The code tracking loop is a key component for user positioning. The pseudorange information of BeiDou B1 signals has been fused and changed for vector tracking, so a correlation output model for complex scenarios is designed to prevent the propagation of error and valuate the signal performance. The relevant software and hardware factors that affect the output are analyzed. A single channel time-division multiplexing (TDM) method for multicorrelation data extraction is proposed. Statistical characteristics of the correlation output data for both vector and scalar structures are evaluated. Simulation results show that correlation outputs for both structures follow normal or Chi-squared distributions in normal conditions, and the Gamma distribution in harsh conditions. It is shown that a tracking model based on the multi-channel fusion hardly changes the probability distribution of the correlation output in the normal case, but it reduces the ranging error of the code loop, and hence the tracking ability of the code loop for weak signals is improved. Furthermore, vector tracking changes the pseudorange characteristics of channels anytime, and affects the mutual correlation outputs of the code loops in the abnormal case. This study provides a basis for the subsequent design of autonomous integrity algorithms for vector tracking.

The measurement of the rolling angle of the projectile is one of the key technologies for the terminal correction projectile. To improve the resolution accuracy of the rolling angle in the laser seeker weapon system, the imaging model of the detector, calculation model of the position and the signal-to-noise ratio (SNR) model of the circuit are built to derive both the correlation between the resolution error of the rolling angle and the spot position, and the relation between the position resolution error and the SNR. Then the influence of each parameter on the SNR is analyzed at large, and the parameters of the circuit are determined. Meanwhile, the SNR and noise voltage of the circuit are calculated according to the SNR model and the decay model of the laser energy. Finally, the actual photoelectric detection circuit is built, whose SNR is measured to be up to 53 dB. It can fully meet the requirement of 0.5° for the resolution error of the rolling angle, thereby realizing the analysis of critical technology for photoelectric detection of laser seeker signals.

The direction of arrival (DOA) estimation problem in the presence of sensor location errors is studied and an algorithm based on space alternating generalized expectation-maximization (SAGE) is presented. First, the narrowband case is considered. Based on the small perturbation assumption, this paper proposes an augmentation scheme so as to estimate DOA and perturbation parameters. The E-step and M-step of the SAGE algorithm in this case are derived. Then, the algorithm is extended to the wideband case. The wideband SAGE algorithm is derived in frequency domain by jointing all frequency bins. Simulation results show that the algorithm achieves good convergence and high parameter estimation precision.

In this paper, a method to infer global depth ordering for monocular images is presented. Firstly a distance metric is defined with color, compactness, entropy and edge features to estimate the difference between pixels and seeds, which can ensure the superpixels to obtain more accurate object contours. To correctly infer local depth relationship, a weighting descriptor is designed that combines edge, T-junction and saliency features to avoid wrong local inference caused by a single feature. Based on the weighting descriptor, a global inference strategy is presented, which not only can promote the performance of global depth ordering, but also can infer the depth relationships correctly between two non-adjacent regions. The simulation results on the BSDS500 dataset, Cornell dataset and NYU 2 dataset demonstrate the effectiveness of the approach.

In this paper, the 40-Gbps orthogonal frequency division multiple access (OFDMA) technology enabled by subcarrier allocation in the form of integrated architecture for the intra-cell is proposed in the downlink transmission passive broadband optical access system. The data-carrying subcarriers in the inverse fast Fourier transform/fast Fourier transform (IFFT/FFT) size of 1 024 points are successfully divided into three sub-channels, in which each sub-channel has 256 useful subcarriers, by using adaptive dynamic bandwidth allocation (DBA). Taking the inherent advantages of M-ary quadrature amplitude modulation (MQAM) modulation mechanism into account, the performance of the absolutely identical MQAM format over the different sub-channels for the downstream OFDMA-passive optical network (PON) is investigated based on the intensity modulation direct detection (IMDD) system by simulations. The results show that three parallel 4QAM or 16QAM or 64QAM OFDMA data, which are transmitted over three sub-channels, is more suitable for different sub-channel allocations, respectively. In addition, comparing with single port 4/16/64QAM OFDM over the same access system, the receiver sensitivity economizes -0.6 dBm, 0.6 dBm, 4.6 dBm at the bit error rate (BER) value of 10$^{ - 3}$ respectively.

A near-field three-dimensional (3D) imaging method combining multichannel joint sparse recovery (MJSR) and fast Gaussian gridding nonuniform fast Fourier transform (FGG-NUFFT) is proposed, based on a perfect combination of the compressed sensing (CS) theory and the matched filtering (MF) technique. The approach has the advantages of high precision and high efficiency: multichannel joint sparse constraint is adopted to improve the problem that the images recovered by the single channel imaging algorithms do not necessarily share the same positions of the scattering centers; the CS dictionary is constructed by combining MF and FGG-NUFFT, so as to improve the imaging efficiency and memory requirement. Firstly, a near-field 3D imaging model of joint sparse recovery is constructed by combining the MF-based imaging method. Secondly, FGG-NUFFT and reverse FGG-NUFFT are used to replace the interpolation and Fourier transform in MF-based imaging methods, and a sensing matrix with high precision and high efficiency is constructed according to the traditional imaging process. Thirdly, a fast imaging recovery is performed by using the improved separable surrogate functionals (SSF) optimization algorithm, only with matrix and vector multiplication. Finally, a 3D imagery of the near-field target is obtained by using both the horizontal and the pitching interferometric phase information. This paper contains two imaging models, the only difference is the sub-aperture method used in inverse synthetic aperture radar (ISAR) imaging. Compared to traditional CS-based imaging methods, the proposed method includes both forward transform and inverse transform in each iteration, which improves the quality of reconstruction. The experimental results show that, the proposed method improves the imaging accuracy by about $\pmb{O(10)}$, accelerates the imaging speed by five times and reduces the memory usage by about $\pmb{O(10^2)}$.

Target detection for wideband radar has recently received extensive attention. The classical energy integrating (EI) detector will always accumulate excess clutter or noise energy, which leads to unacceptable performance deterioration if the detection window is not selected properly. In this paper, an EI detector for the distributed targets in the Gaussian environment is proposed. First, at the stage of preparatory work, the target models are proposed, then, the problem formulation is introduced. Subsequently, in the aspect of optimizing the method of detection window search and the method of threshold setting, the detailed design stages of the proposed detector are provided. Furthermore, theoretical analyses show that the proposed detector is easy to hardware implementation, and it does not need the prior knowledge about the spatial distribution of the target scattering centers in practical radar detection application. Finally, the performance assessment conducted by Monte Carlo simulations verifies that the proposed detector outperforms the conventional detectors.

An antenna adjustment strategy is developed for the target tracking problem in the collocated multiple-input multiple-output (MIMO) radar. The basic technique of this strategy is to optimally allocate antennas by the prior information in the tracking recursive period, with the objective of enhancing the worst-case estimate precision of multiple targets. On account of the posterior Cramer-Rao lower bound (PCRLB) offering a quantitative measure for target tracking accuracy, the PCRLB of joint direction-of-arrival (DOA) and Doppler is derived and utilized as the optimization criterion. It is shown that the dynamic antenna selection problem is NP-hard, and an efficient technique which combines convex relaxation with local search is put forward as the solution. Simulation results demonstrate the outperformance of the proposed strategy to the fixed antenna configuration and heuristic search algorithm. Moreover, it is able to offer close-to performance of the exhaustive search method.

Resource allocation for an equipment development task is a complex process owing to the inherent characteristics, such as large amounts of input resources, numerous sub-tasks, complex network structures, and high degrees of uncertainty. This paper presents an investigation into the influence of resource allocation on the duration and cost of sub-tasks. Mathematical models are constructed for the relationships of the resource allocation quantity with the duration and cost of the sub-tasks. By considering the uncertainties, such as fluctuations in the sub-task duration and cost, rework iterations, and random overlaps, the tasks are simulated for various resource allocation schemes. The shortest duration and the minimum cost of the development task are first formulated as the objective function. Based on a multi-objective particle swarm optimization (MOPSO) algorithm, a multi-objective evolutionary algorithm is constructed to optimize the resource allocation scheme for the development task. Finally, an uninhabited aerial vehicle (UAV) is considered as an example of a development task to test the algorithm, and the optimization results of this method are compared with those based on non-dominated sorting genetic algorithm-Ⅱ (NSGA-Ⅱ), non-dominated sorting differential evolution (NSDE) and strength pareto evolutionary algorithm-Ⅱ (SPEA-Ⅱ). The proposed method is verified for its scientific approach and effectiveness. The case study shows that the optimization of the resource allocation can greatly aid in shortening the duration of the development task and reducing its cost effectively.

Moth-flame optimization (MFO) is a novel metaheuristic algorithm inspired by the characteristics of a moth's navigation method in nature called transverse orientation. Like other metaheuristic algorithms, it is easy to fall into local optimum and leads to slow convergence speed. The chaotic map is one of the best methods to improve exploration and exploitation of the metaheuristic algorithms. In the present study, we propose a chaos-enhanced MFO (CMFO) by incorporating chaos maps into the MFO algorithm to enhance its performance. The chaotic map is utilized to initialize the moths' population, handle the boundary overstepping, and tune the distance parameter. The CMFO is benchmarked on three groups of benchmark functions to find out the most efficient one. The performance of the CMFO is also verified by using two real engineering problems. The statistical results clearly demonstrate that the appropriate chaotic map (singer map) embedded in the appropriate component of MFO can significantly improve the performance of MFO.

Test of consistency is critical for the analytic hierarchy process (AHP) methodology. When a pairwise comparison matrix (PCM) fails the consistency test, the decision maker (DM) needs to make revisions. The state of the art focuses on changing a single entry or creating a new matrix based on the original inconsistent matrix so that the modified matrix can satisfy the consistency requirement. However, we have noticed that the reason that causes inconsistency is not only numerical inconsistency, but also logical inconsistency, which may play a more important role in the whole inconsistency. Therefore, to realize satisfactory consistency, first of all, we should change some entries that form a directed circuit to make the matrix logically consistent, and then adjust other entries within acceptable deviations to make the matrix numerically consistent while preserving most of the original comparison information. In this paper, we firstly present some definitions and theories, based on which two effective methods are provided to identify directed circuits. Four optimization models are proposed to adjust the original inconsistent matrix. Finally, illustrative examples and comparison studies show the effectiveness and feasibility of our method.

For imbalanced datasets, the focus of classification is to identify samples of the minority class. The performance of current data mining algorithms is not good enough for processing imbalanced datasets. The synthetic minority over-sampling technique (SMOTE) is specifically designed for learning from imbalanced datasets, generating synthetic minority class examples by interpolating between minority class examples nearby. However, the SMOTE encounters the overgeneralization problem. The density-based spatial clustering of applications with noise (DBSCAN) is not rigorous when dealing with the samples near the borderline. We optimize the DBSCAN algorithm for this problem to make clustering more reasonable. This paper integrates the optimized DBSCAN and SMOTE, and proposes a density-based synthetic minority over-sampling technique (DSMOTE). First, the optimized DBSCAN is used to divide the samples of the minority class into three groups, including core samples, borderline samples and noise samples, and then the noise samples of minority class is removed to synthesize more effective samples. In order to make full use of the information of core samples and borderline samples, different strategies are used to over-sample core samples and borderline samples. Experiments show that DSMOTE can achieve better results compared with SMOTE and Borderline-SMOTE in terms of precision, recall and F-value.

This study proposes a multiple attribute group decision-making (MAGDM) approach on the basis of the plant growth simulation algorithm (PGSA) and interval 2-tuple weighted average operators for uncertain linguistic weighted aggregation (ULWA). We provide an example for illustration and verification and compare several aggregation operators to indicate the optimality of the assembly method. In addition, we present two comparisons to demonstrate the practicality and effectiveness of the proposed method. The method can be used not only to aggregate MAGDM problems but also to solve multi-granularity uncertain linguistic information. Its high reliability, easy programming, and high-speed calculation can improve the efficiency of ULWA characteristics. Finally, the proposed method has the exact characteristics for linguistic information processing and can effectively avoid information distortion and loss.

Guaranteed quality of service (QoS) support has been an open issue of distributed wireless networks for years. The IEEE 802.11e provides a valuable method for this purpose. However, it could only provide service differentiation, rather than service guarantee, for multi-priority traffic. Many studies have tried to improve its QoS ability, but still leave some problems. This paper investigates these problems and proposes a scheme called the priority access based on busy tone (PABT) to improve the QoS performance. To guarantee the priority channel access, this scheme uses an in-band busy tone to limit the transmission of lower-priority traffic when higher-priority traffic has packets to send. Based on that, it also optimizes the contention window tuning according to the flows number of each traffic type individually, in order to improve the throughput as well as the traffic capacity. Simulation results show that the proposed scheme significantly improves the real-time traffic capacity, throughput, delay, fairness and packet loss rate.

Both time-delays and anti-windup (AW) problems are conventional problems in system design, which are scarcely discussed in cellular neural networks (CNNs). This paper discusses stabilization for a class of distributed time-delayed CNNs with input saturation. Based on the Lyapunov theory and the Schur complement principle, a bilinear matrix inequality (BMI) criterion is designed to stabilize the system with input saturation. By matrix congruent transformation, the BMI control criterion can be changed into linear matrix inequality (LMI) criterion, then it can be easily solved by the computer. It is a one-step AW strategy that the feedback compensator and the AW compensator can be determined simultaneously. The attraction domain and its optimization are also discussed. The structure of CNNs with both constant timedelays and distribute time-delays is more general. This method is simple and systematic, allowing dealing with a large class of such systems whose excitation satisfies the Lipschitz condition. The simulation results verify the effectiveness and feasibility of the proposed method.

For switched linear system with colored measurement noises, the identification difficulties of this system are that there exist unknown switching information, unknown middle variables and noise terms in the information vector. For the mentioned issues, the fuzzy clustering and the multi-innovation recursive identification algorithm are used to deal with these problems. Firstly, the mode detection is transformed into the detection of membership degree values confirmed by the fuzzy clustering method, and the problem of mode detection is solved by judgment and decision of the fuzzy membership values. Moreover, the multi-innovation recursive identification algorithm based on the generalized auxiliary model is proposed to estimate the parameters of the switched linear system with colored noises. Finally, the effectiveness of the proposed method is verified by the results of the simulation example.

The solvability of the coupled Riccati differential equations appearing in the differential game approach to the formation control problem is vital to the finite horizon Nash equilibrium solution. These equations (if solvable) can be solved numerically by using the terminal value and the backward iteration. To investigate the solvability and solution of these equations the formation control problem as the differential game is replaced by a discrete-time dynamic game. The main contributions of this paper are as follows. First, the existence of Nash equilibrium controls for the discretetime formation control problem is shown. Second, a backward iteration approximate solution to the coupled Riccati differential equations in the continuous-time differential game is developed. An illustrative example is given to justify the models and solution.

An event-triggered scheme is proposed to solve the problems of robust guaranteed cost control for a class of two-dimensional (2-D) discrete-time systems. Firstly, an eventtriggered scheme is proposed for 2-D discrete-time systems with parameter uncertainties and sector nonlinearities. Then, according to the Lyapunov functional method, the sufficient conditions for the existence of event-triggered robust guaranteed cost controller for 2-D discrete-time systems with parameter uncertainties and sector nonlinearities are given. Furthermore, based on the sufficient conditions and the linear matrix inequality (LMI) technique, the problem of designing event-triggered robust guaranteed cost controller is transformed into a feasible solution problem of LMI. Finally, a numerical example is given to demonstrate that, under the proposed event-triggered robust guaranteed cost control, the closed-loop system is asymptotically stable and fewer communication resources are occupied.

KANG Rui was born in 1966. He is a Changjiang Scholar, and a distinguished professor awarded by the Chinese Ministry of Education. He is the Chinese director of International Center for Resilience and Safety of Critical Infrastructure (CRESCI) and the Chinese director of Sino-French Risk Science and Engineering (RISE) Lab. Now, he also works in School of Reliability and Systems Engineering, Beihang University, Beijing, China. He received his bachelor's and master's degrees in electrical engineering from Beihang University in 1987 and 1990, respectively. His main research interests include belief reliability theory, reliability-centered systems engineering, reliability design and testing theory and methods for high-reliable and long-lifetime product and resilience modeling and evaluation for cyber-physical system. He has published eight books and more than 150 research papers. He is also the associate editor of IEEE Transaction on Reliability and the associate editor of Proceedings of the Insititution of Mechanical Engineers, Part O: Journal of Risk and Reliability. He is the founder of China Prognostics and Health Management Society and also a famous reliability expert in Chinese industry. He received several awards from the Chinese government for his outstanding scientific contributions, including second prize of National Science and Technology Progress Award, and first prize for Science and Technology Progress Award of Ministry of Industry and Technology, etc. E-mail: kangrui@buaa.edu.cn

During maintainability demonstration, the maintenance time for complex systems consisting of mixed technologies generally conforms to a mixture distribution. However existing maintainability standards and guidance do not explain explicitly how to deal with this situation. This paper develops a comprehensive maintainability demonstration method for complex systems with a mixed maintenance time distribution. First of all, a K-means algorithm and an expectation-maximization (EM) algorithm are used to partition the maintenance time data for all possible clusters. The Bayesian information criterion (BIC) is then used to choose the optimal model. After this, the clustering results for equipment are obtained according to their degree of membership. The degree of similarity for the maintainability of different kinds of equipment is then determined using the projection method. By using a Bootstrap method, the prior distribution is obtained from the maintenance time data for the most similar equipment. Then, a test method based on Bayesian theory is outlined for the maintainability demonstration. Finally, the viability of the proposed approach is illustrated by means of an example.