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.

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.

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.

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.

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.

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.

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.

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.

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.

Remaining useful life (RUL) prediction is one of the most crucial elements in prognostics and health management (PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression (RCR) model with fusing failure time data. Firstly, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures, the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function (PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction.

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.

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.

Non-uniform linear array (NULA) configurations are well renowned due to their structural ability for providing increased degrees of freedom (DOF) and wider array aperture than uniform linear arrays (ULAs). These characteristics play a significant role in improving the direction-of-arrival (DOA) estimation accuracy. However, most of the existing NULA geometries are primarily applicable to circular sources (CSs), while they limitedly improve the DOF and continuous virtual aperture for non-circular sources (NCSs). Toward this purpose, we present a triad-displaced ULAs (Tdis-ULAs) configuration for NCS. The Tdis-ULAs structure generally consists of three ULAs, which are appropriately placed. The proposed antenna array approach fully exploits the non-circular characteristics of the sources. Given the same number of elements, the Tdis-ULAs design achieves more DOF and larger hole-free co-array aperture than its sparse array competitors. Advantageously, the number of uniform DOF, optimal distribution of elements among the ULAs, and precise element positions are uniquely determined by the closed-form expressions. Moreover, the proposed array also produces a filled resulting co-array. Numerical simulations are conducted to show the performance advantages of the proposed Tdis-ULAs configuration over its counterpart designs.

In this paper, an efficient unequal error protection (UEP) scheme for online fountain codes is proposed. In the build-up phase, the traversing-selection strategy is proposed to select the most important symbols (MIS). Then, in the completion phase, the weighted-selection strategy is applied to provide low overhead. The performance of the proposed scheme is analyzed and compared with the existing UEP online fountain scheme. Simulation results show that in terms of MIS and the least important symbols (LIS), when the bit error ratio is $ {10^{ - 4}} $, the proposed scheme can achieve $ 85{\text{% }} $ and $ 31.58{\text{% }} $ overhead reduction, respectively.

How to mine valuable information from massive multi-source heterogeneous data and identify the intention of aerial targets is a major research focus at present. Aiming at the long-term dependence of air target intention recognition, this paper deeply explores the potential attribute features from the spatiotemporal sequence data of the target. First, we build an intelligent dynamic intention recognition framework, including a series of specific processes such as data source, data preprocessing, target space-time, convolutional neural networks-bidirectional gated recurrent unit-atteneion (CBA) model and intention recognition. Then, we analyze and reason the designed CBA model in detail. Finally, through comparison and analysis with other recognition model experiments, our proposed method can effectively improve the accuracy of air target intention recognition, and is of significance to the commanders’ operational command and situation prediction.

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.

The anti-aircraft system plays an irreplaceable role in modern combat. An anti-aircraft system consists of various types of functional entities interacting to destroy the hostile aircraft moving in high speed. The connecting structure of combat entities in it is of great importance for supporting the normal process of the system. In this paper, we explore the optimizing strategy of the structure of the anti-aircraft network by establishing extra communication channels between the combat entities. Firstly, the thought of combat network model (CNM) is borrowed to model the anti-aircraft system as a heterogeneous network. Secondly, the optimization objectives are determined as the survivability and the accuracy of the system. To specify these objectives, the information chain and accuracy chain are constructed based on CNM. The causal strength (CAST) logic and influence network (IN) are introduced to illustrate the establishment of the accuracy chain. Thirdly, the optimization constraints are discussed and set in three aspects: time, connection feasibility and budget. The time constraint network (TCN) is introduced to construct the timing chain and help to detect the timing consistency. Then, the process of the multi-objective optimization of the structure of the anti-aircraft system is designed. Finally, a simulation is conducted to prove the effectiveness and feasibility of the proposed method. Non-dominated sorting based genetic algorithm-II (NSGA2) is used to solve the multi-objective optimization problem and two other algorithms including non-dominated sorting based genetic algorithm-III (NSGA3) and strength Pareto evolutionary algorithm-II (SPEA2) are employed as comparisons. The deciders and system builders can make the anti-aircraft system improved in the survivability and accuracy in the combat reality.

In this paper, a detection method combining Cameron decomposition based on polarization scattering characteristics in sea clutter background is proposed. Firstly, the Cameron decomposition is exploited to fuse the radar echoes of full polarization channels at the data level. Due to the artificial material structure on the surface of the target, it can be shown that the non-reciprocity of the target cell is stronger than that of the clutter cell. Then, based on the analysis of the decomposition results, a new feature with scattering geometry characteristics in polarization domain, denoted as Cameron polarization decomposition scattering weight (CPD-SW), is extracted as the test statistic, which can achieve more detailed descriptions of the clutter scattering characteristics utilizing the difference between their scattering types. Finally, the superiority of the proposed CPD-SW detector over traditional detectors in improving detection performance is verified by the IPIX measured dataset, which has strong stability under short-time observation in threshold detection and can also improve the separability of feature space zin anomaly detection.

Neuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the Von Neumann architecture. Inspired by the real characteristics of physical memristive devices, we propose a threshold-type nonlinear voltage-controlled memristor mathematical model which is used to design a novel memristor-based crossbar array. The presented crossbar array can simulate the synaptic weight in real number field rather than only positive number field. Theoretical analysis and simulation results of a 2×2 image inversion operation validate the feasibility of the proposed crossbar array and the necessary training and inference functions. Finally, the presented crossbar array is used to construct the neural network and then applied in the handwritten digit recognition. The Mixed National Institute of Standards and Technology (MNIST) database is adopted to train this neural network and it achieves a satisfactory accuracy.

The unmanned aerial vehicle (UAV) swarm technology is one of the research hotspots in recent years. With the continuous improvement of autonomous intelligence of UAV, the swarm technology of UAV will become one of the main trends of UAV development in the future. This paper studies the behavior decision-making process of UAV swarm rendezvous task based on the double deep Q network (DDQN) algorithm. We design a guided reward function to effectively solve the problem of algorithm convergence caused by the sparse return problem in deep reinforcement learning (DRL) for the long period task. We also propose the concept of temporary storage area, optimizing the memory playback unit of the traditional DDQN algorithm, improving the convergence speed of the algorithm, and speeding up the training process of the algorithm. Different from traditional task environment, this paper establishes a continuous state-space task environment model to improve the authentication process of UAV task environment. Based on the DDQN algorithm, the collaborative tasks of UAV swarm in different task scenarios are trained. The experimental results validate that the DDQN algorithm is efficient in terms of training UAV swarm to complete the given collaborative tasks while meeting the requirements of UAV swarm for centralization and autonomy, and improving the intelligence of UAV swarm collaborative task execution. The simulation results show that after training, the proposed UAV swarm can carry out the rendezvous task well, and the success rate of the mission reaches 90%.

The fault-tolerant consensus problem for leader-following nonlinear multi-agent systems with actuator faults is mainly investigated. A new super-twisting sliding mode observer is constructed to estimate the velocity and undetectable fault information simultaneously. The time-varying gain is introduced to solve the initial error problem and peak value problem, which makes the observation more accurate and faster. Then, based on the estimated results, an improved sliding mode fault-tolerant consensus control algorithm is designed to compensate the actuator faults. The protocol can guarantee the finite-time consensus control of multi-agent systems and suppress chattering. Finally, the effectiveness and the superiority of the observer and control algorithm are proved by some simulation examples of the multi-aircraft system.

GM(1,1) models have been widely used in various fields due to their high performance in time series prediction.However, some hypotheses of the existing GM(1,1) model family may reduce their prediction performance in some cases. To solve this problem, this paper proposes a self-adaptive GM(1,1) model, termed as SAGM(1,1) model, which aims to solve the defects of the existing GM (1,1) model family by deleting their modeling hypothesis. Moreover, a novel multi-parameter simultaneous optimization scheme based on firefly algorithm is proposed, the proposed multi-parameter optimization scheme adopts machine learning ideas, takes all adjustable parameters of SAGM(1,1) model as input variables, and trains it with firefly algorithm. And Sobol’ sensitivity indices are applied to study global sensitivity of SAGM(1,1) model parameters, which provides an important reference for model parameter calibration. Finally, forecasting capability of SAGM(1,1) model is illustrated by Anhui electricity consumption dataset. Results show that prediction accuracy of SAGM(1,1) model is significantly better than other models, and it is shown that the proposed approach enhances the prediction performance of GM(1,1) model significantly.

Aiming to evaluate the reliability of phase-transition degrading systems, a generalized stochastic degradation model with phase transition is constructed, and the corresponding analytical reliability function is formulated under the concept of the first hitting time. The phase-varying stochastic property and the phase-varying nonlinearity are considered simultaneously in the proposed model. To capture the phase-varying stochastic property, a Wiener process is adopted to model the non-monotonous degradation phase, while a Gamma process is utilized to model the monotonous one. In addition, the phase-varying nonlinearity is captured by different transformed time scale functions. To facilitate the practical application of the proposed model, identification of phase model type and estimation of model parameters are discussed, and the initial guesses for parameters optimization are also given. Based on the constructed model, two simulation studies are carried out to verify the analytical reliability function and analyze the influence of model misspecification. Finally, a practical case study is conducted for illustration.

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

To strengthen border patrol measures, unmanned aerial vehicles (UAVs) are gradually used in many countries to detect illegal entries on borders. However, how to efficiently deploy limited UAVs to patrol on borders of large areas remains challenging. In this paper, we first model the problem of deploying UAVs for border patrol as a Stackelberg game. Two players are considered in this game: The border patrol agency is the leader, who optimizes the patrol path of UAVs to detect the illegal immigrant. The illegal immigrant is the follower, who selects a certain area of the border to pass through at a certain time after observing the leader’s strategy. Second, a compact linear programming problem is proposed to tackle the exponential growth of the number of leader’s strategies. Third, a method is proposed to reduce the size of the strategy space of the follower. Then, we provide some theoretic results to present the effect of parameters of the model on leader’s utilities. Experimental results demonstrate the positive effect of limited starting and ending areas of UAV’s patrolling conditions and multiple patrolling altitudes on the leader ’s utility, and show that the proposed solution outperforms two conventional patrol strategies and has strong robustness.

This paper proposes an optimal deployment method of heterogeneous multistatic radars to construct arc barrier coverage with location restrictions. This method analyzes and proves the properties of different deployment patterns in the optimal deployment sequence. Based on these properties and considering location restrictions, it introduces an optimization model of arc barrier coverage and aims to minimize the total deployment cost of heterogeneous multistatic radars. To overcome the non-convexity of the model and the non-analytical nature of the objective function, an algorithm combining integer line programming and the cuckoo search algorithm (CSA) is proposed. The proposed algorithm can determine the number of receivers and transmitters in each optimal deployment squence to minimize the total placement cost. Simulations are conducted in different conditions to verify the effectiveness of the proposed method.

In engineering application, there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval (PRI). Therefore, if the training samples used to calculate the weight vector does not contain the jamming, then the jamming cannot be removed by adaptive spatial filtering. If the weight vector is constantly updated in the range dimension, the training data may contain target echo signals, resulting in signal cancellation effect. To cope with the situation that the training samples are contaminated by target signal, an iterative training sample selection method based on non-homogeneous detector (NHD) is proposed in this paper for updating the weight vector in entire range dimension. The principle is presented, and the validity is proven by simulation results.

This paper focuses on the adaptive detection of range and Doppler dual-spread targets in non-homogeneous and non-Gaussian sea clutter. The sea clutter from two polarimetric channels is modeled as a compound-Gaussian model with different parameters, and the target is modeled as a subspace range-spread target model. The persymmetric structure is used to model the clutter covariance matrix, in order to reduce the reliance on secondary data of the designed detectors. Three adaptive polarimetric persymmetric detectors are designed based on the generalized likelihood ratio test (GLRT), Rao test, and Wald test. All the proposed detectors have constant false-alarm rate property with respect to the clutter texture, the speckle covariance matrix. Experimental results on simulated and measured data show that three adaptive detectors outperform the competitors in different clutter environments, and the proposed GLRT detector has the best detection performance under different parameters.

Micro-Doppler feature extraction of unmanned aerial vehicles (UAVs) is important for their identification and classification. Noise and the motion state of the UAV are the main factors that may affect feature extraction and estimation precision of the micro-motion parameters. The spectrum of UAV echoes is reconstructed to strengthen the micro-motion feature and reduce the influence of the noise on the condition of low signal to noise ratio (SNR). Then considering the rotor rate variance of UAV in the complex motion state, the cepstrum method is improved to extract the rotation rate of the UAV, and the blade length can be intensively estimated. The experiment results for the simulation data and measured data show that the reconstruction of the spectrum for the UAV echoes is helpful and the relative mean square root error of the rotating speed and blade length estimated by the proposed method can be improved. However, the computation complexity is higher and the heavier computation burden is required.

Component reallocation (CR) is receiving increasing attention in many engineering systems with functionally interchangeable and unbalanced degradation components. This paper studies a CR and system replacement maintenance policy of series repairable systems, which undergoes minimal repairs for each emergency failure of components, and considers constant downtime and cost of minimal repair, CR and system replacement. Two binary mixed integer nonlinear programming models are respectively established to determine the assignment of CR, and the uptime right before CR and system replacement with the objective of minimizing the system average maintenance cost and maximizing the system availability. Further, we derive the optimal uptime right before system replacement with maximization of the system availability, and then give the relationship between the system availability and the component failure rate. Finally, numerical examples show that the CR and system replacement maintenance policy can effectively reduce the system average maintenance cost and improve the system availability, and further give the sensitivity analysis and insights of the CR and system replacement maintenance policy.

The existing recognition algorithms of space-time block code (STBC) for multi-antenna (MA) orthogonal frequency-division multiplexing (OFDM) systems use feature extraction and hypothesis testing to identify the signal types in a complex communication environment. However, owing to the restrictions on the prior information and channel conditions, these existing algorithms cannot perform well under strong interference and non-cooperative communication conditions. To overcome these defects, this study introduces deep learning into the STBC-OFDM signal recognition field and proposes a recognition method based on the fourth-order lag moment spectrum (FOLMS) and attention-guided multi-scale dilated convolution network (AMDCNet). The fourth-order lag moment vectors of the received signals are calculated, and vectors are stitched to form two-dimensional FOLMS, which is used as the input of the deep learning-based model. Then, the multi-scale dilated convolution is used to extract the details of images at different scales, and a convolutional block attention module (CBAM) is introduced to construct the attention-guided multi-scale dilated convolution module (AMDCM) to make the network be more focused on the target area and obtian the multi-scale guided features. Finally, the concatenate fusion, residual block and fully-connected layers are applied to acquire the STBC-OFDM signal types. Simulation experiments show that the average recognition probability of the proposed method at ?12 dB is higher than 98%. Compared with the existing algorithms, the recognition performance of the proposed method is significantly improved and has good adaptability to environments with strong disturbances. In addition, the proposed deep learning-based model can directly identify the pre-processed FOLMS samples without a priori information on channel and noise, which is more suitable for non-cooperative communication systems than the existing algorithms.

This paper studies the fixed-time output-feedback control for a class of linear systems subject to matched uncertainties. To estimate the uncertainties and system states, we design a composite observer which consists of a high-order sliding mode observer and a Luenberger observer. Then, a robust output-feedback controller with fixed-time convergence guarantee is constructed. Rigorous theoretical proof shows that with the proposed controller, the system states can converge to zero in fixed-time free of the initial conditions. Finally, simulation comparison with existing algorithms is given. Simulation results verify the effectiveness of the proposed controller in terms of its fixed-time convergence and perfect disturbance rejection.

Since the dynamical system and control system of the missile are typically nonlinear, an effective acceleration tracking autopilot is designed using the dynamic surface control (DSC) technique in order to make the missile control system more robust despite the uncertainty of the dynamical parameters and the presence of disturbances. Firstly, the nonlinear mathematical model of the tail-controlled missile is decomposed into slow acceleration dynamics and fast pitch rate dynamics based on the naturally existing time scale separation. Secondly, the controller based on DSC is designed after obtaining the linear dynamics characteristics of the slow and fast subsystems. An extended state observer is used to detect the uncertainty of the system state variables and aerodynamic parameters to achieve the compensation of the control law. The closed-loop stability of the controller is derived and rigorously analyzed. Finally, the effectiveness and robustness of the design is verified by Monte Carlo simulation considering different initial conditions and parameter uptake. Simulation results illustrate that the missile autopilot based DSC controller achieves better performance and robustness than the other two well-known autopilots. The method proposed in this paper is applied to the design of a missile autopilot, and the results show that the acceleration tracking autopilot based on the DSC controller can ensure accurate tracking of the required commands and has better performance.

During extended warranty (EW) period, maintenance events play a key role in controlling the product systems within normal operations. However, the modelling of failure process and maintenance optimization is complicated owing to the complex features of the product system, namely, components of the multi-component system are interdependent with each other in some form. For the purpose of optimizing the EW pricing decision of the multi-component system scientifically and rationally, taking the series multi-component system with economic dependence sold with EW policy as a research object, this paper optimizes the imperfect preventive maintenance (PM) strategy from the standpoint of EW cost. Taking into consideration adjusting the PM moments of the components in the system, a group maintenance model is developed, in which the system is repaired preventively in accordance with a specified PM base interval. In order to compare with the system EW cost before group maintenance, the system EW cost model before group maintenance is developed. Numerical example demonstrates that offering group maintenance programs can reduce EW cost of the system to a great extent, thereby reducing the EW price, which proves to be a win-win strategy to manufacturers and users.

Adaptive detection of range-spread targets is considered in the presence of subspace interference plus Gaussian clutter with unknown covariance matrix. The target signal and interference are supposed to lie in two linearly independent subspaces with deterministic but unknown coordinates. Relying on the two-step criteria, two adaptive detectors based on Gradient tests are proposed, in homogeneous and partially homogeneous clutter plus subspace interference, respectively. Both of the proposed detectors exhibit theoretically constant false alarm rate property against unknown clutter covariance matrix as well as the power level. Numerical results show that, the proposed detectors have better performance than their existing counterparts, especially for mismatches in the signal steering vectors.

Mainlobe jamming (MLJ) brings a big challenge for radar target detection, tracking, and identification. The suppression of MLJ is a hard task and an open problem in the electronic counter-counter measures (ECCM) field. Target parameters and target direction estimation is difficult in radar MLJ. A target parameter estimation method via atom-reconstruction in radar MLJ is proposed in this paper. The proposed method can suppress the MLJ and simultaneously provide high estimation accuracy of target range and angle. Precisely, the eigen-projection matrix processing (EMP) algorithm is adopted to suppress the MLJ, and the target range is estimated effectively through the beamforming and pulse compression. Then the target angle can be effectively estimated by the atom-reconstruction method. Without any prior knowledge, the MLJ can be canceled, and the angle estimation accuracy is well preserved. Furthermore, the proposed method does not have strict requirement for radar array construction, and it can be applied for linear array and planar array. Moreover, the proposed method can effectively estimate the target azimuth and elevation simultaneously when the target azimuth (or elevation) equals to the jamming azimuth (or elevation), because the MLJ is suppressed in spatial plane dimension.

Aviation accidents are currently one of the leading causes of significant injuries and deaths worldwide. This entices researchers to investigate aircraft safety using data analysis approaches based on an advanced machine learning algorithm. To assess aviation safety and identify the causes of incidents, a classification model with light gradient boosting machine (LGBM) based on the aviation safety reporting system (ASRS) has been developed. It is improved by k-fold cross-validation with hybrid sampling model (HSCV), which may boost classification performance and maintain data balance. The results show that employing the LGBM-HSCV model can significantly improve accuracy while alleviating data imbalance. Vertical comparison with other cross-validation (CV) methods and lateral comparison with different fold times comprise the comparative approach. Aside from the comparison, two further CV approaches based on the improved method in this study are discussed: one with a different sampling and folding order, and the other with more CV. According to the assessment indices with different methods, the LGBM-HSCV model proposed here is effective at detecting incident causes. The improved model for imbalanced data categorization proposed may serve as a point of reference for similar data processing, and the model’s accurate identification of civil aviation incident causes can assist to improve civil aviation safety.

In the scene of wideband radar, due to the spread of target scattering points, the attitude and angle of view of the target constantly change in the process of moving. It is difficult to predict, and the actual echo of multiple scattered points is not fully matched with the transmitted signal. Therefore, it is challenging for the traditional matching filter method to achieve a complete matching effect in wideband echo detection. In addition, the energy dispersion of complex target echoes is still a problem in radar target detection under broadband conditions. Therefore, this paper proposes a wideband target detection method based on dual-channel correlation processing of range-extended targets. This method fully uses the spatial distribution characteristics of target scattering points of echo signal and the matching characteristics of the dual-channel point extension function itself. The radial accumulation of wideband target echo signal in the complex domain is realized through the adaptive correlation processing of a dual-channel echo signal. The accumulation effect of high matching degree is achieved to improve the detection probability and the performance of wideband detection. Finally, electromagnetic simulation experiments and measured data verify that the proposed method has the advantages of high signal to noise ratio (SNR) gain and high detection probability under low SNR conditions.