This paper presents a new phase unwrapping algorithm based on the unscented Kalman filter (UKF) for synthetic aperture radar (SAR) interferometry. This method is the result of combining an UKF with path-following strategy and an omni-directional local phase slope estimator. This technique performs simultaneously noise filtering and phase unwrapping along the high-quality region to the low-quality region, which is also able to avoid going directly through the noisy regions. In addition, phase slope is estimated directly from the sample frequency spectrum of the complex interferogram, by which the underestimation of phase slope is overcome. Simulation and real data processing results validate the effectiveness of the proposed method, and show a significant improvement with respect to the extended Kalman filtering (EKF) algorithm and some conventional phase unwrapping algorithms in some situations.

The technique of terahertz pulses generated from the photoconductive switches has been applied in the ultrafast electrical pulse metrology recently. A lumped-element theoretical model is established to describe the performance of the LT-GaAs ultrafast photoconductive switch used in the ultrafast pulse standard. The carrier transport processes of the photoexcited semiconductor, the attenuation and dispersion during terahertz pulse propagating are considered in the theoretical model. According to the experimental parameters, the waveforms of the generated terahertz pulses are calculated under optical excitations with different wavelengths of 840 nm and 450 nm, respectively. And comparisons between the theoretical results and the experimental results are carried out.

A novel modified optimization technique known as the multi-objective micro particle swarm optimization (MO-MicPSO) is proposed for polyphase coded signal design. The proposed MO-MicPSO requires only a small population size compared with the standard particle swarm optimization that uses a larger population size. This new method is guided by an elite archive to finish the multi-objective optimization. The orthogonal polyphase coded signal (OPCS) can fundamentally improve the multiple input multiple output (MIMO) radar system performance, with which the radar system has high resolution and abundant signal channels. Simulation results on the polyphase coded signal design show that the MO-MicPSO can perform quite well for this high-dimensional multi-objective optimized problem. Compared with particle swarm optimization or genetic algorithm, the proposed MO-MicPSO has a better optimized efficiency and less time consumption.

Space-time adaptive processing (STAP) has been proven to be one of the best techniques capable of detecting weak moving targets in strong clutter environment and has been widely applied in airborne ground moving target indication (GMTI) radar. This paper applies an amplitude and phase estimation (APES) approach to two aspects of the STAP algorithm. Firstly, APES is applied to accurately describe the clutter characteristic in angle-Doppler domain. Then, APES is incorporated into the standard STAP algorithm to improve its performance without increasing transmitting/receiving channel and pulse number. The experimental examples show that the detection performance can be improved by using the APES technique, as well as the high computational complexity can be avoided.

A novel identification method for point source, coherently distributed (CD) source and incoherently distributed (ICD) source is proposed. The differences among the point source, CD source and ICD source are studied. According to the different characters of covariance matrix and general steering vector of the array received source, a second order blind identification method is used to separate the sources, the mixing matrix could be obtained. From the mixing matrix, the type of the source is identified by using an amplitude criterion. And the direction of arrival for the array received source is estimated by using the matching pursuit algorithm from the vectors of the mixing matrix.  omputer simulations validate the efficiency of the method.

The problem of combined radar imagery from multiple sparse frequency subbands initially incoherent to each other is of practical importance for radar target discrimination. A new coherent processing technique based on probability density analysis of the subband data is proposed, which is applicable for radar imaging from measurements of two or more initially incoherent radar subbands. The coherence parameters for both amplitude and phase are obtained by combining modern spectral analysis with probability density estimation. The major advantage of the proposed technique lies in that it enables much more robust cohering for the sparse subband data of real-world complex targets.

The midcourse ballistic closely spaced objects (CSO) create blur pixel-cluster on the space-based infrared focal plane, making the super-resolution of CSO quite necessary. A novel algorithm of CSO joint super-resolution and trajectory estimation is presented. The algorithm combines the focal plane CSO dynamics and radiation models, proposes a novel least square objective function from the space and time information, where CSO radiant intensity is excluded and initial dynamics (position and velocity) are chosen as the model parameters. Subsequently, the quantum-behaved particle swarm optimization (QPSO) is adopted to optimize the objective function to estimate model parameters, and then CSO focal plane trajectories and radiant intensities are computed. Meanwhile, the estimated CSO focal plane trajectories from multiple space-based infrared focal planes are associated and filtered to estimate the CSO stereo ballistic trajectories. Finally, the performance (CSO estimation precision of the focal plane coordinates, radiant intensities, and stereo ballistic trajectories, together with the computation load) of the algorithm is tested, and the results show that the algorithm is effective and feasible.

A 3D motion and geometric information system of single-antenna radar is proposed, which can be supported by spotlight synthetic aperture radar (SAR) system and inverse SAR (ISAR) system involving relative 3D motion of the rigid target. In this system, applying the geometry invariance of the rigid target, the unknown 3D shape and motion of the radar target can be reconstructed from the 1D range data of some scatterers extracted from the high-resolution range image. Compared with the current 1D-to-3D algorithm, in the proposed algorithm, the requirement of the 1D range data is expanded to incomplete formation involving large angular motion of the target and hence, the quantity of the scatterers and the abundance of 3D motion are enriched. Furthermore, with the three selected affine coordinates fixed, the multi-solution problem of the reconstruction is solved and the technique of nonlinear optimization can be successfully utilized in the system. Two simulations are implemented which verify the higher robustness of the system and the better performance of the 3D reconstruction for the radar target with unknown relative motion.

Orthogonal frequency division multiplexing (OFDM) radar with multicarrier phase-coded waveforms has been recently introduced to achieve high range resolution. The conventional method for obtaining the high resolution range profile (HRRP) is based on matched filters. A method of synthesizing HRRP based on the fast Fourier transform (FFT) and decoding is proposed. The mathematical expressions of HRRP are derived by assuming an elementary scenario of point-scattering targets. Based on the characteristic of OFDM multicarrier signals, it mainly analyzes the influence on HRRP exerted by several factors, such as velocity compensation errors, the sampling frequency offset, and so on. The conclusions are significant for the design of the OFDM imaging radar. Finally, the simulation results demonstrate the validity of the conclusions.

Health management permits the reliability of a system and plays a increasingly important role for achieving efficient system-level maintenance. It has been used for remaining useful life (RUL) prognostics of electronics-rich system including avionics. Prognostics and health management (PHM) have become highly desirable to provide avionics with system level health management. This paper presents a health management and fusion prognostic model for avionics system, combining three baseline prognostic approaches that are model-based, data-driven and knowledge-based approaches, and integrates merits as well as eliminates some limitations of each single approach to achieve fusion prognostics and improved prognostic performance of RUL estimation. A fusion model built upon an optimal linear combination forecast model is then utilized to fuse single prognostic algorithm representing the three baseline approaches correspondingly, and the presented case study shows that the fusion prognostics can provide RUL estimation more accurate and more robust than either algorithm alone.

A methodology for automatically generating risk scenarios is presented. Its main idea is to let the system model “express itself” through simulation. This is achieved by having the simulation model driven by an elaborated simulation engine, which: (i) manipulates the generation of branch points, i.e. event occurrence times; (ii) employs a depth-first systematic exploration strategy to cover all possible branch paths at each branch point. In addition, a backtracking technique, as an extension, is implemented to recover some missed risk scenarios. A widely discussed dynamic reliability example (a holdup tank) is used to aid in the explanation of and to demonstrate the effectiveness of the proposed methodology.

A new method to improve prediction precision of GM(1,1) model with unequal time interval is presented. The grey derivative is multiplied by a parameter to guarantee the time response function satisfying approximately exponential function distribution. To simplify the process of parametric estimation, an approximate value is taken for the multiplied parameter. Then the estimators of coefficient of development and grey action quantity can be derived. At the same time, the principle of the new information priority is also considered. We take the last item of the first-order accumulated generation operator (1-AGO) on raw data sequence as the initial condition in the time response function. Then the new information can be taken full advantage of through the improved initial condition. Some properties of this new model are also discussed. The presented method is actually a combination of improvement of grey derivative and improvement of the initial condition. The results of an example indicate that the proposed method can improve prediction precision prominently.

An adaptive support vector machine decision feedback equalizer (ASVM-DFE) based on the least square support vector machine (LS-SVM) is proposed, it solves linear system iteratively with less computational intensity. An adaptive non-singleton fuzzy support vector machine decision feedback equalizer (ANSFSVMDFE) is also presented, it adopts the non-singleton fuzzy Gaussian kernel function with similar characteristic of pre-filter and is modified with a space transformation based approach. Simulations under nonlinear time variant channels show that ASVM-DFE and ANSFSVM-DFE perform very well on nonlinear equalization and ANSFSVM-DFE acts especially well in resisting abrupt interference.

This paper considers the uniform parallel machine scheduling problem with unequal release dates and delivery times to minimize the maximum completion time. For this NP-hard problem, the largest sum of release date, processing time and delivery time first rule is designed to determine a certain machine for each job, and the largest difference between delivery time and release date first rule is designed to sequence the jobs scheduled on the same machine, and then a novel algorithm for the scheduling problem is built. To evaluate the performance of the proposed algorithm, a lower bound for the problem is proposed. The accuracy of the proposed algorithm is tested based on the data with problem size varying from 200 jobs to 600 jobs. The computational results indicate that the average relative error between the proposed algorithm and the lower bound is only 0.667%, therefore the solutions obtained by the proposed algorithm are very accurate.

According to the aggregation method of experts' evaluation information in group decision-making, the existing methods of determining experts' weights based on cluster analysis take into account the expert's preferences and the consistency of expert's collating vectors, but they lack of the measure of information similarity. So it may occur that although the collating vector is similar to the group consensus, information uncertainty is great of a certain expert. However, it is clustered to a larger group and given a high weight. For this, a new aggregation method based on entropy and cluster analysis in group decision-making process is provided, in which the collating vectors are classified with information similarity coefficient, and the experts' weights are determined according to the result of classification, the entropy of collating vectors and the judgment matrix consistency. Finally, a numerical example shows that the method is feasible and effective.

The problem of global robust asymptotical stability for a class of Takagi-Sugeno fuzzy neural networks (TSFNN) with discontinuous activation functions and time delays is investigated by using Lyapunov stability theory. Based on linear matrix inequalities (LMIs), we originally propose robust fuzzy control to guarantee the global robust asymptotical stability of TSFNNs. Compared with the existing literature, this paper removes the assumptions on the neuron activations such as Lipschitz conditions, bounded, monotonic increasing property or the right-limit value is bigger than the left one at the discontinuous point. Thus, the results are more general and wider. Finally, two numerical examples are given to show the effectiveness of the proposed stability results.

Stability analysis and stabilization for discrete-time singular delay systems are addressed, respectively. Firstly, a sufficient condition for regularity, causality and stability for discrete-time singular delay systems is derived. Then, by applying the skill of matrix theory, the state feedback controller is designed to guarantee the closed-loop discrete-time singular delay systems to be regular, casual and stable. Finally, numerical examples are given to demonstrate the effectiveness of the proposed method.

In order to improve the measurement-precision of the gyro, the gyro experiment is completed based on gyro servo technology. The error sources of gyro servo technology are analyzed in the process of measurement, and the impact of these error sources on measurement is evaluated. To eliminate interference signal existing in the sampled data of the measurement, a modified wavelet threshold filtering method is presented. The results of the simulation and measurement show that the estimation-precision of the proposed method is improvement remarkably compared with the fast Fourier transform method, and the calculation work is reduced compared with the conventional wavelet threshold filtering methods, furthermore, the phenomenon of a common threshold of  "killing" is solved thoroughly.

A robust reliability method for stability analysis and reliability-based stabilization of time-delay dynamic systems with uncertain but bounded parameters is presented by treating the uncertain parameters as interval variables. The performance function used for robust reliability analysis is defined by a delayindependent stability criterion. The design of robust controllers is carried out by solving a reliability-based optimization problem in which the control cost satisfying design requirements is minimized. This kind of treatment makes it possible to achieve a balance between the reliability and control cost in the design of controller when uncertainties must be taken into account. By the method, a robust reliability measure of the degree of stability of a time-delay uncertain system can be provided, and the maximum robustness bounds of uncertain parameters such that the time-delay system to be stable can be obtained. All the procedures are based on the linear matrix inequality approach and therefore can be carried out conveniently. The effectiveness and feasibility of the proposed method are demonstrated with two practical examples. It is shown by numerical simulations and comparison that it is meaningful to take the robust reliability into account in the control design of uncertain systems.

The problem of adaptive stabilization is addressed for a class of uncertain stochastic nonlinear strict-feedback systems with both unknown dead-zone and unknown gain functions. By using the backstepping method and neural network (NN) parameterization, a novel adaptive neural control scheme which contains fewer learning parameters is developed to solve the stabilization problem of such systems. Meanwhile, stability analysis is presented to guarantee that all the error variables are semi-globally uniformly ultimately bounded with desired probability in a compact set. The effectiveness of the proposed design is illustrated by simulation results.

Due to defects of time-difference of arrival localization, which influences by speed differences of various model waveforms and waveform distortion in transmitting process, a neural network technique is introduced to calculate localization of the acoustic emission source. However, in back propagation (BP) neural network, the BP algorithm is a stochastic gradient algorithm virtually, the network may get into local minimum and the result of network training is dissatisfactory. It is a kind of genetic algorithms with the form of quantum chromosomes, the random observation which simulates the quantum collapse can bring diverse individuals, and the evolutionary operators characterized by a quantum mechanism are introduced to speed up convergence and avoid prematurity. Simulation results show that the modeling of neural network based on quantum genetic algorithm has fast convergent and higher localization accuracy, so it has a good application prospect and is worth researching further more.

Strapdown inertial navigation system (SINS) requires an initialization process that establishes the relationship between the body frame and the local geographic reference. This process, called alignment, is generally used to estimate the initial attitude angles. This is possible because an accurate determination of the inertial measurement unit (IMU) motion is available based on the measurement obtained from global position system (GPS). But the update frequency of GPS is much lower than SINS. Due to the non-synchronous data streams from GPS and SINS, the initial attitude angles may not be computed accurately enough. In addition, the estimated initial attitude angles may have relatively large uncertainties that can affect the accuracy of other navigation parameters. This paper presents an effective approach of matching the velocities which are provided by GPS and SINS. In this approach, a digital high-pass filter, which implements a pre-filtering scheme of the measured signal, is used to filter the Schuler cycle of discrete velocity difference between the SINS and GPS. Simulation results show that this approach improves the accuracy greatly and makes the convergence time satisfy the required accuracy.

Because the intrinsic characteristics of mobile ad hoc networks (MANETs) cause several vulnerabilities, anonymous routing protocols attract much more attention in secure mobile ad hoc networks for the purposes of security and privacy concerns. Until recently, lots of anonymous routing protocols have been proposed. However, most of them are single path or use one path at a time, and the multipath schemes can not thwart both the passive attacks and active attacks simultaneously. Thus an anonymous multipath routing protocol based on secret sharing is proposed. The protocol provides identity anonymity, location anonymity, data and traffic anonymity by employing cryptograph technology and secret sharing in MANET communication process. Meanwhile, a hash function is introduced to detect active attacks in the data transmission process. The protocol can effectively thwart various passive attacks and reduce the successful probability of active attacks (such as interception and physical destroy attacks).
Simulation results show that the proposed scheme provides a reasonably good level of network security and performance.

This paper addresses color filter array (CFA) color reproduction problem where the aim is to utilize an image captured by the CFA to produce an image with full color information. First, conventional subband synthesis based color reproduction techniques do not consider the noise during image acquisition and assume that the CFA data are noiseless. To tackle the noisy CFA data, a novel approach is proposed by inserting a subband denoising scheme into the conventional subband synthesis framework. Second, conventional subband synthesis based techniques exploit the decimated wavelet transform that is not shift-invariant and could result in ringing artifacts in the result. To alleviate these artifacts, the directional cycle-spinning (DCS) technique is exploited. Furthermore, a new cycle-spinning pattern is proposed according to the sampling pattern of the Bayer CFA data. Extensive experiments are conducted to demonstrate that the proposed approach outperforms several approaches.

For multi-agent systems based on the local information, the agents automatically converge to a common consensus state and the convergence speed is determined by the algebraic connectivity of the communication network. To study fast consensus seeking problems of multi-agent systems in undirected networks, a consensus protocol is proposed which considers the average information of the agents’ states in a certain time interval, and a consensus convergence criterion for the system is obtained. Based on the frequency-domain analysis and algebra graph theory, it is shown that if the time interval is chosen properly, then requiring the same maximum control effort the proposed protocol reaches consensus faster than the standard consensus protocol. Simulations are provided to demonstrate the effectiveness of these theoretical results.

An improved genetic algorithm (IGA) based on a novel selection strategy to handle nonlinear programming problems is proposed. Each individual in selection process is represented as a three-dimensional feature vector which is composed of objective function value, the degree of constraints violations and the number of constraints violations. It is easy to distinguish excellent individuals from general individuals by using an individuals' feature vector. Additionally, a local search (LS) process is incorporated into selection operation so as to find feasible solutions located in the neighboring areas of some infeasible solutions. The combination of IGA and LS should offer the advantage of both the quality of solutions and diversity of solutions. Experimental results over a set of benchmark problems demonstrate that IGA has better performance than other algorithms.