In MIMO wireless communication systems, when more and more antennas are packed into spatially-limited receive region, the antenna saturation phenomenon will appear. Moreover, the electromagnetic interactions among antennas will also become stronger and stronger and affect the antenna saturation effect considerably. Despite this, few studies consider these two effects jointly. The eects of antenna saturation are investigated under the consideration of mutual coupling, thus a more practical and physically meaningful result can be obtained.

In the wireless guidance system, the signals that receiver received has obvious Doppler shift for the high dynamic characteristic of the carrier. A new solution of carrier frequency tracking loop with frequency modifying system is put forward. The characteristic of cross product auto frequency control and the second order loop filter in this loop are analyzed. The simulation shows that this loop can accomplish frequency tracking well in high dynamic circumstance.

The method of moving target detection based on subimage cancellation for single-antenna airborne SAR is presented. First the subimage is obtained through frequency processing is pointed out. The imaging difference of a stationary objects and moving object in the subimage based on the frequency division is analyzed from the fundamental principle. Then the developed method combines the shear averaging algorithm to focus on the moving target in the subimage, after the clutter suppression and the focusing position in each subimage is obtained. Next the observation model and the relative movement of the moving targets between the subimages estimate the moving targets. The theoretical analysis and simulation results demonstrate that the method is effective and can not only detect the moving targets, but also estimate their motion parameters precisely.

Direct sequence spread spectrum transmission can be realized at low SNR, and has low probability of detection. It is aly problem how to obtain the original users' signal in a non-cooperative context. In practicality, the DS/CDMA sources received are linear convolute mixing. A more complex multichannel blind deconvolution MBD algorithm is required to achieve better source separation. An improved MBD algorithm for separating linear convolved mixtures of signals in CDMA system is proposed. This algorithm is based on minimizing the average squared cross-output-channel-correlation. The mixture coefficients are totally unknown, while some knowledge about temporal model exists. Results show that the proposed algorithm can bring about the exactness and low computational complexity.

Target detection by a noncooperative illuminator is a topic of general interest in the electronic warfare field. First of all, direct-path interference (DPI) suppression which is the technique of bottleneck of moving target detection by a noncooperative frequency modulation(FM) broadcast transmitter is analyzed in this article; Secondly, a space-time-frequency domain synthetic solution to this problem is introduced: Adaptive nulling array processing is considered in the space domain, DPI cancellation based on adaptive fractional delay interpolation (AFDI) technique is used in planned time domain, and long-time coherent integration is utilized in the frequency domain; Finally, an experimental system is planned by considering FM broadcast transmitter as a noncooperative illuminator, Simulation results by real collected data show that the proposed method has a better performance of moving target detection.

A novel space-borne antenna adaptive anti-jamming method based on the genetic algorithm (GA), which is combined with gradient-like reproduction operators is presented, to search for the best weight for pattern synthesis in radio frequency (RF). Combined, the GA's the capability of the whole searching is, but not limited by selection of the initial parameter, with the gradient algorithm's advantage of fast searching. The proposed method requires a smaller sized initial population and lower computational complexity. Therefore, it is exible to implement this method in the real-time systems. By using the proposed algorithm, the designer can efficiently control both main-lobe shaping and side-lobe level. Simulation results based on the spot survey data show that the algorithm proposed is efficient and feasible.

To mitigate the eects of the previous symbol decision errors of a decision-feedback (DF) equalizer on the current decision, a particle filter (PF) based DF equalizer for frequency selective multiple-input-multiple-output (MIMO) channel is proposed. On the basis of the analyses of DF equalization for the MIMO wireless system, it is found that a stochastic interference cancellation (IC) scheme can be employed to prevent the error propagation in a severe space-time interference scenario. This is because the random rather than the deterministic scheme can reduce the probability of an error decision even if an error decision occurs. Besides, the signal-to-interference-plus-noise ratio (SINR) based IC order, which is obtained via pilot, can guarantee the optimality of the cancellation. The bit error rate (BER) performance of the proposed scheme is veried through simulation experiments under different multipath interference environment.

The limited physical size for autonomous underwater vehicles (AUV) or unmanned underwater vehicles (UUV) makes it dicult to acquire enough space gain for localizing long-distance targets. A new technique about long-distance target apperception with passive synthetic aperture array for underwater vehicles is presented. First, a synthetic aperture-processing algorithm based on the FFT transform in the beam space (BSSAP) is introduced. Then, the study on the flank array passive long-distance apperception techniques in the frequency scope of 11~18 kHz is implemented from the view of improving array gains, detection probability and augmenting detected range under a certain sea environment. The results show that the BSSAP algorithm can extend the aperture eectively and improve detection probability. Because of the augment of the transmission loss, the detected range has the trend of decline with the increase of frequency under the same target source level. The synthesized array could improve the space gain by nearly 7 dB and SNR is increased by about 5 dB. The detected range is enhanced to nearly 2 km under the condition of 108{118 dB of the target source level for AUV system in measurement interval of nearly 1 s.

When particle filter is applied in radar target tracking, the accuracy of the initial particles greatly effects the results of filtering. For acquiring more accurate initial particles, a new method called \competition strategy algorithm" is presented. In this method, initial measurements give birth to several particle groups around them, regularly. Each of the groups is tested several times, separately, in the beginning periods, and the group that has the most number of efficient particles is selected as the initial particles. For this method, sample initial particles selected are on the basis of several measurements instead of only one first measurement, which surely improves the accuracy of initial particles. The method sacrifices initialization time and computation cost for accuracy of initial particles. Results of simulation show that it greatly improves the accuracy of initial particles, which makes the effect of filtering much better.

Multisensor data fusion has played a significant role in diverse areas ranging from local robot guidance to global military theatre defense etc. Various multisensor data fusion methods have been extensively investigated by researchers, of which Klaman filtering is one of the most important. Kalman filtering is the best-known recursive least mean-square algorithm to optimally estimate the unknown states of a dynamic system, which has found widespread application in many areas. The scope of the work is restricted to investigate the various data fusion and track fusion techniques based on the Kalman Filter methods, then a new method of state fusion is proposed. Finally the simulation results demonstrate the effectiveness of the introduced method.

A marginalized particle filtering (MPF) approach is proposed for target tracking under the background of passive measurement. Essentially, the MPF is a combination of particle filtering technique and Kalman filter. By making full use of marginalization, the distributions of the tractable linear part of the total state variables are updated analytically using Kalman filter, and only the lower-dimensional nonlinear state variable needs to be dealt with using particle filter. Simulation studies are performed on an illustrative example, and the results show that the MPF method leads to a significant reduction of the tracking errors when compared with the direct particle implementation. Real data test results also validate the effectiveness of the presented method.

Using dual function one direction S-rough sets, this article gives the f-law, the F-law, law distance and the concept of system law collided by the F-law. The characteristics presented by the system law collided by the F-law, the recognition of these characteristics and recognition criterion are also proposed. The dual function one direction S-rough sets is one of the basic forms of function S-rough sets. Its basic theory and application in the study of system law collision are reviewed.

The weighted arithmetical mean complex judgement matrix (WAMCJM) is the most common method for aggregating group opinions, but it has a shortcoming, namely theWAMCJM of the perfectly consistent judgement matrices given by experts canot guarantee its perfect consistency. An upper bound of the WAMCJM's consistency is presented. Simultaneously, a compatibility index of judging the aggregating extent of group opinions is also introduced. The WAMCJM is of acceptable consistency and is proved provided the compatibilities of all judgement matrices given by experts are smaller than the threshold value of acceptable consistency. These conclusions are important to group decision making.

To improve the performance of the supply chain with one supplier and multiple retailers under deterministic price-sensitive customer demand, an optimal strategy is proposed based on knowledge discovery. First the decentralized system in which the supplier and the retailers are independent, profit-maximizing participants with the supplier acting as a Stackelberg game leader is studied. Numerical examples illustrate the importance of the coordination. The conventional quantity discount mechanism needs to be modified to coordinate the supply chain, so a revenue-sharing contract is proposed to coordinate such supply chain. Lastly, a special decision under certain demand rates is studied. The pricing and replenishment policies can be decided sequentially, which yields much less loss comparing with optimal decision when the demand rates are sufficiently large.

After reviewing current researches on early warning, it is found that "bad" data of some systems is not easy to obtain, which makes methods proposed by these researches unsuitable for monitored systems. An interactive early warning technique based on SVDD (support vector data description) is proposed to adopt "good" data as samples to overcome the diculty in obtaining the \bad" data. The process consists of two parts: (1) A hypersphere is fitted on \good" data using SVDD. If the data object are outside the hypersphere, it would be taken as "suspicious"; (2) A group of experts would decide whether the suspicious data is "bad" or "good", early warning messages would be issued according to the decisions. And the detailed process of implementation is proposed. At last, an experiment based on data of a macroeconomic system is conducted to verify the proposed technique.

With the fast growth of Chinese economic, more and more capital will be invested in environmental projects. How to select the environmental investment projects (alternatives) for obtaining the best environmental quality and economic benefits is an important problem for the decision makers. The purpose of this paper is to develop a decision-making model to rank a finite number of alternatives with several and sometimes conflicting criteria. A model for ranking the projects of municipal sewage treatment plants is proposed by using exports' information and the data of the real projects. And, the ranking result is given based on the PROMETHEE method. Furthermore, by means of the concept of the weight stability intervals (WSI), the sensitivity of the ranking results to the size of criteria values and the change of weights value of criteria are discussed. The result shows that some criteria, such as "proportion of benefit to project cost", will influence the ranking result of alternatives very strong while others not. The in influence are not only from the value of criterion but also from the changing the weight of criterion. So, some criteria such as "proportion of benefit to project cost" are key critera for ranking the projects. Decision makers must be cautious to them.

As to oppositional, multi-objective and hierarchical characteristic of air formation to ground attack-defends campaign, and using dynamic space state model of military campaign, this article establishes a principal and subordinate hierarchical interactive decision-making way, the Nash-Stackelberg-Nash model, to solve the problems in military operation, and find out the associated best strategy in hierarchical dynamic decision-making. The simulating result indicate that when applying the model to air formation to ground attack-defends decision-making system, it can solve the problems of two hierarchies' dynamic oppositional decision-making favorably, and reach preferable effect in battle. It proves that the model can provide an effective way for analyzing a battle.

Abundant test data are required in assessment of weapon performance. When weapon test data are insufficient, Bayesian analyses in small sample circumstance should be considered and the test data should be provided by simulations. The several Bayesian approaches are discussed and some limitations are founded. An improvement is put forward after limitations of Bayesian approaches available are analyzed and the improved approach is applied to assessment of some new weapon performance.

A multi-stage influence diagram is used to model the pilot's sequential decision making in one on one air combat. The model based on the multi-stage influence diagram graphically describes the elements of decision process, and contains a point-mass model for the dynamics of an aircraft and takes into account the decision maker's preferences under uncertain conditions. Considering an active opponent, the opponent's maneuvers can be modeled stochastically. The solution of multistage influence diagram can be obtained by converting the multistage influence diagram into a two-level optimization problem. The simulation results show the model is effective.

A modified bottleneck-based (MB) heuristic for large-scale job-shop scheduling problems with a well-defined bottleneck is suggested, which is simpler but more tailored than the shifting bottleneck (SB) procedure. In this algorithm, the bottleneck is first scheduled optimally while the non-bottleneck machines are subordinated around the solutions of the bottleneck schedule by some effective dispatching rules. Computational results indicate that the MB heuristic can achieve a better tradeoff between solution quality and computational time compared to SB procedure for medium-size problems. Furthermore, it can obtain a good solution in a short time for large-scale job- shop scheduling problems.

Two complex properties, varying time-delay and block-oriented nonlinearity, are very common in chemical engineering processes and not easy to be controlled by routine control methods. Aimed at these two complex properties, a novel adaptive control algorithm the basis of nonlinear OFS (orthonormal functional series) model is proposed. First, the hybrid model which combines OFS and Volterra series is introduced. Then, a stable state feedback strategy is used to construct a nonlinear adaptive control algorithm that can guarantee the closed-loop stability and can track the set point curve without steady-state errors. Finally, control simulations and experiments on a nonlinear process with varying time-delay are presented. A number of experimental results validate the efficiency and superiority of this algorithm.

The optimal tracking performance for integrator and dead time plant in the case where plant uncertainty and control energy constraints are to be considered jointly is inrestigated. Firstly, an average cost function of the tracking error and the plant input energy over a class of stochastic model errors are defined. Then, we obtain an internal model controller design method that minimizes the average performance and further studies optimal tracking performance for integrator and dead time plant in the simultaneous presence of plant uncertainty and control energy constraint. The results can be used to evaluate optimal tracking performance and control energy in practical designs.

The H∞ output feedback control problem for uncertain discrete-time switched systems is reasearched. A new characterization of stability and H∞ performance for the switched system under arbitrary switching is obtained by using switched Lyapunov function. Then, based on the characterization, a linear matrix inequality (LMI) approach is developed to design a switched output feedback controller which guarantees the stability and H∞ performance of the closed-loop system. A numerical example is presented to demonstrate the application of the proposed method.

The Newton-Like algorithm with price estimation error in optimization flow control in network is analyzed. The estimation error is treated as inexactness of the gradient and the inexact descent direction is analyzed. Based on the optimization theory, a sufficient condition for convergence of this algorithm with bounded price estimation error is obtained. Furthermore, even when this sufficient condition doesn't hold, this algorithm can also converge, provided a modified step size, and an attraction region is obtained. Based on Lasalle's invariance principle applied to a suitable Lyapunov function, the dynamic system described by this algorithm is proved to be global stability if the error is zero. And the Newton-Like algorithm with bounded price estimation error is also globally stable if the error satisfies the sufficient condition for convergence. All trajectories ultimately converge to the equilibrium point.

A method is developed by which control Lyapunov functions of a class of nonlinear systems can be constructed systematically. Based on the control Lyapunov function, a feedback control is obtained to stabilize the closed-loop system. In addition, this method is applied to stabilize the Benchmark system. A simulation shows the effectiveness of the method.

An adaptive ant colony algorithm is proposed based on dynamically adjusting the strategy of updating trail information. The algorithm can keep good balance between accelerating convergence and averting precocity and stagnation. The results of function optimization show that the algorithm has good searching ability and high convergence speed. The algorithm is employed to design a neuro-fuzzy controller for real-time control of an inverted pendulum. In order to avoid the combinatorial explosion of fuzzy rules due to multivariable inputs, a state variable synthesis scheme is employed to reduce the number of fuzzy rules greatly. The simulation results show that the designed controller can control the inverted pendulum successfully.

Considering chaotic time series multi-step prediction, multi-step direct prediction model based on partial least squares (PLS) is proposed in this article, where PLS, the method for predicting a set of dependent variables forming a large set of predictors, is used to model the dynamic evolution between the space points and the corresponding future points. The model can eliminate error accumulation with the common single-step local model algorithm, and refrain from the high multi-collinearity problem in the reconstructed state space with the increase of embedding dimension. Simulation predictions are done on the Mackey-Glass chaotic time series with the model. The satisfying prediction accuracy is obtained and the model efficiency veried. In the experiments, the number of extracted components in PLS is set with cross-validation procedure.

A robust model predictive control algorithm for discrete linear systems with both state and input delays subjected to constrained input control is presented, where the polytopic uncertainties exist in both state matrices and input matrices. The algorithm optimizes an upper bound with respect to a state feedback control law. The feedback control law is presented based on the construction of a parameter-dependent Lyapunov function. The above optimization problem can be formulated as a LMI-based optimization. The feasibility of the optimization problem guarantees that the algorithm is robustly stable. The simulation results verify the eectiveness of the proposed algorithm.

Energy saving is the most important issue in research and development for wireless sensor networks. A power control mechanism can reduce the power consumption of the whole network. Because the character of wireless sensor networks is restrictive energy, this paper proposes a distributed power control algorithm based on game theory for wireless sensor networks which objects of which are reducing power consumption and decreasing overhead and increasing network lifetime. The game theory and OPNET simulation shows that the power control algorithm converges to a Nash Equilibrium when decisions are updated according to a better response dynamic.

In order to provide larger capacity of the hidden secret data while maintaining a good visual quality of stego-image, in accordance with the visual property that human eyes are less sensitive to strong texture, a novel steganographic method based on wavelet and modulus function is presented. First, an image is divided into blocks of prescribed size, and every block is decomposed into one-level wavelet. Then, the capacity of the hidden secret data is decided with the number of wavelet coefficients of larger magnitude. Finally, secret information is embedded by steganography based on modulus function. From the experimental results, the proposed method hides much more information and maintains a good visual quality of stego-image. Besides, the embedded data can be extracted from the stego-image without referencing the original image.

To guarantee the real-time transmission of a video stream, based on the stochastic optimal control method, a frame layer adaptive rate control algorithm for the wireless transcoder is proposed, which is capable of dynamically determining the transcoder's objective bit rate, according to the bandwidth variation of the wireless channel and the buer occupancy. Then the transient performance, steady performance, and computational complexity of the algorithm are analyzed. Finally, the experiment results demonstrate that the algorithm can improve the synthetic performance of rate control through the compromise between the end-to-end delay and the playout quality.

Ontology heterogeneity is the primary obstacle for interoperation of ontologies. Ontology mapping is the best way to solve this problem. The key of ontology mapping is the similarity computation. At present, the method of similarity computation is imperfect. And the computation quantity is high. To solve these problems, an ontology-mapping framework with a kind of hybrid architecture is put forward, with an improvement in the method of similarity computation. Different areas have different local ontologies. Two ontologies are taken as examples, to explain the specific mapping framework and improved method of similarity computation. These two ontologies are about classes and teachers in a university. The experimental results show that using this framework and improved method can increase the accuracy of computation to a certain extent. Otherwise, the quantity of computation can be decreased.

A new fast and accurate method for estimating the frequency of a complex sinusoid in complex white Gaussian environments is proposed. The new estimator comprises of applications of low-pass filtering, decimation, and frequency estimation by linear prediction. It is computationally efficient yet obtains the Cramer-Rao bound at moderate signal-to-noise ratios. And it is well suited for real time applications requiring precise frequency estimation. Simulation results are included to demonstrate the performance of the proposed method.

A new multi-modal optimization algorithm called the self-organizing worm algorithm (SOWA) is presented for optimization of multi-modal functions. The main idea of this algorithm can be described as follows: disperse some worms equably in the domain; the worms exchange the information each other and creep toward the nearest high point; at last they will stop on the nearest high point. All peaks of multi-modal function can be found rapidly through studying and chasing among the worms. In contrast with the classical multi-modal optimization algorithms, SOWA is provided with a simple calculation, strong convergence, high precision, and does not need any prior knowledge. Several simulation experiments for SOWA are performed, and the complexity of SOWA is analyzed amply. The results show that SOWA is very effective in optimization of multi-modal functions.

An adaptive immune- genetic algorithm (AIGA) is proposed to avoid premature convergence and guarantee the diversity of the population. Rapid immune response (secondary response), adaptive mutation and density operators in the AIGA are emphatically designed to improve the searching ability, greatly increase the converging speed, and decrease locating the local maxima due to the premature convergence. The simulation results obtained from the global optimization to four multivariable and multi-extreme functions show that AIGA converges rapidly, guarantees the diversity, stability and good searching ability.

With the capability of the virtual machine monitor, a novel approach for logging system activities is designed. In the design, the guest operating system runs on the virtual machine monitor as non-privileged mode. The redirecting and monitoring modules are added into the virtual machine monitor. When a guest application is calling a system call, it is trapped and redirected from the least privileged level into the virtual machine monitor running in the most privileged level. After logging is finished, it returns to the guest operating system running in the more privileged level and starts the system call. Compared with the traditional methods for logging system activities, the novel method makes it more difficult to destroy or tamper the logs. The preliminary evaluation shows that the prototype is simple and efficient.