According to low earth orbit (LEO) satellite systems with users of different levels, a dynamic channel reservation scheme based on priorities is proposed. Dynamic calculation of the thresholds for reserved channels is the key of this strategy. In order to obtain the optimal thresholds, the traffic is predicted based on the high-speed deterministic movement property of LEO satellites firstly. Then, a channel allocation model based on Markov is established. Finally, the solution of the model is obtained based on the genetic algorithm. Without user location, this strategy effectively reduces handover failures and improves channel utilization by adjusting dynamically the thresholds according to traffic conditions. The simulation results show that the system’s overall quality of service can be improved by this strategy.

Adaptive antenna arrays have been used to mitigate the interference on global navigation satellite system (GNSS) receivers. The performance of interference mitigation depends on the beamforming algorithms adopted by the antenna array. However, the adaptive beamforming will change the array pattern in realtime,
which has the potential to introduce phase center biases into the antenna array. For precise applications, these phase biases must be mitigated or compensated because they will bring errors in code phase and carrier phase measurements. A novel adaptive beamforming algorithm is proposed firstly, then the phase bias
induced by the proposed algorithm is estimated, and finally a compensation strategy is addressed. Simulations demonstrate that the proposed beamforming algorithm suppresses effectively the strong interference and improves significantly the capturing performance of GNSS signals. Simultaneously, the bias compensation method avoids the loss of the carrier phase lock and reduces the phase measurement errors for GNSS receivers.

A novel dual-band antenna is proposed for mitigating the multi-path interference in the global navigation satellite system (GNSS) applications. The radiation patches consist of a shortedannular-ring reduced-surface-wave (SAR-RSW) element and an inverted-shorted-annular-ring reduced-surface-wave (ISAR-RSW)element. One key feature of the design is the proximity-coupled probe feeds to increase impedance bandwidth. The other is the defected ground structure band rejection filters to suppress the interaction effect between the SAR-RSW and the ISAR-RSW elements. In addition, trans-directional couplers are used to obtain tight coupling. Measurement results indicate that the antenna has a larger than 10 dB return loss bandwidth and a less than 3 dB axial-ratio (AR) bandwidth in the range of (1.164 – 1.255) GHz and (1.552 – 1.610) GHz. The gain of the passive antenna in the whole operating band is more than 7 dBi.

Nonparametric time-of-arrival (TOA) estimators for impulse radio ultra-wideband (IR-UWB) signals are proposed. Nonparametric detection is obviously useful in situations where detailed information about the statistics of the noise is unavailable or not accurate. Such TOA estimators are obtained based on conditional
statistical tests with only a symmetry distribution assumption on the noise probability density function. The nonparametric estimators are attractive choices for low-resolution IR-UWB digital receivers which can be implemented by fast comparators or high sampling rate low resolution analog-to-digital converters (ADCs),
in place of high sampling rate high resolution ADCs which may not be available in practice. Simulation results demonstrate that nonparametric TOA estimators provide more effective and robust performance than typical energy detection (ED) base estimators.

An efficient hybrid time reversal (TR) imaging method based on signal subspace and noise subspace is proposed for electromagnetic superresolution detecting and imaging. First, the locations of targets are estimated by the transmitting-mode decomposition of the TR operator (DORT) method employing the signal subspace. Then, the TR multiple signal classification (TR-MUSIC) method employing the noise subspace is used in the estimated target area to get the superresolution imaging of targets. Two examples with homogeneous and inhomogeneous background mediums are considered, respectively. The results show that the proposed hybrid method has advantages in CPU time and memory cost because of the combination of rough and fine imaging.

The problem of adaptive detection in the situation of signal mismatch is considered; that is, the actual signal steering vector is not aligned with the nominal one. Two novel tunable detectors are proposed. They can control the degree to which the mismatched signals are rejected. Remarkably, it is found that they both cover existing famous detectors as their special cases. More importantly, they possess the constant false alarm rate (CFAR) property and achieve enhanced mismatched signal rejection or improved robustness than their natural competitors. Besides, they can provide slightly better matched signals detection performance than the existing detectors.

The nonuniform L-shaped spatially spread loop and dipole (SSLD) array whose inter-element spacing is greater than half a wavelength is studied. A joint parameter estimation algorithm of direction of arrival (DOA), frequency and polarization is presented for plane-wave signals. The direct sampling and the corresponding delayed sampling data are used to construct the data correlation matrix. On the basis of the subspace theory and the least square method, the frequency and the steering vector of the whole array are obtained. According to the relationship of the array manifold vector between electric dipoles and magnetic loops, the polarization parameters are given. The unambiguous phase estimates are acquired by applying virtual baseline array transformation to the spatial steering vectors, and they are used as coarse references to disambiguate the cyclic phase ambiguities in phase differences between two adjacent array elements on the array, then the high accuracy DOA estimates are obtained. Closed-form solutions for each parameter are obtained. This method has advantages of lower calculation complexity and no parameter matching. The experiment results verify the effectiveness and feasibility of the presented algorithm.

The inherent mathematic principle of active jamming against the wideband linear frequency modulated (LFM) radar is investigated. According to different generation strategies, the active jamming methods are reclassified into three groups, i.e., non-coherent jamming (NCJ), convolution jamming (CJ) and multiplying jamming (MJ). Based on the classification, the mathematic principles of different active jamming groups are put forward, which describe the relationships between the modulated signals and the jamming results. The advantages and disadvantages of different groups are further analyzed, which provides a new perspective for the study of jamming/anti-jamming methods and a potential for engineers to integrate similar jamming methods into one jammer platform. The analyses and simulation results of some typical active jamming methods prove the validity of the proposed mathematics principle.

Traditional monopulse radar cannot resolve two targets present in one range and Doppler cell by means of the monopulse ratio. A novel algorithm is proposed to estimate the directions of two steady targets with two pulses. The algorithm has a closedform expression and its variance is derived at high signal-to-noise ratios (SNRs). Furthermore, the pulse pair selection criterion and the estimation method with multiple pulses are given. Finally, some numerical results are shown to validate the proposed algorithm and the effect of slight target fluctuations is tested.

Segmentation is the key step in auto-interpretation of high-resolution spaceborne synthetic aperture radar (SAR) images. A novel method is proposed based on integrating the geometric active contour (GAC) and the support vector machine (SVM) models. First, the images are segmented by using SVM and textural statistics. A likelihood measurement for every pixel is derived by using the initial segmentation. The Chan-Vese model then is modified by adding two items: the likelihood and the distance between the initial segmentation and the evolving contour. Experimental results using real SAR images demonstrate the good performance of the proposed method compared to several classic GAC models.

This paper develops a temporal decorrelation model for the bistatic synthetic aperture radar (BSAR) interferometry. The temporal baseline is one of the important decorrelation sources for the repeat-pass synthetic aperture radar (SAR) interferometry. The study of temporal decorrelation is challenging, especially for the bistatic configuration, since temporal decorrelation is related to the data acquisition geometry. To develop an appropriate theoretical model for BSAR interferometry, the existing models for monostatic SAR cases are extended, and the general BSAR geometry configuration is involved in the derivation. Therefore, the developed temporal decorrelation model can be seen as a general model. The validity of the theoretical model is supported by Monte Carlo simulations. Furthermore, the impacts of the system parameters and BSAR geometry configurations on the temporal decorrelation model are discussed briefly.

Level of repair analysis (LORA) is an important method of maintenance decision for establishing systems of operation and maintenance in the equipment development period. Currently, the research on equipment of repair level focuses on economic analysis models which are used to optimize costs and rarely considers the maintenance time required by the implementation of the maintenance program. In fact, as to the system requiring high mission complete success, the maintenance time is an important factor which has a great influence on the availability of equipment systems. Considering the relationship between the maintenance time and the spares stocks level, it is obvious that there are contradictions between the maintenance time and the cost. In order to balance these two factors, it is necessary to build an optimization LORA model. To this end, the maintenance time representing performance characteristic is introduced, and on the basis of spares stocks which is traditionally regarded as a decision variable, a decision variable of repair level is added, and a multi-echelon multiindenture (MEMI) optimization LORA model is built which takes the best cost-effectiveness ratio as the criterion, the expected number of backorder (EBO) as the objective function and the cost as the constraint. Besides, the paper designs a convex programming algorithm of multi-variable for the optimization model, provides solutions to the non-convex objective function and methods for improving the efficiency of the algorithm. The method provided in this paper is proved to be credible and effective according to the numerical example and the simulation result.

This paper aims to study a new grey prediction approach and its solution for forecasting the main system variable whose accurate value could not be collected while the potential value set could be defined. Based on the traditional nonhomogenous discrete grey forecasting model (NDGM), the interval grey number and its algebra operations are redefined and combined with the NDGM model to construct a new interval grey number sequence prediction approach. The solving principle of the model is analyzed, the new accuracy evaluation indices, i.e. mean absolute percentage error of mean value sequence (MAPEM) and mean percent of interval sequence simulating value set covered (MPSVSC), are defined and, the procedure of the interval grey number sequence based the NDGM (IG-NDGM) is given out. Finally, a numerical case is used to test the modelling accuracy of the proposed model. Results show that the proposed approach could solve the interval grey number sequence prediction problem and it is much better than the traditional DGM(1,1) model and GM(1,1) model.

The morbidity problem of the GM(1,1) power model in parameter identification is discussed by using multiple and rotation transformation of vectors. Firstly we consider the morbidity problem of the special matrix and prove that the condition number of the coefficient matrix is determined by the ratio of lengths and the included angle of the column vector, which could be adjusted by multiple and rotation transformation to turn the matrix to a well-conditioned one. Then partition the corresponding matrix of the GM(1,1) power model in accordance with the column vector and regulate the matrix to a well-conditioned one by multiple and rotation transformation of vectors, which completely solve the instability problem of the GM(1,1) power model. Numerical results show that vector transformation is a new method in studying the stability problem of the GM(1,1) power model.

In order to deeply research the structure discrepancy and modeling mechanism among different grey prediction models, the equivalence and unbiasedness of grey prediction models are analyzed and verified. The results show that all the grey prediction models that are strictly derived from x(0)(k) + az(1)(k) = b have the identical model structure and simulation precision. Moreover, the unbiased simulation for the homogeneous exponential sequence can be accomplished. However, the models derived from dx(1)/dt + ax(1) = b are only close to those derived from x(0)(k)+az(1)(k) = b provided that |a| has to satisfy |a| < 0.1; neither could the unbiased simulation for the homogeneous exponential sequence be achieved. The above conclusions are proved and verified through some theorems and examples.

A synthesis method for global stability and performance of input constrained linear systems, which uses a linear outputfeedback controller and a static anti-windup compensator is investigated. Different from the traditional two-step anti-windup design procedure, the proposed method synthesizes all controller parameters simultaneously. Sufficient conditions for global stability and minimizing the induced L2 gain are formulated and solved as a linear matrix inequalities (LMIs) optimization problem, which also provides an opportunity to search for a better performance tradeoff between the linear controller and the anti-windup compensator.
The well-posedness of the close-loop system is also guaranteed. Simulation results show the effectiveness of the proposed method.

Feature aided design of estimators and guidance laws can significantly improve the interception performance of the terminal guidance system. The achieved enhancement can be effectively assessed by observability analysis methods. This paper first analyzes and discusses the existing assessment methods in a typical endgame scenario with target orientation observations. To get over their deficiencies, a novel singular value decomposition (SVD) method is proposed. Employing both theoretical analysis and numerical simulation, the proposed method can represent the degree of state observability which is enhanced by integrating target features more completely and quantitatively.

Using semi-tensor product of matrices, the controllability and stabilizability of finite automata are investigated. By expressing the states, inputs, and outputs in vector forms, the transition and output functions are represented in matrix forms. Based on this algebraic description, a necessary and sufficient condition is proposed for checking whether a state is controllable to another one. By this condition, an algorithm is established to find all the control sequences of an arbitrary length. Moreover, the stabilizability of finite automata is considered, and a necessary and sufficient condition is presented to examine whether some states can be stabilized. Finally, the study of illustrative examples verifies the correctness of the presented results/algorithms.

The performance of the model algorithm control method is partially based on the accuracy of the system’s model. It is difficult to obtain a good model of a nonlinear system, especially when the nonlinearity is high. Neural networks have the ability to “learn” the characteristics of a system through nonlinear mapping to represent nonlinear functions as well as their inverse functions. This paper presents a model algorithm control method using neural networks for nonlinear time delay systems. Two neural networks are used in the control scheme. One neural network is trained as the model of the nonlinear time delay system, and the other one produces the control inputs. The neural networks are combined with the model algorithm control method to control the nonlinear time delay systems. Three examples are used to illustrate the proposed control method. The simulation results show that the proposed control method has a good control performance for nonlinear time delay systems.

Large latency of applications will bring revenue loss to cloud infrastructure providers in the cloud data center. The existing controllers of software-defined networking architecture can fetch and process traffic information in the network. Therefore, the controllers can only optimize the network latency of applications. However, the serving latency of applications is also an important factor in delivered user-experience for arrival requests. Unintelligent request routing will cause large serving latency if arrival requests are allocated to overloaded virtual machines. To deal with the request routing problem, this paper proposes the workload-aware software-defined networking controller architecture. Then, request routing algorithms are proposed to minimize the total round trip time for every type of request by considering the congestion in the network and the workload in virtual machines (VMs). This paper finally provides the evaluation of the proposed algorithms in a simulated prototype. The simulation results show that the proposed methodology is efficient compared with the existing approaches.

To get the high compression ratio as well as the high-quality reconstructed image, an effective image compression scheme named irregular segmentation region coding based on spiking cortical model (ISRCS) is presented. This scheme is region-based and mainly focuses on two issues. Firstly, an appropriate segmentation algorithm is developed to partition an image into some irregular regions and tidy contours, where the crucial regions corresponding to objects are retained and a lot of tiny parts are eliminated. The irregular regions and contours are coded using different methods respectively in the next step. The other issue is the coding method of contours where an efficient and novel chain code is employed. This scheme tries to find a compromise between the quality of reconstructed images and the compression ratio. Some principles and experiments are conducted and the results show its higher performance compared with other compression technologies, in terms of higher quality of reconstructed images, higher compression ratio and less time consuming.

Model validation and updating is critical to model credibility growth. In order to assess model credibility quantitatively and locate model error precisely, a new dynamic validation method based on extremum field mean mode decomposition (EMMD) and the Prony method is proposed in this paper. Firstly, complex dynamic
responses from models and real systems are processed into stationary components by EMMD. These components always have definite physical meanings which can be the evidence about rough model error location. Secondly, the Prony method is applied to identify the features of each EMMD component. Amplitude similarity,
frequency similarity, damping similarity and phase similarity are defined to describe the similarity of dynamic responses. Then quantitative validation metrics are obtained based on the improved entropy weight and energy proportion. Precise model error location is realized based on the physical meanings of these features. The application of this method in aircraft controller design provides evidence about its feasibility and usability.

As a new-style stochastic algorithm, the electromagnetism-like mechanism (EM) method gains more and more attention from many researchers in recent years. A novel model based on EM (NMEM) for multiobjective optimization problems is proposed, which regards the charge of all particles as the constraints in the current population and the measure of the uniformity of non-dominated solutions as the objective function. The charge of the particle is evaluated based on the dominated concept, and its magnitude determines the direction of a force between two particles. Numerical studies are carried out on six complex test functions and the experimental results demonstrate that the proposed NMEM algorithm is a very robust method for solving the multiobjective optimization problems.

Testing-effort (TE) and imperfect debugging (ID) in the reliability modeling process may further improve the fitting and prediction results of software reliability growth models (SRGMs). For describing the S-shaped varying trend of TE increasing rate more accurately, first, two S-shaped testing-effort functions (TEFs), i.e., delayed S-shaped TEF (DS-TEF) and inflected S-shaped TEF (IS-TEF), are proposed. Then these two TEFs are incorporated into various types (exponential-type, delayed S-shaped and inflected S-shaped) of non-homogeneous Poisson process (NHPP) SRGMs with two forms of ID respectively for obtaining a series of new NHPP SRGMs which consider S-shaped TEFs as well as ID. Finally these new SRGMs and several comparison NHPP SRGMs are applied into four real failure data-sets respectively for investigating the fitting and prediction power of these new SRGMs. The experimental results show that: (i) the proposed IS-TEF is more suitable and flexible for describing the consumption of TE than the previous TEFs; (ii) incorporating TEFs into the inflected S-shaped NHPP SRGM may be more effective and appropriate compared with the exponential-type and the delayed S-shaped NHPP SRGMs; (iii) the inflected S-shaped NHPP SRGM considering both IS-TEF and ID yields the most accurate fitting and prediction results than the other comparison NHPP SRGMs.

For high reliability and long life systems, system pass/fail data are often rare. Integrating lower-level data, such as data drawn from the subsystem or component pass/fail testing, the Bayesian analysis can improve the precision of the system reliability assessment. If the multi-level pass/fail data are overlapping, one challenging problem for the Bayesian analysis is to develop a likelihood function. Since the computation burden of the existing methods makes them infeasible for multi-component systems, this paper proposes an improved Bayesian approach for the system reliability assessment in light of overlapping data. This approach includes three steps: fristly searching for feasible paths based on the binary decision diagram, then screening feasible points based on space partition and constraint decomposition, and finally simplifying the likelihood function. An example of a satellite rolling control system demonstrates the feasibility and the efficiency of the proposed approach.