For localisation of unknown non-cooperative targets in space, the existence of interference points causes inaccuracy of pose estimation while utilizing point cloud registration. To address this issue, this paper proposes a new iterative closest point (ICP) algorithm combined with distributed weights to intensify the dependability and robustness of the non-cooperative target localisation. As interference points in space have not yet been extensively studied, we classify them into two broad categories, far interference points and near interference points. For the former, the statistical outlier elimination algorithm is employed. For the latter, the Gaussian distributed weights, simultaneously valuing with the variation of the Euclidean distance from each point to the centroid, are commingled to the traditional ICP algorithm. In each iteration, the weight matrix ${\boldsymbol{W}} $ in connection with the overall localisation is obtained, and the singular value decomposition is adopted to accomplish high-precision estimation of the target pose. Finally, the experiments are implemented by shooting the satellite model and setting the position of interference points. The outcomes suggest that the proposed algorithm can effectively suppress interference points and enhance the accuracy of non-cooperative target pose estimation. When the interference point number reaches about 700, the average error of angle is superior to 0.88°.

This review article aims to give a comprehensive review of periodic orbits in the circular restricted three-body problem (CRTBP), which is a standard ideal model for the Earth-Moon system and is closest to the practical mechanical model. It focuses the attention on periodic orbits in the Earth-Moon system. This work is primarily motivated by a series of missions and plans that take advantages of the three-body periodic orbits near the libration points or around two gravitational celestial bodies. Firstly, simple periodic orbits and their classi?cation that is usually considered to be early work before 1970 are summarized, and periodic orbits around Lagrange points, either planar or three-dimensional, are intensively studied during past decades. Subsequently, stability index of a periodic orbit and bifurcation analysis are presented, which demonstrate a guideline to ?nd more periodic orbits inspired by bifurcation signals. Then, the practical techniques for computing a wide range of periodic orbits and associated quasi-periodic orbits, as well as constructing database of periodic orbits by numerical searching techniques are also presented. For those unstable periodic orbits, the station keeping maneuvers are reviewed. Finally, the applications of periodic orbits are presented, including those in practical missions, under consideration, and still in conceptual design stage. This review article has the function of bridging between engineers and researchers, so as to make it more convenient and faster for engineers to understand the complex restricted three-body problem (RTBP). At the same time, it can also provide some technical thinking for general researchers.

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.

In this paper, a velocity filtering based track-before-detect algorithm in mixed coordinates is presented to address the problem of integration loss caused by inaccurate motion model in polar coordinate sensors. Since the motion of a constant velocity (CV) target is better modeled in Cartesian coordinates, the search of measurements for integration in polar sensor coordinates is carried out according to the CV model in Cartesian coordinates instead of an approximate model in polar sensor coordinates. The position of each cell is converted into Cartesian coordinates and predicted according to an assumed velocity. Then, the predicted Cartesian position is converted back to polar sensor coordinates for multiframe accumulation. The use of the correct model improves integration effectiveness and consequently improves algorithm performance. To handle the weak target with unknown velocity, a velocity filter bank in mixed coordinates is presented. The influence of velocity mismatch on the performance of filter bank is analyzed, and an efficient strategy for filter bank design is proposed. Numerical results are presented to demonstrate the effectiveness of the proposed algorithm.

In this paper, we first propose a memristive chaotic system and implement it by circuit simulation. The chaotic dynamics and various attractors are analysed by using phase portrait, bifurcation diagram, and Lyapunov exponents. In particular, the system has robust chaos in a wide parameter range and the initial value space, which is favourable to the security communication application. Consequently, we further explore its application in image encryption and present a new scheme. Before image processing, the external key is protected by the Grain-128a algorithm and the initial values of the memristive system are updated with the plain image. We not only perform random pixel extraction and masking with the chaotic cipher, but also use them as control parameters for Brownian motion to obtain the permutation matrix. In addition, multiplication on the finite field GF(2⁸) is added to further enhance the cryptography. Finally, the simulation results verify that the proposed image encryption scheme has better performance and higher security, which can effectively resist various attacks.

In some tracking applications, due to the sensor characteristic, only range measurements are available. If this is the case, due to the lack of full position measurements, the observability of Cartesian states (e.g., position and velocity) are limited to particular cases. For general cases, the range measurements can be utilized by developing a state estimation algorithm in range-Doppler (R-D) plane to obtain accurate range and Doppler estimates. In this paper, a state estimation method based on the proper dynamic model in the R-D plane is proposed. The unscented Kalman filter is employed to handle the strong nonlinearity in the dynamic model. Two filtering initialization methods are derived to extract the initial state estimate and the initial covariance in the R-D plane from the first several range measurements. One is derived based on the well-known two-point differencing method. The other incorporates the correct dynamic model information and uses the unscented transformation method to obtain the initial state estimates and covariance, resulting in a model-based method, which capitalizes the model information to yield better performance. Monte Carlo simulation results are provided to illustrate the effectiveness and superior performance of the proposed state estimation and filter initialization methods.

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 liner active disturbance rejection control (LADRC) method based on the Q-Learning algorithm of reinforcement learning (RL) to control the six-degree-of-freedom motion of an autonomous underwater vehicle (AUV). The number of controllers is increased to realize AUV motion decoupling. At the same time, in order to avoid the oversize of the algorithm, combined with the controlled content, a simplified Q-learning algorithm is constructed to realize the parameter adaptation of the LADRC controller. Finally, through the simulation experiment of the controller with fixed parameters and the controller based on the Q-learning algorithm, the rationality of the simplified algorithm, the effectiveness of parameter adaptation, and the unique advantages of the LADRC controller are verified.

Initiated three decades ago, integrated design of controllers and fault detectors has continuously attracted research attention. The recent development of the unified control and detection framework with an observer-based residual generator in its core gives a more general form of the previous works. Its applications to residual centred modelling of uncertain control systems, fault detection in feedback control systems with uncertainties, fault-tolerant control (FTC) as well as control performance degradation monitoring, detection and recovery are introduced. In conclusion, some future perspectives are proposed.

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

This paper introduces an algorithm for beamforming systems by the aid of multidimensional harmonic retrieval (MHR). This algorithm resolves problems, removes limitations of sampling and provides a more robust beamformer. A new sample space is created that can be used for estimating weights of a new beamforming called spatial-harmonics retrieval beamformer (SHRB). Simulation results show that SHRB has a better performance, accuracy, and applicability and more powerful eigenvalues than conventional beamformers. A simple mathematical proof is provided. By changing the number of harmonics, as a degree of freedom that is missing in conventional beamformers, SHRB can achieve more optimal outputs without increasing the number of spatial or temporal samples. We will demonstrate that SHRB offers an improvement of 4 dB in signal to noise ratio (SNR) in bit error rate (BER) of $ {10}^{-4} $ over conventional beamformers. In the case of direction of arrival (DOA) estimation, SHRB can estimate the DOA of the desired signal with an SNR of ?25 dB, when conventional methods cannot have acceptable response.

In this paper, we propose a beam space coversion (BSC)-based approach to achieve a single near-field signal localization under uniform circular array (UCA). By employing the centro-symmetric geometry of UCA, we apply BSC to extract the two-dimensional (2-D) angles of near-field signal in the Vandermonde form, which allows for azimuth and elevation angle estimation by utilizing the improved estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. By substituting the calculated 2-D angles into the direction vector of near-field signal, the range parameter can be consequently obtained by the 1-D multiple signal classification (MUSIC) method. Simulations demonstrate that the proposed algorithm can achieve a single near-field signal localization, which can provide satisfactory performance and reduce computational complexity.

An automatic method for classifying frequency shift keying (FSK), minimum shift keying (MSK), phase shift keying (PSK), quadrature amplitude modulation (QAM), and orthogonal frequency division multiplexing (OFDM) is proposed by simultaneously using normality test, spectral analysis, and geometrical characteristics of in-phase-quadrature (I-Q) constellation diagram. Since the extracted features are unique for each modulation, they can be considered as a fingerprint of each modulation. We show that the proposed algorithm outperforms the previously published methods in terms of signal-to-noise ratio (SNR) and success rate. For example, the success rate of the proposed method for 64-QAM modulation at SNR=11 dB is 99%. Another advantage of the proposed method is its wide SNR range; such that the probability of classification for 16-QAM at SNR=3 dB is almost 1. The proposed method also provides a database for geometrical features of I-Q constellation diagram. By comparing and correlating the data of the provided database with the estimated I-Q diagram of the received signal, the processing gain of 4 dB is obtained. Whatever can be mentioned about the preference of the proposed algorithm are low complexity, low SNR, wide range of modulation set, and enhanced recognition at higher-order modulations.

In this paper, we introduce an incident angle based fusion method for radar and infrared sensors to improve the recognition rate of complex targets under half space scenarios, e.g., vehicles on the ground in this paper. For radar sensors, convolutional operation is introduced into the autoencoder, a “winner-take-all (WTA)” convolutional autoencoder (CAE) is used to improve the recognition rate of the radar high resolution range pro?le (HRRP). Moreover, different from the free space, the HRRP in half space is more complex. In order to get closer to the real situation, the half space HRRP is simulated as the dataset. The recognition rate has a growth more than 7% compared with the traditional CAE or denoised sparse autoencoder (DSAE). For infrared sensor, a convolutional neural network (CNN) is used for infrared image recognition. Finally, we combine the two results with the Dempster-Shafer (D-S) evidence theory, and the discounting operation is introduced in the fusion to improve the recognition rate. The recognition rate after fusion has a growth more than 7% compared with a single sensor. After the discounting operation, the accuracy rate has been improved by 1.5%, which validates the effectiveness of the proposed method.

Aerial image sequence mosaicking is one of the challenging research fields in computer vision. To obtain large-scale orthophoto maps with object detection information, we propose a vision-based image mosaicking algorithm without any extra location data. According to object detection results, we define a complexity factor to describe the importance of each input image and dynamically optimize the feature extraction process. The feature points extraction and matching processes are mainly guided by the speeded-up robust features (SURF) and the grid motion statistic (GMS) algorithm respectively. A robust reference frame selection method is proposed to eliminate the transformation distortion by searching for the center area based on overlaps. Besides, the sparse Levenberg-Marquardt (LM) algorithm and the heavy occluded frames removal method are applied to reduce accumulated errors and further improve the mosaicking performance. The proposed algorithm is performed by using multithreading and graphics processing unit (GPU) acceleration on several aerial image datasets. Extensive experiment results demonstrate that our algorithm outperforms most of the existing aerial image mosaicking methods in visual quality while guaranteeing a high calculation speed.

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

As high-dynamics and weak-signal are of two primary concerns of navigation using Global Navigation Satellite System (GNSS) signals, an acquisition algorithm based on three-time fractional Fourier transform (FRFT) is presented to simplify the calculation effectively. Firstly, the correlation results similar to linear frequency modulated (LFM) signals are derived on the basis of the high dynamic GNSS signal model. Then, the principle of obtaining the optimum rotation angle is analyzed, which is measured by FRFT projection lengths with two selected rotation angles. Finally, Doppler shift, Doppler rate, and code phase are accurately estimated in a real-time and low signal to noise ratio (SNR) wireless communication system. The theoretical analysis and simulation results show that the fast FRFT algorithm can accurately estimate the high dynamic parameters by converting the traditional two-dimensional search process to only three times FRFT. While the acquisition performance is basically the same, the computational complexity and running time are greatly reduced, which is more conductive to practical application.

Coherent change detection (CCD) is an effective method to detect subtle scene changes that occur between temporal synthetic aperture radar (SAR) observations. Most coherence estimators are obtained from a Hermitian product based on local statistics. Increasing the number of samples in the local window can improve the estimation bias, but cause the loss of the estimated images spatial resolution. The limitations of these estimators lead to unclear contour of the disturbed region, and even the omission of fine change targets. In this paper, a CCD approach is proposed to detect fine scene changes from multi-temporal and multi-angle SAR image pairs. Multi-angle CCD estimator can improve the contrast between the change target and the background clutter by jointly accumulating single-angle alternative estimator results without further loss of image resolution. The sensitivity of detection performance to image quantity and angle interval is analyzed. Theoretical analysis and experimental results verify the performance of the proposed algorithm.

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

Main lobe jamming seriously affects the detection performance of airborne early warning radar. The joint processing of polarization-space has become an effective way to suppress the main lobe jamming. To avoid the main beam distortion and wave crest migration caused by the main lobe jamming in adaptive beamforming, a joint optimization algorithm based on adaptive polarization canceller (APC) and stochastic variance reduction gradient descent (SVRGD) is proposed. First, the polarization plane array structure and receiving signal model based on primary and auxiliary array cancellation are established, and an APC iterative algorithm model is constructed to calculate the optimal weight vector of the auxiliary channel. Second, based on the stochastic gradient descent principle, the variance reduction method is introduced to modify the gradient through internal and external iteration to reduce the variance of the stochastic gradient estimation, the airspace optimal weight vector is calculated and the equivalent weight vector is introduced to measure the beamforming effect. Third, by setting up a planar polarization array simulation scene, the performance of the algorithm against the interference of the main lobe and the side lobe is analyzed, and the effectiveness of the algorithm is verified under the condition of short snapshot number and certain signal to interference plus noise ratio.

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

In order to simulate metamaterial rotational symmetric open region problems, unconditionally stable perfectly match layer (PML) implementation is proposed in the body of revolution (BOR) finite-difference time-domain (FDTD) lattice. More precisely, the proposed algorithm is implemented by the Crank-Nicolson (CN) Douglas-Gunn (DG) procedure for BOR metamaterial simulation. The constitutive relationship of metamaterial can be expressed by the Drude model and calculated by the piecewise linear recursive convolution (PLRC) approach. The effectiveness including absorption, efficiency, and accuracy is demonstrated through the numerical example. It can be concluded that the proposed implementation is to take the advantages of the CNDG-PML procedure, PLRC approach, and BOR-FDTD algorithm in terms of considerable accuracy, enhanced absorption and remarkable efficiency. Meanwhile, it can be demonstrated that the proposed scheme can maintain its unconditional stability when the time step exceeds the Courant-Friedrichs-Levy (CFL) condition.

Fabry-Perot (FP) antennas have characteristics of planar structures combined with high gain, and they have been widely used in wireless communications. With the progress of ongoing research, FP antennas have achieved various capabilities, but many of them are still under development, such as low-profile, wideband, circular polarization, multi-band, low-radar cross section (RCS) and reconfigurable features. This paper discusses the theoretical analysis methods and research progress of FP antennas, and explains the realization methods of different features of FP antennas. In order to indicate different technologies for realizing various capabilities, the key technologies and features of some of the latest designs are described. Finally, the research situation and prospects of FP antennas are summarized to guide their research directions in the future.

This work explores an alternative 3D geometry measurement method for non-cooperative spacecraft guiding navigation and proximity operations. From one snapshot of an unfocused light-field camera, the 3D point cloud of a non-cooperative spacecraft can be calculated from sub-aperture images with the epipolar plane image (EPI) based light-field rendering algorithm. A Chang’e?3 model (7.2 cm×5.6 cm×7.0 cm) is tested to validate the proposed technique. Three measurement distances (1.0 m, 1.2 m, 1.5 m) are considered to simulate different approaching stages. Measuring errors are quantified by comparing the light-field camera data with a high precision commercial laser scanner. The mean error distance for the three cases are 0.837 mm, 0.743 mm, and 0.973 mm respectively, indicating that the method can well reconstruct 3D geometry of a non-coope-rative spacecraft with a densely distributed 3D point cloud and is thus promising in space-related missions.

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.

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

In this paper, a low complexity direction of arrival (DOA) estimation method for massive uniform circular array (UCA) with single snapshot is proposed. Firstly, the coarse DOAs are estimated by finding the peaks from the circular convolution between a fixed coefficient vector and the received data vector. Thereafter, in order to refine coarse DOA estimates, we reconstruct the direction matrix based on the coarse DOA estimations and take the first order Taylor expansion with DOA estimation offsets into account. Finally, the refined estimations are obtained by compensating the offsets, which are obtained via least squares (LS) without any complex searches. In addition, the refinement can be iteratively implemented to enhance the estimation results. Compared to the offset search method, the proposed method achieves a better estimation performance while requiring lower complexity. Numerical simulations are presented to demonstrate the effectiveness of the proposed method.

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

The main contribution of this paper is the design of an event-triggered formation control for leader-following consensus in second-order multi-agent systems (MASs) under communication faults. All the agents must follow the trajectories of a virtual leader despite communication faults considered as smooth time-varying delays dependent on the distance between the agents. Linear matrix inequalities (LMIs)-based conditions are obtained to synthesize a controller gain that guarantees stability of the synchronization error. Based on the closed-loop system, an event-triggered mechanism is designed to reduce the control law update and information exchange in order to reduce energy consumption. The proposed approach is implemented in a real platform of a fleet of unmanned aerial vehicles (UAVs) under communication faults. A comparison between a state-of-the-art technique and the proposed technique has been provided, demonstrating the performance improvement brought by the proposed approach.

This paper employs system dynamics to explore how the synergy between technology management and technological capability affects new product development. The results show that the synergy between technology management and technological capability has positive impact on new product development. Moreover, the leading synergy processes between technology management and technological capability in different new product development stages are different. This paper deepens the theoretical understanding of how to achieve new product development, and also provides useful guidance for firms to implement new product development.

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

The scattering centers (SCs) of low-detectable targets (LDTs) have a low scattering intensity. It is difficult to build the SC model of an LDT using the existing methods because these methods mainly concern dominant SCs with strong scattering contributions. This paper presents an SC modeling approach to acquire the weak SCs of LDTs. We employ the induced currents at the LDT to search SCs, and the joint time-frequency transform together with the Hough transform to separate the scattering contributions of different SCs. Particle swarm optimization (PSO) is applied to improve the estimation results of SCs. The accuracy of the SC model built by this approach is verified by a full-wave numerical method. The validation results show that the SC model of the LDT can precisely simulate the signatures of high-resolution images, such as high-resolution range profile and inverse synthetic aperture radar (ISAR) images.

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

This paper introduces a fault-tolerant control (FTC) design for a faulty fixed-wing unmanned aerial vehicle (UAV). To constrain tracking errors against actuator faults, error constraint inequalities are first transformed to a new set of variables based on prescribed performance functions. Then, the commonly used and powerful proportional-integral-derivative (PID) control concept is employed to filter the transformed error variables. To handle the fault-induced nonlinear terms, a composite learning algorithm consisting of neural network and disturbance observer is incorporated for increasing flight safety. It is shown by Lyapunov stability analysis that the tracking errors are strictly constrained within the specified error bounds. Experimental results are presented to verify the feasibility of the developed FTC scheme.

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

Ultra-wideband (UWB) through-wall radar has a wide range of applications in non-contact human information detection and monitoring. With the integration of machine learning technology, its potential prospects include the physiological monitoring of patients in the hospital environment and the daily monitoring at home. Although many target detection methods of UWB through-wall radar based on machine learning have been proposed, there is a lack of an opensource dataset to evaluate the performance of the algorithm. This published dataset is measured by impulse radio UWB (IR-UWB) through-wall radar system. Three test subjects are measured in different environments and several defined motion status. Using the presented dataset, we propose a human-motion-status recognition method using a convolutional neural network (CNN), and the detailed dataset partition method and the recognition process flow are given. On the well-trained network, the recognition accuracy of testing data for three kinds of motion status is higher than 99.7%. The dataset presented in this paper considers a simple environment. Therefore, we call on all organizations in the UWB radar field to cooperate to build opensource datasets to further promote the development of UWB through-wall radar.

Rolling element bearings are machine components used to allow circular movement and hence deliver forces between components of machines used in diverse areas of industry. The likelihood of failure has the propensity of increasing under prolonged operation and varying working conditions. Hence, the accurate fault severity categorization of bearings is vital in diagnosing faults that arise in rotating machinery. The variability and complexity of the recorded vibration signals pose a great hurdle to distinguishing unique characteristic fault features. In this paper, the efficacy and the leverage of a pre-trained convolutional neural network (CNN) is harnessed in the implementation of a robust fault classification model. In the absence of sufficient data, this method has a high-performance rate. Initially, a modified VGG16 architecture is used to extract discriminating features from new samples and serves as input to a classifier. The raw vibration data are strategically segmented and transformed into two representations which are trained separately and jointly. The proposed approach is carried out on bearing vibration data and shows high-performance results. In addition to successfully implementing a robust fault classification model, a prognostic framework is developed by constructing a health indicator (HI) under varying operating conditions for a given fault condition.

When multiple ground-based radars (GB-rads) are utilized together to resolve three-dimensional (3-D) deformations, the resolving accuracy is related with the measurement geometry constructed by these radars. This paper focuses on constrained geometry analysis to resolve 3-D deformations from three GB-rads. The geometric dilution of precision (GDOP) is utilized to evaluate 3-D deformation accuracy of a single target, and its theoretical equation is derived by building a simplified 3-D coordinate system. Then for a 3-D scene, its optimal accuracy problem is converted into determining the minimum value of an objective function with a boundary constraint. The genetic algorithm is utilized to solve this constrained optimization problem. Numerical simulations are made to validate the correctness of the theoretical analysis results.

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

This paper focuses on the sea-surface weak target detection based on memory-fully convolutional network (M-FCN) in strong sea clutter. Firstly, the constant false alarm rate (CFAR) detection method utilizes a low threshold with high probability of false alarm to detect sea-surface weak targets after non-coherent integration. Reducing the detection threshold can generate a large number of false alarms while increasing the detection rate, and how to suppress a large number of false alarms is the key to improve the performance of weak target detection. Then, the detection result of the low threshold is operated to construct the target matrix suitable for the size of fully convolutional networks and the convolution operator form. Finally, the M-FCN architecture is designed to learn the different accumulation characteristics of the target and the sea clutter between different frames. For improving the detection performance, the historical multi-frame information is memorized by the network, and the end-to-end structure is established to detect sea-surface weak target automatically. Experimental results on measured data demonstrate that the M-FCN method outperforms the traditional track before detection (TBD) method and reduces false alarm tracks by 35.1%, which greatly improves the track quality.