To analyze and optimize the weapon system of systems (WSOS) scheduling process, a new method based on robust capabilities for WSOS scheduling optimization is proposed. First, we present an activity network to represent the military mission. The member systems need to be reasonably assigned to perform different activities in the mission. Then we express the problem as a set partitioning formulation with novel columns (activity flows). A heuristic branch-and-price algorithm is designed based on the model of the WSOS scheduling problem (WSOSSP). The algorithm uses the shortest resource-constrained path planning to generate robust activity flows that meet the capability requirements. Finally, we discuss this method in several test cases. The results show that the solution can reduce the makespan of the mission remarkably.

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.

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.

Aiming at the suppression of enemy air defense (SEAD) task under the complex and complicated combat scenario, the spatiotemporal cooperative path planning methods are studied in this paper. The major research contents include optimal path points generation, path smoothing and cooperative rendezvous. In the path points generation part, the path points availability testing algorithm and the path segments availability testing algorithm are designed, on this foundation, the swarm intelligence-based path point generation algorithm is utilized to generate the optimal path. In the path smoothing part, taking terminal attack angle constraint and maneuverability constraint into consideration, the Dubins curve is introduced to smooth the path segments. In cooperative rendezvous part, we take estimated time of arrival requirement constraint and flight speed range constraint into consideration, the speed control strategy and flight path control strategy are introduced, further, the decoupling scheme of the circling maneuver and detouring maneuver is designed, in this case, the maneuver ways, maneuver point, maneuver times, maneuver path and flight speed are determined. Finally, the simulation experiments are conducted and the acquired results reveal that the time-space cooperation of multiple unmanned aeriel vehicles (UAVs) is effectively realized, in this way, the combat situation suppression against the enemy can be realized in SEAD scenarios.

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).

Navigation via signals of opportunity (NAVSOP) is able to realize positioning by making use of hundreds of different signals that are all around us. A method to realize NAVSOP for low earth orbit (LEO) satellites is proposed in this paper, in which the global navigation satellite system (GNSS) authorized signals are utilized as the signal of opportunity (SOP). At first, the carrier recovery technique is studied under the premise that the pseudo-code is unknown. Secondly, a method based on characteristics of Doppler frequency shift is proposed to recognize the navigation satellites. Thirdly, the extended Kalman filter (EKF) is utilized to estimate the orbital parameters by using carrier phase measurements. Finally, the proposed method is evaluated by using signals generated by a satellite navigation data simulator. The simulation results show that the proposed method can successfully realize navigation via GNSS authorized signals.

This paper proposes a time-varying sliding mode control method to address nonlinear missile body kinematics based on the suboptimal control theory. The analytical solution of suboptimal time-varying sliding surface and the corresponding suboptimal control law are obtained by solving the state-dependent Riccati equation analytically. Then, the Lyapunov method is used to analyze the motion trend in sliding surface and the asymptotic stability of the closed-loop system is validated. The suboptimal control law is transformed to the form of pseudo-angle-of-attack feedback. The simulation results indicate that the satisfactory performance can be obtained and the control law can overcome the influence of parameter errors.

Radar detection of small targets in sea clutter is a particularly demanding task because of the nonstationary characteristic of sea clutter. The track-before-detect (TBD) filter is an effective way to increase the signal-to-clutter ratio (SCR), thus improving the detection performance of small targets in sea clutter. To cope with the nonstationary characteristic of sea clutter, an easily-implemented Bayesian TBD filter with adaptive detection threshold is proposed and a new parameter estimation method is devised which is integrated into the detection process. The detection threshold is set according to the parameter estimation result under the framework of information theory. For detection of closely spaced targets, those within the same range cell as the one under test are treated as contribution to sea clutter, and a successive elimination method is adopted to detect them. Simulation results prove the effectiveness of the proposed algorithm in detecting small targets in nonstationary sea clutter, especially closely spaced ones.

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 source location based on the hybrid time difference of arrival (TDOA)/frequency difference of arrival (FDOA) is a basic problem in wireless sensor networks, and the layout of sensors in the hybrid TDOA/FDOA positioning will greatly affect the accuracy of positioning. Using unmanned aerial vehicle (UAV) as base stations, by optimizing the trajectory of the UAV swarm, an optimal positioning configuration is formed to improve the accuracy of the target position and velocity estimation. In this paper, a hybrid TDOA/FDOA positioning model is first established, and the positioning accuracy of the hybrid TDOA/FDOA under different positioning configurations and different measurement errors is simulated by the geometric dilution of precision (GDOP) factor. Second, the Cramer-Rao lower bound (CRLB) matrix of hybrid TDOA/FDOA location under different moving states of the target is derived theoretically, the objective function of the track optimization is obtained, and the track of the UAV swarm is optimized in real time. The simulation results show that the track optimization effectively improves the accuracy of the target position and velocity estimation.

The contribution rate of equipment system-of-systems architecture (ESoSA) is an important index to evaluate the equipment update, development, and architecture optimization. Since the traditional ESoSA contribution rate evaluation method does not make full use of the fuzzy information and uncertain information in the equipment system-of-systems (ESoS), and the Bayesian network is an effective tool to solve the uncertain information, a new ESoSA contribution rate evaluation method based on the fuzzy Bayesian network (FBN) is proposed. Firstly, based on the operation loop theory, an ESoSA is constructed considering three aspects: reconnaissance equipment, decision equipment, and strike equipment. Next, the fuzzy set theory is introduced to construct the FBN of ESoSA to deal with fuzzy information and uncertain information. Furthermore, the fuzzy importance index of the root node of the FBN is used to calculate the contribution rate of the ESoSA, and the ESoSA contribution rate evaluation model based on the root node fuzzy importance is established. Finally, the feasibility and rationality of this method are validated via an empirical case study of aviation ESoSA. Compared with traditional methods, the evaluation method based on FBN takes various failure states of equipment into consideration, is free of acquiring accurate probability of traditional equipment failure, and models the uncertainty of the relationship between equipment. The proposed method not only supplements and improves the ESoSA contribution rate assessment method, but also broadens the application scope of the Bayesian network.

With the rapidly growing abuse of drones, monitoring and classification of birds and drones have become a crucial safety issue. With similar low radar cross sections (RCSs), velocities, and heights, drones are usually difficult to be distinguished from birds in radar measurements. In this paper, we propose to exploit different periodical motions of birds and drones from high-resolution Doppler spectrum sequences (DSSs) for classification. This paper presents an elaborate feature vector representing the periodic fluctuations of RCS and micro kinematics. Fed by the Doppler spectrum and feature sequence, the long to short-time memory (LSTM) is used to solve the time series classification. Different classification schemes to exploit the Doppler spectrum series are validated and compared by extensive real-data experiments, which confirms the effectiveness and superiorities of the proposed algorithm.

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.

This paper proposes reliability and maintenance models for systems suffering random shocks arriving according to a non-homogeneous Poisson process. The system degradation process include two stages: from the installation of a new system to an initial point of a defect (normal stage), and then from that point to failure (defective stage), following the delay time concept. By employing the virtual age method, the impact of external shocks on the system degradation process is characterized by random virtual age increment in the two stages, resulting in the corresponding two-stage virtual age process. When operating in the defective state, the system becomes more susceptible to fatigue and suffers from a greater aging rate. Replacement is carried out either on failure or on the detection of a defective state at periodic or opportunistic inspections. This paper evaluates system reliability performance and investigates the optimal opportunistic maintenance policy. A case study on a cooling system is given to verify the obtained results.

A range-spread target (RST) detector is proposed for wideband radar. The detector, referred to as a conjugate multiplication and block thresholding (CMBT) detector, is simple for implementation in existing radar systems and has the advantage of minor calculation. First, the target energy of adjacent stretched echoes is coherently accumulated via conjugate multiplication and Fourier transform operations. It is noted that conjugate multiplication of two complex Gaussian distributed noise is complex double Gaussian distributed, leading to a signal to noise ratio (SNR) loss. Subsequently, considering the sparsity and clustering characteristics of the conjugate multiplication amplitude spectrum (CMAS), the block thresholding method is adopted for denoising, where the noise and cross-terms are adaptively smoothed, and the signal terms can be basically preserved. Finally, numerical simulation results for both synthetic and real radar data validate the effectiveness of the proposed detector, comparing with the conventional integration detector (ID), the spatial scattering density (SSD) detector, and waveform entropy (WE) and waveform contrast (WC) based detectors.

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.

The cooperative interception trajectories generation of multiple interceptors to hypersonic targets is studied. First, to solve the problem of on-line trajectory generation of the single interceptor, a generation method based on neighborhood optimal control is adopted. Then, when intercepting the strong maneuvering targets, the single interceptor is insufficient in maneuverability, therefore, an on-line multiple trajectories generation algorithm is proposed, which uses the multiple interceptors intercept area (IIA) to cover the target’s predicted intercept area (PIA) cooperatively. Through optimizing the interceptors’ zero control terminal location, the trajectories are generated on-line by using the neighborhood optimal control method, these trajectories could make the IIA maximally cover the PIA. The simulation results show that the proposed method can greatly improve the interception probability, which provides a reference for the collaborative interception of multiple interceptors.

In this paper, we study autonomous landing scene recognition with knowledge transfer for drones. Considering the difficulties in aerial remote sensing, especially that some scenes are extremely similar, or the same scene has different representations in different altitudes, we employ a deep convolutional neural network (CNN) based on knowledge transfer and fine-tuning to solve the problem. Then, LandingScenes-7 dataset is established and divided into seven classes. Moreover, there is still a novelty detection problem in the classifier, and we address this by excluding other landing scenes using the approach of thresholding in the prediction stage. We employ the transfer learning method based on ResNeXt-50 backbone with the adaptive momentum (ADAM) optimization algorithm. We also compare ResNet-50 backbone and the momentum stochastic gradient descent (SGD) optimizer. Experiment results show that ResNeXt-50 based on the ADAM optimization algorithm has better performance. With a pre-trained model and fine-tuning, it can achieve 97.8450% top-1 accuracy on the LandingScenes-7 dataset, paving the way for drones to autonomously learn landing scenes.

Higher-order statistics based approaches and signal sparseness based approaches have emerged in recent decades to resolve the underdetermined direction-of-arrival (DOA) estimation problem. These model-based methods face great challenges in practical applications due to high computational complexity and dependence on ideal assumptions. This paper presents an effective DOA estimation approach based on a deep residual network (DRN) for the underdetermined case. We first extract an input feature from a new matrix calculated by stacking several covariance matrices corresponding to different time delays. We then provide the input feature to the trained DRN to construct the super resolution spectrum. The DRN learns the mapping relationship between the input feature and the spatial spectrum by training. The proposed approach is superior to existing model-based estimation methods in terms of calculation efficiency, independence of source sparseness and adaptive capacity to non-ideal conditions (e.g., low signal to noise ratio, short bit sequence). Simulations demonstrate the validity and strong performance of the proposed algorithm on both overdetermined and underdetermined cases.

The emergent task is a kind of uncertain event that satellite systems often encounter in the application process. In this paper, the multi-satellite distributed coordinating and scheduling problem considering emergent tasks is studied. Due to the limitation of onboard computational resources and time, common online onboard rescheduling methods for such problems usually adopt simple greedy methods, sacrificing the solution quality to deliver timely solutions. To better solve the problem, a new multi-satellite onboard scheduling and coordinating framework based on multi-solution integration is proposed. This method uses high computational power on the ground and generates multiple solutions, changing the complex onboard rescheduling problem to a solution selection problem. With this method, it is possible that little time is used to generate a solution that is as good as the solutions on the ground. We further propose several multi-satellite coordination methods based on the multi-agent Markov decision process (MMDP) and mixed-integer programming (MIP). These methods enable the satellite to make independent decisions and produce high-quality solutions. Compared with the traditional centralized scheduling method, the proposed distributed method reduces the cost of satellite communication and increases the response speed for emergent tasks. Extensive experiments show that the proposed multi-solution integration framework and the distributed coordinating strategies are efficient and effective for onboard scheduling considering emergent tasks.

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.

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 model reference adaptive control (MRAC) augmentation method of a linear controller is proposed for air-breathing hypersonic vehicle (AHV) during inlet unstart. With the development of hypersonic flight technology, hypersonic vehicles have been gradually moving to the stage of weaponization. During the maneuvers, changes of attitude, Mach number and the back pressure can cause the inlet unstart phenomenon of scramjet. Inlet unstart causes significant changes in the aerodynamics of AHV, which may lead to deterioration of the tracking performance or instability of the control system. Therefore, we firstly establish the model of hypersonic vehicle considering inlet unstart, in which the changes of aerodynamics caused by inlet unstart is described as nonlinear uncertainty. Then, an MRAC augmentation method of a linear controller is proposed and the radial basis function (RBF) neural network is used to schedule the adaptive parameters of MRAC. Furthermore, the Lyapunov function is constructed to prove the stability of the proposed method. Finally, numerical simulations show that compared with the linear control method, the proposed method can stabilize the attitude of the hypersonic vehicle more quickly after the inlet unstart, which provides favorable conditions for inlet restart, thus verifying the effectiveness of the augmentation method proposed in the paper.

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.

This paper develops a deep estimator framework of deep convolution networks (DCNs) for super-resolution direction of arrival (DOA) estimation. In addition to the scenario of correlated signals, the quantization errors of the DCN are the major challenge. In our deep estimator framework, one DCN is used for spectrum estimation with quantization errors, and the remaining two DCNs are used to estimate quantization errors. We propose training our estimator using the spatial sampled covariance matrix directly as our deep estimator’s input without any feature extraction operation. Then, we reconstruct the original spatial spectrum from the spectrum estimate and quantization errors estimate. Also, the feasibility of the proposed deep estimator is analyzed in detail in this paper. Once the deep estimator is appropriately trained, it can recover the correlated signals’ spatial spectrum fast and accurately. Simulation results show that our estimator performs well in both resolution and estimation error compared with the state-of-the-art algorithms.

Least squares projection twin support vector machine (LSPTSVM) has faster computing speed than classical least squares support vector machine (LSSVM). However, LSPTSVM is sensitive to outliers and its solution lacks sparsity. Therefore, it is difficult for LSPTSVM to process large-scale datasets with outliers. In this paper, we propose a robust LSPTSVM model (called R-LSPTSVM) by applying truncated least squares loss function. The robustness of R-LSPTSVM is proved from a weighted perspective. Furthermore, we obtain the sparse solution of R-LSPTSVM by using the pivoting Cholesky factorization method in primal space. Finally, the sparse R-LSPTSVM algorithm (SR-LSPTSVM) is proposed. Experimental results show that SR-LSPTSVM is insensitive to outliers and can deal with large-scale datasets fastly.

Considering the problem that the scattering echo images of airborne Doppler weather radar are often reduced by ground clutters, the accuracy and confidence of meteorology target detection are reduced. In this paper, a deep convolutional neural network (DCNN) is proposed for meteorology target detection and ground clutter suppression with a large collection of airborne weather radar images as network input. For each weather radar image, the corresponding digital elevation model (DEM) image is extracted on basis of the radar antenna scanning parameters and plane position, and is further fed to the network as a supplement for ground clutter suppression. The features of actual meteorology targets are learned in each bottleneck module of the proposed network and convolved into deeper iterations in the forward propagation process. Then the network parameters are updated by the back propagation iteration of the training error. Experimental results on the real measured images show that our proposed DCNN outperforms the counterparts in terms of six evaluation factors. Meanwhile, the network outputs are in good agreement with the expected meteorology detection results (labels). It is demonstrated that the proposed network would have a promising meteorology observation application with minimal effort on network variables or parameter changes.

The rapid increase of the scale and the complexity of the controlled plants bring new challenges such as computing power and storage for conventional control systems. Cloud computing is concerned as a powerful solution to handle complex large-scale control missions by using sufficient computing resources. However, the computing ability enables more complex devices and more data to be involved and most of the data have not been fully utilized. Meanwhile, it is even impossible to obtain an accurate model of each device in the complex control systems for the model-based control algorithms. Therefore, motivated by the above reasons, we propose a data-driven predictive cloud control system. To achieve the proposed system, a practical data-driven predictive cloud control testbed is established and together a cloud-edge communication scheme is developed. Finally, the simulations and experiments demonstrate the effectiveness of the proposed system.

In the applications of joint control and robot movement, the joint torque estimation has been treated as an effective technique and widely used. Researches are made to analyze the kinematic and compliance model of the robot joint with harmonic drive to acquire high precision torque output. Through analyzing the structures of the harmonic drive and experiment apparatus, a scheme of the proposed joint torque estimation method based on both the dynamic characteristics and unscented Kalman filter (UKF) is designed and built. Based on research and scheme, torque estimation methods in view of only harmonic drive compliance model and compliance model with the Kalman filter are simulated as guidance and reference to promote the research on the torque estimation technique. Finally, a promoted torque estimation method depending on both harmonic drive compliance model and UKF is designed, and simulation results compared with the measurements of a commercial torque sensor, have verified the effectiveness of the proposed method.

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.

A miniaturized periodic element for constructing bandpass frequency selective surface (FSS) independent of incident angles and polarizations is presented. An interdigital resonator (IR) with one extending finger to connect the two separate parts of the interdigital capacitor is explored to achieve parallel resonance. The equivalent circuit model (ECM) and electric field distributions are introduced to explain frequency performance of FSS. The whole structure has only one layer and possesses a low profile (a thickness of 0.001 5 $\lambda $ , where $\lambda $ represents the resonant wavelength in free space) as well as a small size (0.03 $\lambda $ ×0.03 $\lambda $ ). This FSS performs as a spatial bandpass filter which exhibits a great angular stability with incident angles ranging from 0° to 80° for both transverse electric (TE) and transverse magnetic (TM) polarizations. As an example, a prototype of one proposed FSS is fabricated and tested. The measured results show a good angular stability.

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.

The perception module of advanced driver assistance systems plays a vital role. Perception schemes often use a single sensor for data processing and environmental perception or adopt the information processing results of various sensors for the fusion of the detection layer. This paper proposes a multi-scale and multi-sensor data fusion strategy in the front end of perception and accomplishes a multi-sensor function disparity map generation scheme. A binocular stereo vision sensor composed of two cameras and a light deterction and ranging (LiDAR) sensor is used to jointly perceive the environment, and a multi-scale fusion scheme is employed to improve the accuracy of the disparity map. This solution not only has the advantages of dense perception of binocular stereo vision sensors but also considers the perception accuracy of LiDAR sensors. Experiments demonstrate that the multi-scale multi-sensor scheme proposed in this paper significantly improves disparity map estimation.

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.

A fast self-adapting high-order sliding mode (FSHOSM) controller is designed for a class of nonlinear systems with unknown uncertainties. As for uncertainty-free nonlinear system, a new switching condition is introduced into the standard geometric homogeneity. Different from the existing geometric homogeneity method, both state variables and their derivatives are considered to bring a reasonable effective switching condition. As a result, a faster convergence rate of state variables is achieved. Furthermore, based on the integral sliding mode (ISM) and above geometric homogeneity, a self-adapting high-order sliding mode (HOSM) control law is proposed for a class of nonlinear systems with uncertainties. The resulting controller allows the closed-loop system to conduct with the expected properties of strong robustness and fast convergence. Stable analysis of the nonlinear system is also proved based on the Lyapunov approach. The effectiveness of the resulting controller is verified by several simulation results.

Because of the low convergence efficiency of the typical Vicsek model, a Vicsek with static summoning points (VSSP) algorithm based on the Vicsek model considering static summoning points is proposed. Firstly, the mathematical model of the individual movement total cost on each summoning point is established. Then the individual classification rule is designed according to the initial state of the cluster to obtain the subclusters guided by each summoning point. Finally, the summoning factor is introduced to modify the course angle updating formula of the Vicsek model. To verify the effectiveness of the proposed algorithm and study the effect of the cluster summoning factor on the convergence rate, three groups of simulation experiments under different summoning factors are designed in this paper. To verify the superiority of the VSSP algorithm, the performance of the VSSP algorithm is compared with the classic algorithm by designing the algorithm performance comparison verification experiment. The results show that the algorithm proposed in this paper has good convergence and course angle consistency. The summoning factor is the sensitive factor of cluster convergence. This algorithm can provide a reference for efficient cluster segmentation movement.

Reconnaissance mission planning of multiple unmanned aerial vehicles (UAVs) under an adversarial environment is a discrete combinatorial optimization problem which is proved to be a non-deterministic polynomial (NP)-complete problem. The purpose of this study is to research intelligent multi-UAVs reconnaissance mission planning and online re-planning algorithm under various constraints in mission areas. For numerous targets scattered in the wide area, a reconnaissance mission planning and re-planning system is established, which includes five modules, including intelligence analysis, sub-mission area division, mission sequence planning, path smoothing, and online re-planning. The intelligence analysis module depicts the attribute of targets and the heterogeneous characteristic of UAVs and computes the number of sub-mission areas on consideration of voyage distance constraints. In the sub-mission area division module, an improved K-means clustering algorithm is designed to divide the reconnaissance mission area into several sub-mission areas, and each sub-mission is detected by the UAV loaded with various detective sensors. To control reconnaissance cost, the sampling and iteration algorithms are proposed in the mission sequence planning module, which are utilized to solve the optimal or approximately optimal reconnaissance sequence. In the path smoothing module, the Dubins curve is applied to smooth the flight path, which assure the availability of the planned path. Furthermore, an online re-planning algorithm is designed for the uncertain factor that the UAV is damaged. Finally, reconnaissance planning and re-planning experiment results show that the algorithm proposed in this paper are effective and the algorithms designed for sequence planning have a great advantage in solving efficiency and optimality.

The development of image classification is one of the most important research topics in remote sensing. The prediction accuracy depends not only on the appropriate choice of the machine learning method but also on the quality of the training datasets. However, real-world data is not perfect and often suffers from noise. This paper gives an overview of noise filtering methods. Firstly, the types of noise and the consequences of class noise on machine learning are presented. Secondly, class noise handling methods at both the data level and the algorithm level are introduced. Then ensemble-based class noise handling methods including class noise removal, correction, and noise robust ensemble learners are presented. Finally, a summary of existing data-cleaning techniques is given.

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.

For ship targets with complex motion, it is difficult for the traditional monostatic inverse synthetic aperture radar (ISAR) imaging to improve the cross-range resolution by increasing of accumulation time. In this paper, a distributed ISAR imaging algorithm is proposed to improve the cross-range resolution for the ship target. Multiple stations are used to observe the target in a short time, thereby the effect of incoherence caused by the complex motion of the ship can be reduced. The signal model of ship target with three-dimensional (3-D) rotation is constructed firstly. Then detailed analysis about the improvement of cross-range resolution is presented. Afterward, we propose the methods of parameters estimation to solve the problem of the overlap or gap, which will cause a loss of resolution and is necessary for subsequent processing. Besides, the compressed sensing (CS) method is applied to reconstruct the echoes with gaps. Finally, numerical simulations are presented to verify the effectiveness and the robustness of the proposed algorithm.