our proposed scheme is confirmed by comparing with the existing BF schemes.

Orbital angular momentum (OAM) at radio frequency (RF) has attracted more and more attention as a novel approach of multiplexing a set of orthogonal OAM modes on the same frequency channel to achieve high spectral efficiency (SE). However, the precondition for maintaining the orthogonality among different OAM modes is perfect alignment of the transmit and receive uniform circular arrays (UCAs), which is difficult to be satisfied in practical wireless communication scenarios. Therefore, to achieve available multi-mode OAM broadband wireless communication, we first investigate the effect of oblique angles on the transmission performance of the multi-mode OAM broadband system in the non-parallel misalignment case. Then, we compare the UCA-based RF analog and baseband digital transceiver structures and corresponding beam steering schemes. Mathematical analysis and numerical simulations validate that the SE of the misaligned multi-mode OAM broadband system is quite low, while analog and digital beam steering (DBS) both can significantly improve the SE of the system. However, DBS can obtain higher SE than analog beam steering especially when the bandwidth and the number of array elements are large, which validates that the baseband digital transceiver with DBS is more suitable for multi-mode OAM broadband wireless communication systems in practice.

The dual-axis rotational inertial navigation system (INS) with dithered ring laser gyro (DRLG) is widely used in high precision navigation. The major inertial sensor errors such as drift errors of gyro and accelerometer can be averaged out, but the G-sensitive drifts of laser gyro cannot be averaged out by indexing. A 16-position rotational simulation experiment proves the G-sensitive drift will affect the long-term navigation error for the rotational INS quantitatively. The vibration coupling and asymmetric structure of the DRLG are the main errors. A new dithered mechanism and optimized DRLG is designed. The validity and efficiency of the optimized design are conformed by 1 g sinusoidal vibration experiments. An optimized inertial measurement unit (IMU) is formulated and measured experimentally. Laboratory and vehicle experimental results show that the divergence speed of longitude errors can be effectively slowed down in the optimized IMU. In long term independent navigation, the position accuracy of dual-axis rotational INS is improved close to 50%, and the G-sensitive drifts of laser gyro in the optimized IMU are less than 0.000 2 °/h. These results have important theoretical significance and practical value for improving the structural dynamic characteristics of DRLG INS, especially the high-precision inertial system.

In recent years, with the continuous development of multi-agent technology represented by unmanned aerial vehicle (UAV) swarm, consensus control has become a hot spot in academic research. In this paper, we put forward a discrete-time consensus protocol and obtain the necessary and sufficient conditions for the second-order consensus of the second-order multi-agent system with a fixed structure under the condition of no saturation input. The theoretical derivation verifies that the two eigenvalues of the Laplacian of the communication network matrix and the sampling period have an important effect on achieving consensus. Then we construct and verify sufficient conditions to achieve consensus under the condition of input saturation constraints. The results show that consensus can be achieved if velocity, position gain, and sampling period satisfy a set of inequalities related to the eigenvalues of the Laplacian matrix. Finally, the accuracy and validity of the theoretical results are proved by numerical simulations.

In this paper, we focus on the failure analysis of unmanned autonomous swarm (UAS) considering cascading effects. A framework of failure analysis for UAS is proposed. Guided by the framework, the failure analysis of UAS with crash fault agents is performed. Resilience is used to analyze the processes of cascading failure and self-repair of UAS. Through simu-lation studies, we reveal the pivotal relationship between resilience, the swarm size, and the percentage of failed agents. The simulation results show that the swarm size does not affect the cascading failure process but has much influence on the process of self-repair and the final performance of the swarm. The results also reveal a tipping point exists in the swarm. Meanwhile, we get a counter-intuitive result that larger-scale UAS loses more resilience in the case of a small percentage of failed individuals, suggesting that the increasing swarm size does not necessarily lead to high resilience. It is also found that the temporal degree failure strategy performs much more harmfully to the resilience of swarm systems than the random failure. Our work can provide new insights into the mechanisms of swarm collapse, help build more robust UAS, and develop more efficient failure or protection strategies.

Effectiveness evaluation of the joint operation system is an important basis for the demonstration and development of weapon equipment. With the consideration that existing models of system effectiveness evaluation seldom describe the structural relationship among equipment clearly as well as reflect the dynamic, the analog-to-digital converter-graphical evaluation and review technique (ADC-GERT) network parameter estimation model is proposed based on the ADC model and the joint operation system structure. Firstly, analysis of the joint operation system structure and operation process is conducted to build the GERT network, where equipment subsystems are nodes and activities are directed arches. Then the mission effectiveness of equipment subsystems is calculated by the ADC model. The probability transfer parameters are modified by the mission effectiveness of equipment subsystems based on the Bayesian theorem, with the ADC-GERT network parameter estimation model constructed. Finally, a case study is used to validate the efficiency and dynamic of the ADC-GERT network parameter estimation model.

In the application of persistent scatterer interferometry (PSI), deformation information is extracted from persistent scatterer (PS) points. Thus, the density and position of PS points are critical for PSI. To increase the PS density, a time-series InSAR chain termed as “super-resolution persistent scatterer interferometry” (SR-PSI) is proposed. In this study, we investigate certain important properties of SR-PSI. First, we review the main workflow and dataflow of SR-PSI. It is shown that in the implementation of the Capon algorithm, the diagonal loading (DL) approach should be only used when the condition number of the covariance matrix is sufficiently high to reduce the discontinuities between the joint images. We then discuss the density and positioning accuracy of PS when compared with traditional PSI. The theory and experimental results indicate that SR-PSI can increase the PS density in urban areas. However, it is ineffective for the rural areas, which should be an important consideration for the engineering application of SR-PSI. Furthermore, we validate that the positioning accuracy of PS can be improved by SR-PSI via simulations.

This paper studies the optimal policy for joint control of admission, routing, service, and jockeying in a queueing system consisting of two exponential servers in parallel. Jobs arrive according to a Poisson process. Upon each arrival, an admission/routing decision is made, and the accepted job is routed to one of the two servers with each being associated with a queue. After each service completion, the servers have an option of serving a job from its own queue, serving a jockeying job from another queue, or staying idle. The system performance is inclusive of the revenues from accepted jobs, the costs of holding jobs in queues, the service costs and the job jockeying costs. To maximize the total expected discounted return, we formulate a Markov decision process (MDP) model for this system. The value iteration method is employed to characterize the optimal policy as a hedging point policy. Numerical studies verify the structure of the hedging point policy which is convenient for implementing control actions in practice.

This paper proposes an $ {\cal{L}}_1$ adaptive fault tolerant control method for trajectory tracking of tail-sitter aircraft in the state of motor loss fault. The tail-sitter model considers the uncertainties produced by the features of nonlinearities and couplings which cause difficulties in control. An $ {\cal{L}}_1$ adaptive controller is designed to reduce the position and attitude error when actuators have faults. A reference trajectory containing large maneuver flight transitions is designed, which makes it even harder for the $ {\cal{L}}_1$ controller to track accurately. Compensators are designed to assist $ {\cal{L}}_1$ adaptive controller tracking of the reference trajectory. The stability of the $ {\cal{L}}_1$ adaptive controller including compensators is proved. Finally, the simulation results are used to analyse the effectiveness of the proposed controller. Compared to the ${H_\infty }$ controller, the $ {\cal{L}}_1$ adaptive controller with compensators has better performance in position control and attitude control under fault tolerance state even when the aircraft conducts large maneuver. Besides, as the $ {\cal{L}}_1$ adaptive control method separates feedback control and adaptive law design, the response speed of the whole system is improved.

The existing theories for closed loop identification with the linear feedback controller are very mature. To apply the existed theories directly in the control field, we propose a new idea about replacing the original unknown and nonlinear feedback controller with one approximated linear controller, while guaranteeing the equivalent property for the obtained closed loop system. Based on some statistical correlation functions, one condition is derived to show the equivalent property between the approximated linear controller and the original nonlinear controller. The detailed explicit form, corresponding to the approximated linear controller, is also constructed. Furthermore, to give a complete analysis for closed loop identification, the cost function is rewritten as one extended expression, being convenient to understand. Then spectral estimation is introduced to identify the unknown plant in the closed loop system. Finally, the proposed theories are verified by one simulation example.

As the scale of current systems become larger and larger and their complexity is increasing gradually, research on executable models in the design phase becomes significantly important as it is helpful to simulate the execution process and capture defects of a system in advance. Meanwhile, the capability of a system becomes so important that stakeholders tend to emphasize their capability requirements when developing a system. To deal with the lack of official specifications and the fundamental theory basis for capability requirement, we propose a cooperative capability requirements (CCR) meta-model as a theory basis for researchers to refer to in this research domain, in which we provide detailed definition of the CCR concepts, associations and rules. Moreover, we also propose an executable framework, which may enable modelers to simulate the execution process of a system in advance and do well in filling the inconsistency and semantic gaps between stakeholders’ requirements and their models. The primary working mechanism of the framework is to transform the Alf activity meta-model into the communicating sequential process (CSP) process meta-model based on some mapping rules, after which the internal communication mechanism between process nodes is designed to smooth the execution of behaviors in a CSP system. Moreover, a validation method is utilized to check the correctness and consistency of the models, and a self-fixing mechanism is used to fix the errors and warnings captured during the validation process automatically. Finally, a validation report is generated and fed back to the modelers for system optimization.

In this paper, a trajectory shaping guidance law, which considers constraints of ?eld-of-view (FOV) angle, impact angle, and terminal lateral acceleration, is proposed for a constant speed missile against a stationary target. First, to decouple constraints of the FOV angle and the terminal lateral acceleration, the third-order polynomial with respect to the line-of-sight (LOS) angle is introduced. Based on an analysis of the relationship between the looking angle and the guidance coefficient, the boundary of the coefficient that satisfies the FOV constraint is obtained. The terminal guidance law coefficient is used to guarantee the convergence of the terminal conditions. Furthermore, the proposed law can be implemented under bearings-only information, as the guidance command does not involve the relative range and the LOS angle rate. Finally, numerical simulations are performed based on a kinematic vehicle model to verify the effectiveness of the guidance law. Overall, the work offers an easily implementable guidance law with closed-form guidance gains, which is suitable for engineering applications.

Test selection is to select the test set with the least total cost or the least total number from the alternative test set on the premise of meeting the required testability indicators. The existing models and methods are not suitable for system level test selection. The first problem is the lack of detailed data of the units’ fault set and the test set, which makes it impossible to establish a traditional dependency matrix for the system level. The second problem is that the system level fault detection rate and the fault isolation rate (referred to as "two rates") are not enough to describe the fault diagnostic ability of the system level tests. An innovative dependency matrix (called combinatorial dependency matrix) composed of three submatrices is presented. The first problem is solved by simplifying the submatrix between the units’ fault and the test, and the second problem is solved by establishing the system level fault detection rate, the fault isolation rate and the integrated fault detection rate (referred to as "three rates") based on the new matrix. The mathematical model of the system level test selection problem is constructed, and the binary genetic algorithm is applied to solve the problem, which achieves the goal of system level test selection.

This paper examines the direction of arrival (DOA) estimation for polarized signals impinging on a sparse vector sensor array which is based on the maximum interelement spacing constraint (MISC). The vector array effectively utilizes the polarization domain information of incident signals, and the quaternion model is adopted for signals polarization characteristic maintenance and computational burden reduction. The features of MISC arrays are crucial to the mutual coupling effects reduction and higher degrees of freedom (DOFs). The quaternion data model based on vector MISC arrays is established, which extends the scalar MISC array into the vector MISC array. Based on the model, a quaternion multiple signal classification (MUSIC) algorithm based on vector MISC arrays is proposed for DOA estimation. The algorithm combines the advantages of the quaternion model and the vector MISC array to enhance the DOA estimation performance. Analytical simulations are performed to certify the capability of the algorithm.

Most of the near-field source localization methods are developed with the approximated signal model, because the phases of the received near-field signal are highly non-linear. Nevertheless, the approximated signal model based methods suffer from model mismatch and performance degradation while the exact signal model based estimation methods usually involve parameter searching or multiple decomposition procedures. In this paper, a search-free near-field source localization method is proposed with the exact signal model. Firstly, the approximative estimates of the direction of arrival (DOA) and range are obtained by using the approximated signal model based method through parameter separation and polynomial rooting operations. Then, the approximative estimates are corrected with the exact signal model according to the exact expressions of phase difference in near-field observations. The proposed method avoids spectral searching and parameter pairing and has enhanced estimation performance. Numerical simulations are provided to demonstrate the effectiveness of the proposed method.

Equipment development planning (EDP) is usually a long-term process often performed in an environment with high uncertainty. The traditional multi-stage dynamic programming cannot cope with this kind of uncertainty with unpredictable situations. To deal with this problem, a multi-stage EDP model based on a deep reinforcement learning (DRL) algorithm is proposed to respond quickly to any environmental changes within a reasonable range. Firstly, the basic problem of multi-stage EDP is described, and a mathematical planning model is constructed. Then, for two kinds of uncertainties (future capability requirements and the amount of investment in each stage), a corresponding DRL framework is designed to define the environment, state, action, and reward function for multi-stage EDP. After that, the dueling deep Q-network (Dueling DQN) algorithm is used to solve the multi-stage EDP to generate an approximately optimal multi-stage equipment development scheme. Finally, a case of ten kinds of equipment in 100 possible environments, which are randomly generated, is used to test the feasibility and effectiveness of the proposed models. The results show that the algorithm can respond instantaneously in any state of the multi-stage EDP environment and unlike traditional algorithms, the algorithm does not need to re-optimize the problem for any change in the environment. In addition, the algorithm can flexibly adjust at subsequent planning stages in the event of a change to the equipment capability requirements to adapt to the new requirements.

It is essential to maximize capacity while satisfying the transmission time delay of unmanned aerial vehicle (UAV) swarm communication system. In order to address this challenge, a dynamic decentralized optimization mechanism is presented for the realization of joint spectrum and power (JSAP) resource allocation based on deep Q-learning networks (DQNs). Each UAV to UAV (U2U) link is regarded as an agent that is capable of identifying the optimal spectrum and power to communicate with one another. The convolutional neural network, target network, and experience replay are adopted while training. The findings of the simulation indicate that the proposed method has the potential to improve both communication capacity and probability of successful data transmission when compared with random centralized assignment and multichannel access methods.

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.

This paper presents a deep reinforcement learning (DRL)-based motion control method to provide unmanned aerial vehicles (UAVs) with additional flexibility while flying across dynamic unknown environments autonomously. This method is applicable in both military and civilian fields such as penetration and rescue. The autonomous motion control problem is addressed through motion planning, action interpretation, trajectory tracking, and vehicle movement within the DRL framework. Novel DRL algorithms are presented by combining two difference-amplifying approaches with traditional DRL methods and are used for solving the motion planning problem. An improved Lyapunov guidance vector field (LGVF) method is used to handle the trajectory-tracking problem and provide guidance control commands for the UAV. In contrast to conventional motion-control approaches, the proposed methods directly map the sensor-based detections and measurements into control signals for the inner loop of the UAV, i.e., an end-to-end control. The training experiment results show that the novel DRL algorithms provide more than a 20% performance improvement over the state-of-the-art DRL algorithms. The testing experiment results demonstrate that the controller based on the novel DRL and LGVF, which is only trained once in a static environment, enables the UAV to fly autonomously in various dynamic unknown environments. Thus, the proposed technique provides strong flexibility for the controller.

To improve applicability and adaptability of the impact time control guidance (ITCG) in practical engineering, a two-stage ITCG law with simple but effective structure is proposed based on the hybrid proportional navigation, namely, the pure-proportional-navigation and the retro-proportional-navigation. For the case with the impact time error less than zero, the first stage of the guided trajectory is driven by the retro-proportional-navigation and the second one is driven by the pure-proportional-navigation. When the impact time error is greater than zero, both of the stages are generated by the pure-proportional-navigation but using different navigation gains. It is demonstrated by two- and three-dimensional numerical simulations that the proposed guidance law at least has comparable results to existing proportional-navigation-based ITCG laws and is shown to be advantageous in certain circumstances in that the proposed guidance law alleviates its dependence on the time-to-go estimation, consumes less control energy, and adapts itself to more boundary conditions and constraints. The results of this research are expected to be supplementary to the current research literature.

How to improve the efficiency of exact learning of the Bayesian network structure is a challenging issue. In this paper, four different causal constraints algorithms are added into score calculations to prune possible parent sets, improving state-of-the-art learning algorithms’ efficiency. Experimental results indicate that exact learning algorithms can significantly improve the efficiency with only a slight loss of accuracy. Under causal constraints, these exact learning algorithms can prune about 70% possible parent sets and reduce about 60% running time while only losing no more than 2% accuracy on average. Additionally, with sufficient samples, exact learning algorithms with causal constraints can also obtain the optimal network. In general, adding max-min parents and children constraints has better results in terms of efficiency and accuracy among these four causal constraints algorithms.

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.

The complexity of business and information systems (IS) alignment is a growing concern for researchers and practitioners alike. The extant research on alignment architecture fails to consider the human viewpoint, which makes it difficult to embrace emergent complexity. This paper contributes to the extant literature in the following ways. First, we combine an enterprise architecture (EA) framework with a human viewpoint to address alignment issues in the architecture design phase; second, we describe a dynamic alignment model by developing a human-centered meta-model that explains first- and second-order changes and their effects on alignment evolution. This paper provides better support for the theoretical research and the practical application of dynamic alignment.

In this paper, the reinforcement learning method for cooperative multi-agent systems (MAS) with incremental number of agents is studied. The existing multi-agent reinforcement learning approaches deal with the MAS with a specific number of agents, and can learn well-performed policies. However, if there is an increasing number of agents, the previously learned in may not perform well in the current scenario. The new agents need to learn from scratch to find optimal policies with others, which may slow down the learning speed of the whole team. To solve that problem, in this paper, we propose a new algorithm to take full advantage of the historical knowledge which was learned before, and transfer it from the previous agents to the new agents. Since the previous agents have been trained well in the source environment, they are treated as teacher agents in the target environment. Correspondingly, the new agents are called student agents. To enable the student agents to learn from the teacher agents, we first modify the input nodes of the networks for teacher agents to adapt to the current environment. Then, the teacher agents take the observations of the student agents as input, and output the advised actions and values as supervising information. Finally, the student agents combine the reward from the environment and the supervising information from the teacher agents, and learn the optimal policies with modified loss functions. By taking full advantage of the knowledge of teacher agents, the search space for the student agents will be reduced significantly, which can accelerate the learning speed of the holistic system. The proposed algorithm is verified in some multi-agent simulation environments, and its efficiency has been demonstrated by the experiment results.