An unsplit-field higher order nearly perfectly matched layer (NPML) based on the auxiliary differential equation approach is introduced in three-dimensional finite-difference time-domain lattices. The proposed scheme has the advantage of both the NPML scheme and the higher order concept in terms of the improved absorbing performance and considerable computational efficiency. By incorporating with the generalized material independent concept, the proposed implementation is independent of the material’s type. Thus, it has the advantages of terminating arbitrary media without changing the updated equations in the PML regions. Its effectiveness and efficiency is further demonstrated through numerical examples.

An obstacle perception system for intelligent vehicle is proposed. The proposed system combines the stereo version technique and the deep learning network model, and is applied to obstacle perception tasks in complex environment. In this paper, we provide a complete system design project, which includes the hardware parameters, software framework, algorithm principle, and optimization method. In addition, special experiments are designed to demonstrate that the performance of the proposed system meets the requirements of actual application. The experiment results show that the proposed system is valid to both standard obstacles and non-standard obstacles, and suitable for different weather and lighting conditions in complex environment. It announces that the proposed system is flexible and robust to the intelligent vehicle.

In this paper, a space-time adaptive processing (STAP) method is proposed for the airborne radar with the array amplitude-phase error considered, which is based on atomic norm minimization (ANM). In the conventional ANM-based STAP method, the influence of the array amplitude-phase error is not considered and restrained, which inevitably causes performance deterioration. To solve this problem, the array amplitude-phase error is firstly estimated. Then, by pre-estimating the array amplitude-phase error information, a modified ANM model is built, in which the array amplitude-phase error factor is separated from the clutter response and the clutter covariance matrix (CCM) to improve the estimation accuracy of the CCM. To prove that the atomic norm theory is applicable in the presence of the array amplitude-phase error, the clutter sparsity is analyzed in this paper. Meanwhile, simulation results demonstrate that the proposed method is superior to the state-of-the-art STAP method. Moreover, the measured data is used to verify the effectiveness of the proposed method.

By incorporating the higher order concept, the piecewise linear recursive convolution (PLRC) method and Crank-Nicolson Douglas-Gunn (CNDG) algorithm, the unconditionally stable complex frequency shifted nearly perfectly matched layer (CFS-NPML) is proposed to terminate the left-handed material (LHM) domain. The proposed scheme takes advantages of CFS-NPML formulation, the higher order concept PLRC method and the unconditionally stable CNDG algorithm in terms of absorbing performance, computational efficiency, calculation accuracy and convenient implementation. A numerical example is carried out to demonstrate the effectiveness and efficiency of the proposed scheme. The results indicate that the proposed scheme can not only have considerable absorbing performance but also maintain the unconditional stability of the algorithm with the enlargement of time steps.

Most of the reconstruction-based robust adaptive beamforming (RAB) algorithms require the covariance matrix reconstruction (CMR) by high-complexity integral computation. A Gauss-Legendre quadrature (GLQ) method with the highest algebraic precision in the interpolation-type quadrature is proposed to reduce the complexity. The interference angular sector in RAB is regarded as the GLQ integral range, and the zeros of the three-order Legendre orthogonal polynomial is selected as the GLQ nodes. Consequently, the CMR can be efficiently obtained by simple summation with respect to the three GLQ nodes without integral. The new method has significantly reduced the complexity as compared to most state-of-the-art reconstruction-based RAB techniques, and it is able to provide the similar performance close to the optimal. These advantages are verified by numerical simulations.

The adaptive digital beamforming technique in the space-polarization domain suppresses the interference with forming the coupling nulls of space and polarization domain. When there is the interference in mainlobe, it will cause serious mainlobe distortion, that the target detection suffers from. To overcome this problem and make radar cope with the complex multiple interferences scenarios, we propose a multiple mainlobe and/or sidelobe interferences suppression method for dual polarization array radar. Specifically, the proposed method consists of a signal preprocessing based on the proposed angle estimation with degree of polarization (DoP), and a filtering criterion based on the proposed linear constraint. The signal preprocessing provides the accurate estimated parameters of the interference, which contributes to the criterion for null-decoupling in the space-polarization domain of mainlobe. The proposed method can reduce the mainlobe distortion in the space-polarization domain while suppressing the multiple mainlobe and/or sidelobe interferences. The effectiveness of the proposed method is verified by simulations.

To avoid the complicated motion compensation in interferometric inverse synthetic aperture (InISAR) and achieve real-time three-dimensional (3D) imaging, a novel approach for 3D imaging of the target only using a single echo is presented. This method is based on an isolated scatterer model assumption, thus the scatterers in the beam can be extracted individually. The radial range of each scatterer is estimated by the maximal likelihood estimation. Then, the horizontal and vertical wave path difference is derived by using the phase comparison technology for each scatterer, respectively. Finally, by utilizing the relationship among the 3D coordinates, the radial range, the horizontal and vertical wave path difference, the 3D image of the target can be reconstructed. The reconstructed image is free from the limitation in InISAR that the image plane depends on the target ’s own motions and on its relative position with respect to the radar. Furthermore, a phase ambiguity resolution method is adopted to ensure the success of the 3D imaging when phase ambiguity occurs. It can be noted that the proposed phase ambiguity resolution method only uses one antenna pair and does not require a priori knowledge, whereas the existing phase ambiguity methods may require two or more antenna pairs or a priori knowledge for phase unwarping. To evaluate the performance of the proposed method, the theoretical analyses on estimation accuracy are presented and the simulations in various scenarios are also carried out.

Inverse synthetic aperture radar (ISAR) imaging of the target with the non-rigid body is very important in the field of radar signal processing. In this paper, a motion compensation method combined with the preprocessing and global technique is proposed to reduce the influence of micro-motion components in the fast time domain, and the micro-Doppler (m-D) signal in the slow time domain is separated by the improved complex-valued empirical-mode decomposition (CEMD) algorithm, which makes the m-D signal more effectively distinguishable from the signal for the main body by translating the target to the Doppler center. Then, a better focused ISAR image of the target with the non-rigid body can be obtained consequently. Results of the simulated and raw data demonstrate the effectiveness of the algorithm.

A novel synthetic aperture radar (SAR) image de-noising method based on the local pixel grouping (LPG) principal component analysis (PCA) and guided filter is proposed. This method contains two steps. In the first step, we process the noisy image by coarse filters, which can suppress the speckle effectively. The original SAR image is transformed into the additive noise model by logarithmic transform with deviation correction. Then, we use the pixel and its nearest neighbors as a vector to select training samples from the local window by LPG based on the block similar matching. The LPG method ensures that only the similar sample patches are used in the local statistical calculation of PCA transform estimation, so that the local features of the image can be well preserved after coefficients shrinkage in the PCA domain. In the second step, we do the guided filtering which can effectively eliminate small artifacts left over from the coarse filtering. Experimental results of simulated and real SAR images show that the proposed method outstrips the state-of-the-art image de-noising methods in the peak signal-to-noise ratio (PSNR), the structural similarity (SSIM) index and the equivalent number of looks (ENLs), and is of perceived image quality.

The airborne conformal array (CFA) radar’s clutter ridges are range-modulated, which result in a bias in the estimation of the clutter covariance matrix (CCM) of the cell under test (CUT), further, reducing the clutter suppression performance of the airborne CFA radar. The clutter ridges can be effectively compensated by the space-time separation interpolation (STSINT) method, which costs less computation than the space-time interpolation (STINT) method, but the performance of interpolation algorithms is seriously affected by the short-range clutter, especially near the platform height. Location distributions of CFA are free, which yields serious impact that range spaces of steering vector matrices are non-orthogonal complement and even no longer disjoint. Further, a new method is proposed that the short-range clutter is pre-processed by oblique projection with the intersected range spaces (OPIRS), and then clutter data after being pre-processed are compensated to the desired range bin through the STSINT method. The OPIRS also has good compatibility and can be used in combination with many existing methods. At the same time, oblique projectors of OPIRS can be obtained in advance, so the proposed method has almost the same computational load as the traditional compensation method. In addition, the proposed method can perform well when the channel error exists. Computer simulation results verify the effectiveness of the proposed method.

Aiming at the problem of gliding near space hypersonic vehicle (NSHV) trajectory prediction, a trajectory prediction method based on aerodynamic acceleration empirical mode decomposition (EMD) is proposed. The method analyzes the motion characteristics of the skipping gliding NSHV and verifies that the aerodynamic acceleration of the target has a relatively stable rule. On this basis, EMD is used to extract the trend of aerodynamic acceleration into multiple sub-items, and aggregate sub-items with similar attributes. Then, a prior basis function is set according to the aerodynamic acceleration stability rule, and the aggregated data are fitted by the basis function to predict its future state. After that, the prediction data of the aerodynamic acceleration are used to drive the system to predict the target trajectory. Finally, experiments verify the effectiveness of the method. In addition, the distribution of prediction errors in space is discussed, and the reasons are analyzed.

Decomposition of tasks and selection of optimal schemes are key procedures in high-end equipment development processes. However, such procedures are highly innovative, technology intensive, interdisciplinary, and multi-party engineering projects, making the decomposition and scheme selection more difficult and complicated than that in the development of ordinary equipment. In this study, we consider three factors, namely, functional structure, task granularity, and task feasibility in task decomposition of high-end equipment development. Based on the principles of systems engineering, a method of task decomposition is proposed. As for decomposition scheme selection, a method based on the superiority and inferiority ranking (SIR) method mixed information and multiple attribute decision making is proposed by considering attributes of scheme feasibility, uncertainty risk and task integration complexity. To verify the proposed method, development of a military electric vehicle is used as an example to demonstrate the calculation process.

This paper analyzes a problem processing mechanism in a new collaboration system between the main manufacturer and the supplier in the “main manufacturer-supplier” mode, which has been widely applied in the collaborative development management of the complex product. This paper adopts the collaboration theory, the evolutionary game theory and numerical simulation to analyze the decision-making mechanism where one upstream supplier and one downstream manufacturer must process an unpredicted problem without any advance contract in common. Results show that both players ’ decision-makings are in some correlation with the initial state, income impact coefficients, and dealing cost. It is worth noting that only the initial state influences the final decision, while income impact coefficients and dealing cost just influence the decision process. This paper shows reasonable and practical suggestions for the manufacturer and supplier in a new collaboration system for the first time and is dedicated to the managerial implications on reducing risks of processing problems.

Least square support vector regression (LSSVR) is a method for function approximation, whose solutions are typically non-sparse, which limits its application especially in some occasions of fast prediction. In this paper, a sparse algorithm for adaptive pruning LSSVR algorithm based on global representative point ranking (GRPR-AP-LSSVR) is proposed. At first, the global representative point ranking (GRPR) algorithm is given, and relevant data analysis experiment is implemented which depicts the importance ranking of data points. Furthermore, the pruning strategy of removing two samples in the decremental learning procedure is designed to accelerate the training speed and ensure the sparsity. The removed data points are utilized to test the temporary learning model which ensures the regression accuracy. Finally, the proposed algorithm is verified on artificial datasets and UCI regression datasets, and experimental results indicate that, compared with several benchmark algorithms, the GRPR-AP-LSSVR algorithm has excellent sparsity and prediction speed without impairing the generalization performance.

Most earth observation satellites (EOSs) are low-orbit satellites equipped with optical sensors that cannot see through clouds. Hence, cloud coverage, high dynamics, and cloud uncertainties are important issues in the scheduling of EOSs. The proactive-reactive scheduling framework has been proven to be effective and efficient for the uncertain scheduling problem and has been extensively employed. Numerous studies have been conducted on methods for the proactive scheduling of EOSs, including expectation, chance-constrained, and robust optimization models and the relevant solution algorithms. This study focuses on the reactive scheduling of EOSs under cloud uncertainties. First, using an example, we describe the reactive scheduling problem in detail, clarifying its significance and key issues. Considering the two key objectives of observation profits and scheduling stability, we construct a multi-objective optimization mathematical model. Then, we obtain the possible disruptions of EOS scheduling during execution under cloud uncertainties, adopting an event-driven policy for the reactive scheduling. For the different disruptions, different reactive scheduling algorithms are designed. Finally, numerous simulation experiments are conducted to verify the feasibility and effectiveness of the proposed reactive scheduling algorithms. The experimental results show that the reactive scheduling algorithms can both improve observation profits and reduce system perturbations.

In this paper, the method of extracting guidance information such as the line-of-sight (LOS) rates under the anti-infrared decoy state for the roll-pitch seeker is researched. Coordinate systems which are used to describe the angles transform are defined. The LOS angles reconstruction model of the roll-pitch seeker in inertial space is established. A Kalman filter model for extracting LOS rates of the roll-pitch seeker is proposed. In this model, the target performs constant acceleration (CA) model maneuvers. The error model of LOS rates extraction under infrared decoy state is established. Several existing methods of extracting LOS rates under anti-infrared decoy state are listed in this paper. Different from the existing methods, a novel method that uses extrapolated values of target accelerations as filter measurements is proposed to solve the guidance information extraction problem under the anti-infrared decoy state. Numerical simulations are conducted to verify the effectiveness of the proposed method under different target maneuvering models such as the CA model, the CA extended model and the singer model. The simulation results show that the proposed method of extracting guidance information such as LOS rates for the roll-pitch seeker under the anti-infrared decoy state is effective.

This study focuses on implementing consensus tracking using both open-loop and closed-loop $D^{\alpha} $ -type iterative learning control (ILC) schemes, for fractional-order multi-agent systems (FOMASs) with state-delays. The desired trajectory is constructed by introducing a virtual leader, and the fixed communication topology is considered and only a subset of followers can access the desired trajectory. For each control scheme, one controller is designed for one agent individually. According to the tracking error between the agent and the virtual leader, and the tracking errors between the agent and neighboring agents during the last iteration (for open-loop scheme) or the current running (for closed-loop scheme), each controller continuously corrects the last control law by a combination of communication weights in the topology to obtain the ideal control law. Through the rigorous analysis, sufficient conditions for both control schemes are established to ensure that all agents can achieve the asymptotically consistent output along the iteration axis within a finite-time interval. Sufficient numerical simulation results demonstrate the effectiveness of the control schemes, and provide some meaningful comparison results.

Extreme learning machine (ELM) has been proved to be an effective pattern classification and regression learning mechanism by researchers. However, its good performance is based on a large number of hidden layer nodes. With the increase of the nodes in the hidden layers, the computation cost is greatly increased. In this paper, we propose a novel algorithm, named constrained voting extreme learning machine (CV-ELM). Compared with the traditional ELM, the CV-ELM determines the input weight and bias based on the differences of between-class samples. At the same time, to improve the accuracy of the proposed method, the voting selection is introduced. The proposed method is evaluated on public benchmark datasets. The experimental results show that the proposed algorithm is superior to the original ELM algorithm. Further, we apply the CV-ELM to the classification of superheat degree (SD) state in the aluminum electrolysis industry, and the recognition accuracy rate reaches 87.4%, and the experimental results demonstrate that the proposed method is more robust than the existing state-of-the-art identification methods.

Model predictive controllers (MPC) with the two-loop scheme are successful approaches practically and can be classified into two main categories, tube-based MPC and MPC-based reference governors (RG). In this paper, an enhanced two-loop MPC design is proposed for a pre-stabilized system with the bounded uncertainty subject to the input and state constraints. The proposed method offers less conservatism than the tube-based MPC methods by enlarging the restricted input constraint. Contrary to the MPC-based RGs, the investigated method improves tracking performance of the pre-stabilized system while satisfying the constraints. Additionally, the robust global asymptotic stability of the closed-loop system is guaranteed in a novel procedure with terminal constraint relaxation. Simulation of the proposed method on a servo system shows its effectiveness in comparison to the others.

A consensus-distributed fault-tolerant (CDFT) control law is proposed for a class of leader-following multi-vehicle cooperative attack (MVCA) systems in this paper. In particular, the switching communication topologies, stochastic multi-hop time-varying delays, and actuator faults are considered, which may lead to system performance degradation or on certain occasions even cause system instability. Firstly, the estimator of actuator faults for the following vehicle is designed to identify the actuator faults under a fixed topology. Then the CDFT control protocol and trajectory following error are derived by the relevant content of Lyapunov stability theory, the graph theory, and the matrix theory. The CDFT control protocol is proposed in the same manner, where a more realistic scenario is considered, in which the maximum trajectory following error and information on the switching topologies during the cooperative attack are available. Finally, numerical simulation are carried out to indicate that the proposed distributed fault-tolerant (DFT) control law is effective.

Due to the simplicity and flexibility of the power law process, it is widely used to model the failures of repairable systems. Although statistical inference on the parameters of the power law process has been well developed, numerous studies largely depend on complete failure data. A few methods on incomplete data are reported to process such data, but they are limited to their specific cases, especially to that where missing data occur at the early stage of the failures. No framework to handle generic scenarios is available. To overcome this problem, from the point of view of order statistics, the statistical inference of the power law process with incomplete data is established in this paper. The theoretical derivation is carried out and the case studies demonstrate and verify the proposed method. Order statistics offer an alternative to the statistical inference of the power law process with incomplete data as they can reformulate current studies on the left censored failure data and interval censored data in a unified framework. The results show that the proposed method has more flexibility and more applicability.

The optimization of inspection intervals for composite structures has been proposed, but only one damage type, dent damage, has been addressed so far. The present study focuses on the two main damage types of dent and delamination, and a model for optimizing the inspection interval of composite structures is proposed to minimize the total maintenance cost on the premise that the probability of structure failure will not exceed the acceptable level. In order to analyze the damage characteristics and the residual strength of the composite structure, the frequency, energy, size, and depth of the damage are studied, and the situation of missing detection during the inspection is considered. The structural residual strength and total maintenance cost are quantified corresponding to different inspection intervals. The proposed optimization method relieves the constraints in previous simulation methods, and is more consistent with the actual situation. Finally, the outer wing of aircraft is taken as an example, and with the historical cases and experimental data, the optimization method is verified. The optimal inspection interval is shorter than the actually implemented inspection interval, and the corresponding maintenance cost is reduced by 23.3%. The result shows the feasibility and effectiveness of the proposed optimization method.