In this paper, we investigate the issues of initialization and deployment of wireless sensor networks (WSNs) under IEEE 802.11b/g interference and fading channels using frequency hopping (FH). We propose an FH algorithm for WSNs, which is implemented and tested with a pair of nodes employing IPv6 over low power wireless personal area networks (6LoWPAN) standard. The merits and demerits of the proposed FH scheme in WSNs are studied under strong IEEE 802.11b/g interference and frequency selective fading channels. We compare the performance results of the proposed FH scheme with those obtained by single-channel radio in WSNs, and show that FH maintains very reliable data rates in the presence of adverse conditions where the single-channel radio fails. We determine a minimum center frequency offset of channels between IEEE 802.15.4 and IEEE 802.11b/g-based networks, which guarantees the error free network operation of IEEE 802.15.4 using a single channel. We design a second FH procedure comprising only four free channels (15, 20, 25, and 26) of IEEE 802.15.4 standard, and show that in the presence of nearby IEEE 802.11b/g interference, the IEEE 802.15.4 data rate using this method is always 98% and more.

Magnetic field gradient tensor measurement is an important technique to obtain position information of magnetic objects. When using magnetic field sensors to measure magnetic field gradient as the coefficients of tensor, field differentiation is generally approximated by field difference. As a result, magnetic objects positioning by magnetic field gradient tensor measurement always involves an inherent error caused by sensor sizes, leading to a reduction in detectable distance and detectable angle. In this paper, the inherent positioning error caused by magnetic field gradient tensor measurement is calculated and corrected by iterations based on the systematic position error distribution patterns. The results show that, the detectable distance range and the angle range of an ac magnetic object (2.44 Am$ \bf^2 $ @1 kHz) can be increased from (0.45 m, 0.75 m), (0$ ^\circ $, 25$ ^\circ $) to (0.30 m, 0.80 m), (0$ ^\circ $, 80$ ^\circ $), respectively.

A hybrid carrier (HC) scheme based on weighted-type fractional Fourier transform (WFRFT) has been proposed recently. While most of the works focus on HC scheme's inherent characteristics, little attention is paid to the WFRFT modulation recognition. In this paper, a new theory is provided to recognize the WFRFT modulation based on higher order cumulants (HOC). First, it is deduced that the optimal WFRFT received order can be obtained through the minimization of 4th-order cumulants, $ C_{42} $. Then, a combinatorial searching algorithm is designed to minimize $ C_{42} $. Finally, simulation results show that the designed scheme has a high recognition rate and the combinatorial searching algorithm is effective and reliable.

Sparse representation has attracted extensive attention and performed well on image super-resolution (SR) in the last decade. However, many current image SR methods face the contradiction of detail recovery and artifact suppression. We propose a multi-resolution dictionary learning (MRDL) model to solve this contradiction, and give a fast single image SR method based on the MRDL model. To obtain the MRDL model, we first extract multi-scale patches by using our proposed adaptive patch partition method (APPM). The APPM divides images into patches of different sizes according to their detail richness. Then, the multiresolution dictionary pairs, which contain structural primitives of various resolutions, can be trained from these multi-scale patches. Owing to the MRDL strategy, our SR algorithm not only recovers details well, with less jag and noise, but also significantly improves the computational efficiency. Experimental results validate that our algorithm performs better than other SR methods in evaluation metrics and visual perception.

In order to better represent infrared target features under different environments, a saliency detection method based on region covariance and global feature is proposed. Firstly, the region covariance features on different scale spaces and different image regions are extracted and transformed into sigma features, then combined with central position feature, the local salient map is generated. Next, a global salient map is generated by gray contrast and density estimation. Finally, the saliency detection result of infrared images is obtained by fusing the local and global salient maps. The experimental results show that the salient map of the proposed method has complete target features and obvious edges, and the proposed method is better than the state of art method both qualitatively and quantitatively.

This paper presents an approach for mitigating the cross-eye jamming using a dual-polarization array. By transmitting a sum beam and a difference beam in two orthogonal polarimetric channels, a synthesized transmitted beam with spatially varying polarization is produced, such that the polarization of the transmitted radar wave varies in azimuth or elevation. Thus, the phases of the signals received on the two antennas of a cross-eye jammer become unequal, and an additional phase difference is introduced to disrupt the 180° phase shifting in the retrodirective loop of the jammer. By means of beam scanning in a small angular range, the optimal beam steering configuration can be found to maximize the phase error for the mitigation of cross-eye jamming. As a result, the jamming performance of the cross-eye jammer degrades largely. Theoretical analysis and simulation results indicate that the proposed method is valid and feasible.

To automatically detect oil tanks in polarimetric synthetic aperture radar (SAR) images, a coastal oil tank detection method is proposed based on recognition of T-shaped harbor. First of all, the T-shaped harbor is detected to locate the region of interest (ROI) of oil tanks. Then all suspicious targets in the ROI are extracted by the segmentation of strong scattering targets and the classifier of H/α. The template targets are selected from the suspicious targets by the combination of a proposed circular degree parameter and the similarity parameter (SP) of the polarimetric coherency matrix. Finally, oil tanks are detected according to the statistics of the similarity parameter between each suspicious target and template targets in ROI. Polarimetric SAR data acquired by RADARSAT-2 over Berkeley and Singapore areas are used for testing. Experiment results show that most of the targets are correctly detected and the overall detection rate is close to 80%. The false rate is effectively reduced by the proposed algorithm compared with the method without T-shaped harbor recognition.

System of systems architecture (SoSA) has received increasing emphasis by scholars since Zachman ignited its flame in 1987. Given its complexity and abstractness, it is critical to validate and evaluate SoSA to ensure requirements have been met. Multiple qualities are discussed in the literature of SoSA evaluation, while research on functionality is scarce. In order to assess SoSA functionality, an extended influence diagram (EID) is developed in this paper. Meanwhile, a simulation method is proposed to elicit the conditional probabilities in EID through designing and executing SoSA. An illustrative anti-missile architecture case is introduced for EID development, architecture design, and simulation.

In this paper, a static weapon target assignment (WTA) problem is studied. As a critical problem in cooperative air combat, outcome of WTA directly influences the battle. Along with the cost of weapons rising rapidly, it is indispensable to design a target assignment model that can ensure minimizing targets survivability and weapons consumption simultaneously. Afterwards an algorithm named as improved artificial fish swarm algorithm-improved harmony search algorithm (IAFSA-IHS) is proposed to solve the problem. The effect of the proposed algorithm is demonstrated in numerical simulations, and results show that it performs positively in searching the optimal solution and solving the WTA problem.

Aiming at the intervention decision-making problem in manned/unmanned aerial vehicle (MAV/UAV) cooperative engagement, this paper carries out a research on allocation strategy of emergency discretion based on human factors engineering (HFE). Firstly, based on the brief review of research status of HFE, it gives structural description to emergency in the process of cooperative engagement and analyzes intervention of commanders. After that, constraint conditions of intervention decision-making of commanders based on HFE (IDMCBHFE) are given, and the mathematical model, which takes the overall efficiency value of handling emergencies as the objective function, is established. Then, through combining K-best and variable neighborhood search (VNS) algorithm, a K-best optimization variable neighborhood search mixed algorithm (KBOVNSMA) is designed to solve the model. Finally, through three groups of simulation experiments, effectiveness and superiority of the proposed algorithm are verified.

Redundancy is a common structure for warship system, and it is an effective way to improve the reliability of the system. In this paper, warship system is taken as the object of study, based on the system reliability equivalence principle, a spares demand rate calculation method for redundant system is proposed through structure transformation. According to the system analysis method, the general modeling data structure of spares support echelon and system indenture is established, and the mission success probability is taken as the optimization target to build the dynamic optimization model of carrying spares during the process of multi-phase. By introducing the Lagrange multiplier, the spares weight, volume and cost are transformed to the single target of the spares total scale, and the initial Lagrange factors and its dynamic adjustment policy is proposed. In a given example, the main influence factors of the carrying spares project are analyzed, and the study results are in accordance with the reality, which can provide a new approach to mission-oriented carrying spares optimization for the redundant system.

Multiple complex networks, each with different properties and mutually fused, have the problems that the evolving process is time varying and non-equilibrium, network structures are layered and interlacing, and evolving characteristics are difficult to be measured. On that account, a dynamic evolving model of complex network with fusion nodes and overlap edges (CNFNOEs) is proposed. Firstly, we define some related concepts of CNFNOEs, and analyze the conversion process of fusion relationship and hierarchy relationship. According to the property difference of various nodes and edges, fusion nodes and overlap edges are subsequently split, and then the CNFNOEs is transformed to interlacing layered complex networks (ILCN). Secondly, the node degree saturation and attraction factors are defined. On that basis, the evolution algorithm and the local world evolution model for ILCN are put forward. Moreover, four typical situations of nodes evolution are discussed, and the degree distribution law during evolution is analyzed by means of the mean field method. Numerical simulation results show that nodes unreached degree saturation follow the exponential distribution with an error of no more than 6%; nodes reached degree saturation follow the distribution of their connection capacities with an error of no more than 3%; network weaving coefficients have a positive correlation with the highest probability of new node and initial number of connected edges. The results have verified the feasibility and effectiveness of the model, which provides a new idea and method for exploring CNFNOE’s evolving process and law. Also, the model has good application prospects in structure and dynamics research of transportation network, communication network, social contact network, etc.

With the passage of time, it has become important to investigate new methods for updating data to better fit the trends of the grey prediction model. The traditional GM(1,1) usually sets the grey action quantity as a constant. Therefore, it cannot effectively fit the dynamic characteristics of the sequence, which results in the grey model having a low precision. The linear grey action quantity model cannot represent the index change law. This paper presents a grey action quantity model, the exponential optimization grey model (EOGM(1,1)), based on the exponential type of grey action quantity; it is constructed based on the exponential characteristics of the grey prediction model. The model can fully reflect the exponential characteristics of the simulation series with time. The exponential sequence has a higher fitting accuracy. The optimized result is verified using a numerical example for the fluctuating sequence and a case study for the index of the tertiary industry’s GDP. The results show that the model improves the precision of the grey forecasting model and reduces the prediction error.

This paper proposes a fast integral terminal sliding mode (ITSM) control method for a cascaded nonlinear dynamical system with mismatched uncertainties. Firstly, an integral terminal sliding mode surface is presented, which not only avoids the singularity in the traditional terminal sliding mode, but also addresses the mismatched problems in the nonlinear control system. Secondly, a new ITSM controller with finite convergence time based on the backstepping technique is derived for a cascaded nonlinear dynamical system with mismatched uncertainties. Thirdly, the convergence time of ITSM is analyzed, whose convergence speed is faster than those of two nonsingular terminal sliding modes. Finally, simulation results are presented in order to evaluate the effectiveness of ITSM control strategies for mismatched uncertainties.

A neural-network-based adaptive gain scheduling backstepping sliding mode control (NNAGS-BSMC) approach for a class of uncertain strict-feedback nonlinear system is proposed. First, the control problem of uncertain strict-feedback nonlinear systems is formulated. Second, the detailed design of NNAGSBSMC is described. The sliding mode control (SMC) law is designed to track a referenced output via backstepping technique. To decrease chattering result from SMC, a radial basis function neural network (RBFNN) is employed to construct the NNAGSBSMC to facilitate adaptive gain scheduling, in which the gains are scheduled adaptively via neural network (NN), with sliding surface and its differential as NN inputs and the gains as NN outputs. Finally, the verification example is given to show the effectiveness and robustness of the proposed approach. Contrasting simulation results indicate that the NNAGS-BSMC decreases the chattering effectively and has better control performance against the BSMC.

An optimal midcourse trajectory planning approach that considers the capture region (CR) of the terminal guidance is proposed in this article based on the Gauss pseudospectral method (GPM). Firstly, the planar CR of the proportional navigation in terminal guidance is analyzed and innovatively introduced in the midcourse trajectory planning problems, with the collision triangle (CT) serving as the ideal terminal states parameters of the midcourse phase, and the CR area serving as the robustness against target maneuvers. Secondly, the midcourse trajectory planning problem that considers the path, terminal and control constraints is formulated and the well-developed GPM is used to generate the nominal trajectory that meets the CR demands. The interceptor will reshape the trajectory only when the former CR fails to cover the target, which has loosened the critical demand for frequent trajectory modification. Finally, the simulations of four different scenarios are carried out and the results prove the effectiveness and optimality of the proposed method.

The paper deals with the state estimation of the widely used scaled unscented Kalman filter (UKF). In particular, the stress is laid on the scaling parameters selection principle for the scaled UKF. Several problems caused by recommended constant scaling parameters are highlighted. On the basis of the analyses, an effective scaled UKF is proposed with self-adaptive scaling parameters, which is easy to understand and implement in engineering. Two typical strong nonlinear examples are given and their simulation results show the effectiveness of the proposed principle and algorithm.

Designing a robust controller for a system with timevarying delays poses a major challenge. In this paper, we propose a method based on mixed sensitivity H_∞ for the control of linear time invariant (LTI) systems with varying time delays. The time delay is assumed bounded and the upper bound is known. In the technique we propose, the delay affecting the plant to be controlled is treated as an unmodeled uncertainty (in form of multiplicative uncertainty). That uncertainty is approximated and then an H_∞ based controller, for the plant represented by the multiplicative uncertainty and the nominal model, is calculated. The obtained H_∞ controller is used to control the LTI systems with varying time delays. Simulation examples are given to illustrate the effectiveness of the proposed method.

The disturbance torque of aerostatic bearings is in the same order of the reaction wheel, which causes difficulty in evaluation of the designed attitude control strategy of a nano-satellite based on the aerostatic bearing. Two approaches are proposed to model the disturbance torque. Firstly, the gravity induced moment, the vortex torque, and the damping moment are modeled separately. However, the vortex torque and the damping moment are coupled with each other as both of them are caused by the viscosity. In the second approach, the coupling effect is considered. A nano-satellite is constructed based on aerostatic bearing. The time history of the free rotation rate from an initial speed is measured by the gyro, which is further used to calculate the rotation angle and acceleration. The static vortex torque is measured via the removable micro-torque measurement system. Based on these data, the model parameters are identified and modeling errors are presented. Results show that the second model is more precise. The root mean squire error is less than 0.5×10^-4 N·m and the relative error of the static vortex torque is 0.16%.

The time dependent vehicle routing problem with time windows (TDVRPTW) is considered. A multi-type ant system (MTAS) algorithm hybridized with the ant colony system (ACS) and the max-min ant system (MMAS) algorithms is proposed. This combination absorbs the merits of the two algorithms in solutions construction and optimization separately. In order to improve the efficiency of the insertion procedure, a nearest neighbor selection (NNS) mechanism, an insertion local search procedure and a local optimization procedure are specified in detail. And in order to find a balance between good scouting performance and fast convergence rate, an adaptive pheromone updating strategy is proposed in the MTAS. Computational results confirm the MTAS algorithm's good performance with all these strategies on classic vehicle routing problem with time windows (VRPTW) benchmark instances and the TDVRPTW instances, and some better results especially for the number of vehicles and travel times of the best solutions are obtained in comparison with the previous research.

The rapid development of data communication in modern era demands secure exchange of information. Steganography is an established method for hiding secret data from an unauthorized access into a cover object in such a way that it is invisible to human eyes. The cover object can be image, text, audio, or video. This paper proposes a secure steganography algorithm that hides a bitstream of the secret text into the least significant bits (LSBs) of the approximation coefficients of the integer wavelet transform (IWT) of grayscale images as well as each component of color images to form stego-images. The embedding and extracting phases of the proposed steganography algorithms are performed using the MATLAB software. Invisibility, payload capacity, and security in terms of peak signal to noise ratio (PSNR) and robustness are the key challenges to steganography. The statistical distortion between the cover images and the stego-images is measured by using the mean square error (MSE) and the PSNR, while the degree of closeness between them is evaluated using the normalized cross correlation (NCC). The experimental results show that, the proposed algorithms can hide the secret text with a large payload capacity with a high level of security and a higher invisibility. Furthermore, the proposed technique is computationally efficient and better results for both PSNR and NCC are achieved compared with the previous algorithms.

With society’s increasing dependence on critical infrastructure such as power grids and communications systems, the robustness of these systems has attracted significant attention. Failure of some nodes can trigger a cascading failure, which completely fragments the network, necessitating recovery efforts to improve robustness of complex systems. Inspired by real-world scenarios, this paper proposes repair models after two kinds of network failures, namely complete and incomplete collapse. In both models, three kinds of repair strategies are possible, including random selection (RS), node selection based on single network node degree (SD), and node selection based on double network node degree (DD). We find that the node correlation in each of the two coupled networks affects repair efficiency. Numerical simulation and analysis results suggest that the repair node ratio and repair strategies may have a significant impact on the economics of the repair process. The results of this study thus provide insight into ways to improve the robustness of coupled networks after cascading failures.

For the system with the fuzzy failure state, the effects of the input random variables and the fuzzy failure state on the fuzzy probability of failure for the structural system are studied, and the moment-independence global sensitivity analysis (GSA) model is proposed to quantitatively measure these effects. According to the fuzzy random theory, the fuzzy failure state is transformed into an equivalent new random variable for the system, and the complementary function of the membership function of the fuzzy failure state is defined as the cumulative distribution function (CDF) of the new random variable. After introducing the new random variable, the equivalent performance function of the original problem is built. The difference between the unconditional fuzzy probability of failure and conditional fuzzy probability of failure is defined as the moment-independent GSA index. In order to solve the proposed GSA index efficiently, the Kriging-based algorithm is developed to estimate the defined moment-independence GSA index. Two engineering examples are employed to verify the feasibility and rationality of the presented GSA model, and the advantages of the developed Kriging method are also illustrated.