This paper takes further insight into the sparse geometry which offers a larger array aperture than uniform linear array (ULA) with the same number of physical sensors. An efficient method based on closed-form robust Chinese remainder theorem (CFRCRT) is presented to estimate the direction of arrival (DOA) from their wrapped phase with permissible errors. The proposed algorithm has significantly less computational complexity than the searching method while maintaining similar estimation precision. Furthermore, we combine all phase discrete Fourier transfer (APDFT) and the CFRCRT algorithm to achieve a considerably high DOA estimation precision. Both the theoretical analysis and simulation results demonstrate that the proposed algorithm has a higher estimation precision as well as lower computation complexity.

Polar codes represent one of the major breakthroughs in 5G standard, and have been proven to be able to achieve the symmetric capacity of binary-input discrete memoryless channels using the successive cancellation list (SCL) decoding algorithm. However, the SCL algorithm suffers from a large amount of memory overhead. This paper proposes an adaptive simplified decoding algorithm for multiple cyclic redundancy check (CRC) polar codes. Simulation results show that the proposed method can reduce the decoding complexity and memory space. It can also acquire the performance gain in the low signal to noise ratio region.

Media based modulation (MBM) is expected to be a prominent modulation scheme, which has access to the high data rate by using radio frequency (RF) mirrors and fewer transmit antennas. Associated with multiuser multiple input multiple output (MIMO), the MBM scheme achieves better performance than other conventional multiuser MIMO schemes. In this paper, the massive MIMO uplink is considered and a conjunctive MBM transmission scheme for each user is employed. This conjunctive MBM transmission scheme gathers aggregate MBM signals in multiple continuous time slots, which exploits the structured sparsity of these aggregate MBM signals. Under this kind of scenario, a multiuser detector with low complexity based on the compressive sensing (CS) theory to gain better detection performance is proposed. This detector is developed from the greedy sparse recovery technique compressive sampling matching pursuit (CoSaMP) and exploits not only the inherently distributed sparsity of MBM signals but also the structured sparsity of multiple aggregate MBM signals. By exploiting these sparsity, the proposed CoSaMP based multiuser detector achieves reliable detection with low complexity. Simulation results demonstrate that the proposed CoSaMP based multiuser detector achieves better detection performance compared with the conventional methods.

A joint two-dimensional (2D) direction-of-arrival (DOA) and radial Doppler frequency estimation method for the L-shaped array is proposed in this paper based on the compressive sensing (CS) framework. Revised from the conventional CS-based methods where the joint spatial-temporal parameters are characterized in one large scale matrix, three smaller scale matrices with independent azimuth, elevation and Doppler frequency are introduced adopting a separable observation model. Afterwards, the estimation is achieved by $L_{1}$-norm minimization and the Bayesian CS algorithm. In addition, under the L-shaped array topology, the azimuth and elevation are separated yet coupled to the same radial Doppler frequency. Hence, the pair matching problem is solved with the aid of the radial Doppler frequency. Finally, numerical simulations corroborate the feasibility and validity of the proposed algorithm.

Carrier frequency and symbol rate estimation are the main contents of parameter estimation, which is the basis of modulation recognition and further processing of signals especially in non-cooperative communication. With the development of wireless communication, the signal transmission environment has become increasingly bad, causing more difficulties in parameter estimation. It is well known that the signal cycle spectrum is robust to noises and signal parameters are closely related. In practice, it is impossible to calculate the cyclic spectrum of infinite length data signals. When using finite length data to obtain a cycle spectrum, the truncation noise is induced, resulting in interference. It is necessary to overcome the influence of noises in order to improve the detection ability of discrete spectral lines. An improved method of the discrete spectral line extraction algorithm is proposed by reflecting the amplitude advantage of discrete spectral lines through salient features of continuous noises in discrete spectral line neighborhood.

Person re-identification (re-id) involves matching a person across nonoverlapping views, with different poses, illuminations and conditions. Visual attributes are understandable semantic information to help improve the issues including illumination changes, viewpoint variations and occlusions. This paper proposes an end-to-end framework of deep learning for attribute-based person re-id. In the feature representation stage of framework, the improved convolutional neural network (CNN) model is designed to leverage the information contained in automatically detected attributes and learned low-dimensional CNN features. Moreover, an attribute classifier is trained on separate data and includes its responses into the training process of our person re-id model. The coupled clusters loss function is used in the training stage of the framework, which enhances the discriminability of both types of features. The combined features are mapped into the Euclidean space. The L2 distance can be used to calculate the distance between any two pedestrians to determine whether they are the same. Extensive experiments validate the superiority and advantages of our proposed framework over state-of-the-art competitors on contemporary challenging person re-id datasets.

In many practical applications of image segmentation problems, employing prior information can greatly improve segmentation results. This paper continues to study one kind of prior information, called prior distribution. Within this research, there is no exact template of the object; instead only several samples are given. The proposed method, called the parametric distribution prior model, extends our previous model by adding the training procedure to learn the prior distribution of the objects. Then this paper establishes the energy function of the active contour model (ACM) with consideration of this parametric form of prior distribution. Therefore, during the process of segmenting, the template can update itself while the contour evolves. Experiments are performed on the airplane data set. Experimental results demonstrate the potential of the proposed method that with the information of prior distribution, the segmentation effect and speed can be both improved efficaciously.

Not confined to a certain point, such as waveform, this paper systematically studies the low-intercept radio frequency (RF) stealth design of synthetic aperture radar (SAR) from the system level. The study is carried out from two levels. In the first level, the maximum low-intercept range equation of the conventional SAR system is deduced firstly, and then the maximum low-intercept range equation of the multiple-input multiple-output SAR system is deduced. In the second level, the waveform design and imaging method of the low-intercept RF SAR system are given and verified by simulation. Finally, the main technical characteristics of the low-intercept RF stealth SAR system are given to guide the design of low-intercept RF stealth SAR system.

This paper studies the adaptive beamforming algorithm based on the frequency diverse array (FDA) array where the interference is located at the same angle (but different range) with the target. We take the cross subarray-based FDA with sinusoidal frequency offset (CSB sin-FDA) as the receiving array instead of the basic FDA. The sampling covariance matrix under insufficient snapshot can be corrected by the automatic diagonal loading method. On the basis of decomposing the mismatched steering vector error into a vertical component and a parallel one, this paper searches the vertical component of the error by the quadratic constraint method. The numerical simulation verifies that the beamformer based on the CSB sin-FDA can effectively hold the mainlobe at the target position when the snapshot is insufficient or the steering vector is mismatched.

The traversal search of multi-dimensional parameter during the process of hypersonic target echo signal coherent integration, leads to the problem of large amounts of calculation and poor real-time performance. In view of these problems, a modified polynomial Radon-polynomial Fourier transform (MPRPFT) hypersonic target coherent integration detection algorithm based on Doppler feedback is proposed in this paper. Firstly, the Doppler estimation value of the target is obtained by using the target point information obtained by subsequent non-coherent integration detection. Then, the feedback adjustment of the coherent integration process is performed by using the acquired target Doppler estimation value. Finally, the coherent integration is completed after adjusting the search interval of compensation. The simulation results show that the algorithm can effectively reduce the computational complexity and improve the real-time performance on the basis of the effective coherent integration of hypersonic target echo signals.

Measuring the business-IT alignment (BITA) of an organization determines its alignment level, provides directions for further improvements, and consequently promotes the organizational performances. Due to the capabilities of enterprise architecture (EA) in interrelating different business/IT viewpoints and elements, the development of EA is superior to support BITA measurement. Extant BITA measurement literature is sparse when it concerns EA. The literature tends to explain how EA viewpoints or models correlate with BITA, without discussing where to collect and integrate EA data. To address this gap, this paper attempts to propose a specific BITA measurement process through associating a BITA maturity model with a famous EA framework: DoD Architectural Framework 2.0 (DoDAF2.0). The BITA metrics in the maturity model are connected to the meta-models and models of DoDAF2.0. An illustrative ArchiSurance case is conducted to explain the measurement process. Systematically, this paper explores the process of BITA measurement from the viewpoint of EA, which helps to collect the measurement data in an organized way and analyzes the BITA level in the phase of architecture development.

To analyze the behavioral model of the command, control, communication, computer, intelligence, surveillance, reconnaissance (C4ISR) architecture, we propose an executable modeling and analyzing approach to it. First, the meta concept model of the C4ISR architecture is introduced. According to the meta concept model, we construct the executable meta models of the C4ISR architecture by extending the meta models of fUML. Then, we define the concrete syntax and executable activity algebra (EAA) semantics for executable models. The semantics functions are introduced to translating the syntax description of executable models into the item of EAA. To support the execution of models, we propose the executable rules which are the structural operational semantics of EAA. Finally, an area air defense of the C4ISR system is used to illustrate the feasibility of the approach.

In order to cope with the increasing threat of the ballistic missile (BM) in a shorter reaction time, the shooting policy of the layered defense system needs to be optimized. The main decision-making problem of shooting optimization is how to choose the next BM which needs to be shot according to the previous engagements and results, thus maximizing the expected return of BMs killed or minimizing the cost of BMs penetration. Motivated by this, this study aims to determine an optimal shooting policy for a two-layer missile defense (TLMD) system. This paper considers a scenario in which the TLMD system wishes to shoot at a collection of BMs one at a time, and to maximize the return obtained from BMs killed before the system demise. To provide a policy analysis tool, this paper develops a general model for shooting decision-making, the shooting engagements can be described as a discounted reward Markov decision process. The index shooting policy is a strategy that can effectively balance the shooting returns and the risk that the defense mission fails, and the goal is to maximize the return obtained from BMs killed before the system demise. The numerical results show that the index policy is better than a range of competitors, especially the mean returns and the mean killing BM number.

This paper presents a path planning approach for rotary unmanned aerial vehicles (R-UAVs) in a known static rough terrain environment. This approach aims to find collision-free and feasible paths with minimum altitude, length and angle variable rate. First, a three-dimensional (3D) modeling method is proposed to reduce the computation burden of the dynamic models of R-UAVs. Considering the length, height and tuning angle of a path, the path planning of R-UAVs is described as a tri-objective optimization problem. Then, an improved multi-objective particle swarm optimization algorithm is developed. To render the algorithm more effective in dealing with this problem, a vibration function is introduced into the collided solutions to improve the algorithm efficiency. Meanwhile, the selection of the global best position is taken into account by the reference point method. Finally, the experimental environment is built with the help of the Google map and the 3D terrain generator World Machine. Experimental results under two different rough terrains from Guilin and Lanzhou of China demonstrate the capabilities of the proposed algorithm in finding Pareto optimal paths.

Many-objective optimization problems take challenges to multi-objective evolutionary algorithms. A number of non-dominated solutions in population cause a difficult selection towards the Pareto front. To tackle this issue, a series of indicator-based multi-objective evolutionary algorithms (MOEAs) have been proposed to guide the evolution progress and shown promising performance. This paper proposes an indicator-based many-objective evolutionary algorithm called $ \boldsymbol{\varepsilon} $ -indicator-based shuffled frog leaping algorithm ($\boldsymbol{\varepsilon}$ -MaOSFLA), which adopts the shuffled frog leaping algorithm as an evolutionary strategy and a simple and effective $\boldsymbol{\varepsilon}$ -indicator as a fitness assignment scheme to press the population towards the Pareto front. Compared with four state-of-the-art MOEAs on several standard test problems with up to 50 objectives, the experimental results show that $\boldsymbol{\varepsilon}$ -MaOSFLA outperforms the competitors.

A study on knowledge transfer in a mutli-agent organization is performed by applying the basic principle in physics such as the kinetic theory. Based on the theoretical analysis of the knowledge accumulation process and knowledge transfer attributes, a special type of knowledge field (KF) is introduced and the knowledge diffusion equation (KDE) is developed. The evolution of knowledge potential is modeled by lattice kinetic equation and verified by numerical experiments. The new equation-based modeling developed in this paper is meaningful to simulate and predict the knowledge transfer process in firms. The development of the lattice kinetic model (LKM) for knowledge transfer can contribute to the knowledge management theory, and the managers can also simulate the knowledge accumulation process by using the LKM.

An integral sliding mode guidance law (ISMGL) combined with the advantages of the integral sliding mode control (SMC) method is designed to address maneuvering target interception problems with impact angle constraints. The relative motion equation of the missile and the target considering the impact angle constraint is established in the longitudinal plane, and an integral sliding mode surface is constructed. The proposed guidance law resolves the existence of a steady-state error problem in the traditional SMC. Such a guidance law ensures that the missile hits the target with an ideal impact angle in finite time and the missile is kept highly robust throughout the interception process. By adopting the dynamic surface control method, the ISMGL is designed considering the impact angle constraints and the autopilot dynamic characteristics. According to the Lyapunov stability theorem, all states of the closed-loop system are finally proven to be uniformly bounded. Simulation results are compared with the general sliding mode guidance law and the trajectory shaping guidance law, and the findings verify the effectiveness and superiority of the ISMGL.

The identification of nonlinear systems with multiple sampled rates is a difficult task. The motivation of our paper is to study the parameter estimation problem of Hammerstein systems with dead-zone characteristics by using the dual-rate sampled data. Firstly, the auxiliary model identification principle is used to estimate the unmeasurable variables, and the recursive estimation algorithm is proposed to identify the parameters of the static nonlinear model with the dead-zone function and the parameters of the dynamic linear system model. Then, the convergence of the proposed identification algorithm is analyzed by using the martingale convergence theorem. It is proved theoretically that the estimated parameters can converge to the real values under the condition of continuous excitation. Finally, the validity of the proposed algorithm is proved by the identification of the dual-rate sampled nonlinear systems.

Nonlinearity and implicitness are common degradation features of the stochastic degradation equipment for prognostics. These features have an uncertain effect on the remaining useful life (RUL) prediction of the equipment. The current data-driven RUL prediction method has not systematically studied the nonlinear hidden degradation modeling and the RUL distribution function. This paper uses the nonlinear Wiener process to build a dual nonlinear implicit degradation model. Based on the historical measured data of similar equipment, the maximum likelihood estimation algorithm is used to estimate the fixed coefficients and the prior distribution of a random coefficient. Using the on-site measured data of the target equipment, the posterior distribution of a random coefficient and actual degradation state are step-by-step updated based on Bayesian inference and the extended Kalman filtering algorithm. The analytical form of the RUL distribution function is derived based on the first hitting time distribution. Combined with the two case studies, the proposed method is verified to have certain advantages over the existing methods in the accuracy of prediction.

This paper considers the parameters and reliability characteristics estimation problem of the generalized Rayleigh distribution under progressively Type-Ⅱ censoring with random removals, that is, the number of units removed at each failure time follows the binomial distribution. The maximum likelihood estimation and the Bayesian estimation are derived. In the meanwhile, through a great quantity of Monte Carlo simulation experiments we have studied different hyperparameters as well as symmetric and asymmetric loss functions in the Bayesian estimation procedure. A real industrial case is presented to justify and illustrate the proposed methods. We also investigate the expected experimentation time and discuss the influence of the parameters on the termination point to complete the censoring test.

Performance degradation or system resource exhaustion can be attributed to inadequate computing resources as a result of software aging. In the real world, the workload of a web server varies with time, which will cause a nonlinear aging phenomenon. The nonlinear property often makes analysis and modelling difficult. Workload is one of the important factors influencing the speed of aging. This paper quantitatively analyzes the workload-aging relation and proposes a framework for aging control under varying workloads. In addition, this paper proposes an approach that employs prior information of workloads to accurately forecast incoming system exhaustion. The workload data are used as a threshold to divide the system resource usage data into multiple sections, while in each section the workload data can be treated as a constant. Each section is described by an individual autoregression (AR) model. Compared with other AR models, the proposed approach can forecast the aging process with a higher accuracy.