The aim of this paper is to discuss the approximate rea- soning problems with interval-valued fuzzy environments based on the fully implicational idea.First,this paper constructs a class of interval-valued fuzzy implications by means of a type of impli- cations and a parameter on the unit interval,then uses them to establish fully implicational reasoning methods for interval-valued fuzzy modus ponens(IFMP)and interval-valued fuzzy modus tol- lens(IFMT)problems.At the same time the reversibility properties of these methods are analyzed and the reversible conditions are given.It is shown that the existing unified forms ofα-triple I(the abbreviation of triple implications)methods for FMP and FMT can be seen as the particular cases of our methods for IFMP and IFMT.
According to the characteristic of cruise missiles, navigation point setting is simplified, and the principle of route planning for saturation attack and a concept of reference route are put forward. With the help of the shortest-tangent idea in route-planning and the algorithm of back reasoning from targets, a reference route algorithm is built on the shortest range and threat avoidance. Then a route-flight-time algorithm is built on navigation points. Based on the conditions of multi-direction saturation attack, a route planning algorithm of multi-direction saturation attack is built on reference route, route-flight-time, and impact azimuth. Simulation results show that the algorithm can realize missiles fired in a salvo launch reaching the target simultaneously from different directions while avoiding threat.
The problem of identification of friend-or-foe aircraft in the actual application condition is addressed. A hybrid algorithm combining fuzzy neutral network with probability factor (FNNP), multi-level fuzzy comprehensive evaluation and the Dempster- Shafer (D-S) theory is proposed. This hybrid algorithm constructs a complete process from generating the fuzzy database to the final identification, realizes the identification of friend-or-foe automatically if the training samples or expert’s experience can be obtained, and reduces the effect of uncertainties in the process of identification. At the same time, the whole algorithm can update the identification result with the augment of observations. The performance of the proposed algorithm is assessed by simulations. Results show that the proposed algorithm can successfully deduce the aircraft’s identity even if the observations have measurement errors.
Structure learning of Bayesian networks is a wellresearched but computationally hard task. For learning Bayesian networks, this paper proposes an improved algorithm based on unconstrained optimization and ant colony optimization (U-ACO-B) to solve the drawbacks of the ant colony optimization (ACO-B). In this algorithm, firstly, an unconstrained optimization problem is solved to obtain an undirected skeleton, and then the ACO algorithm is used to orientate the edges, thus returning the final structure. In the experimental part of the paper, we compare the performance of the proposed algorithm with ACO-B algorithm. The experimental results show that our method is effective and greatly enhance convergence speed than ACO-B algorithm.
The robust reliable H∞ control problem for discrete-time Markovian jump systems with actuator failures is studied. A more practical model of actuator failures than outage is considered. Based on the state feedback method, the resulting closed-loop systems are reliable in that they remain robust stochastically stable and satisfy a certain level of H∞ disturbance attenuation not only when all actuators are operational, but also in case of some actuator failures. The solvability condition of controllers can be equivalent to a feasibility problem of coupled linear matrix inequalities (LMIs). A numerical example is also given to illustrate the design procedures and their effectiveness.
The mixture of factor analyzers(MFA)can accurately describe high resolution range profile(HRRP)statistical charac- teristics.But how to determine the proper number of the models is a problem.This paper develops a variational Bayesian mixture of factor analyzers(VBMFA)model.This procedure can obtain a lower bound on the Bayesian integral using the Jensen’s inequality. An analytical solution of the Bayesian integral could be obtained by a hypothesis that latent variables in the model are indepen- dent.During computing the parameters of the model,birth-death moves are utilized to determine the optimal number of model au- tomatically.Experimental results for measured data show that the VBMFA method has better recognition performance than FA and MFA method.
As for the drop of particle diversity and the slow convergent speed of particle in the late evolution period when particle swarm optimization (PSO) is applied to solve high-dimensional multi-modal functions, a hybrid optimization algorithm based on the cat mapping, the cloud model and PSO is proposed. While the PSO algorithm evolves a certain of generations, this algorithm applies the cat mapping to implement global disturbance of the poorer individuals, and employs the cloud model to execute local search of the better individuals; accordingly, the obtained best individuals form a new swarm. For this new swarm, the evolution operation is maintained with the PSO algorithm, using the parameter of pop distr to balance the global and local search capacity of the algorithm, as well as, adopting the parameter of mix gen to control mixing times of the algorithm. The comparative analysis is carried out on the basis of 4 functions and other algorithms. It indicates that this algorithm shows faster convergent speed and better solving precision for solving functions particularly those high-dimensional multi-modal functions. Finally, the suggested values are proposed for parameters pop distr and mix gen applied to different dimension functions via the comparative analysis of parameters.
A new method for array calibration of array gain and phase uncertainties, which severely degrade the performance of spatial spectrum estimation, is presented. The method is based on the idea of the instrumental sensors method (ISM), two well-calibrated sensors are added into the original array. By applying the principle of estimation of signal parameters via rotational invariance techniques (ESPRIT), the direction-of-arrivals (DOAs)and uncertainties can be estimated simultaneously through eigen-decomposition. Compared with the conventional ones, this new method has less computational complexity while has higher estimation precision, what’s more, it can overcome the problem of ambiguity. Both theoretical analysis and computer simulations show the effectiveness of the proposed method.
In some object tracking systems, the moving object future position is an area (i.e., target area). It is a successful estimation strategy if the predicted points fall in the target area. If the object makes a sudden maneuvering, the prediction may get out of the target area easily which may make the tracking system lose the object. The aim is to investigate the admissible maximum object maneuvering intensity, which is characterized as model noise variance, for such kind of tracking system. Firstly, the concept of stochastic passage characteristics over the boundary of target area and their relationship with prediction error variance are described. Secondly, the consistency among the indices of regional pole, prediction error variance and stochastic passage characteristics is analyzed. Thirdly, the multi-indices constraints are characterized by a set of bi-linear matrix inequalities (BMIs). Then, the admissible maximum model noise variance and the satisfactory estimation strategy are presented by iteratively solving linear matrix inequalities (LMIs) to approximate BMIs. Finally, a numerical example is proposed to demonstrate the obtained results.
The problem of optimal aeroassisted symmetric transfer between elliptical orbits is concerned. The complete trajectory is assumed as consisting of two impulsive velocity changes at the beginning and the end of an interior atmospheric subarc, where the vehicle is controlled via the lift coefficient and thrust. The corresponding dynamic equations are built and bounded controls are considered. For the purpose of optimization computation, the equations are normalized. In order to minimize the total fuel consumption, the geocentric radius of initial elliptical transfer orbital perigee and controls during atmospheric flight should all be optimized. It is an optimal control problem which involves additional parameter optimization. To solve the problem, a two-level optimization method denoted by “genetic algorithm + Gauss pseudospectral method” is adopted: the genetic algorithm is used for parameter optimization and the Gauss pseudospectral method is used for optimal control problems. The flow chart of simulation is given. On this basis, the issue of more realistic modeling with two finite-thrust subarcs in the nonatmospheric part of the trajectory is simultaneously addressed. The orbital transfer problem is transformed to three continuous optimal control problems, and the constraints at different times are given, which are respectively solved by using the Gauss pseudospectral method. The obtained numerical results indicate that the optimal thrust control is of bangbang type. The minimum-fuel trajectory in the atmosphere consists of aeroglide, aerocruise and aeroglide. They are compared with the results of pure impulsive model, and the conclusions that a significant fuel saving will be achieved by synergetic maneuver are drawn.
A multiple targets detection method based on spatial smoothing (MTDSS) is proposed to solve the problem of the source number estimation under the colored noise background. The forward and backward smoothing based on auxiliary vectors which are received data on some specific elements is computed. By the spatial smoothing with auxiliary vectors, the correlated signals are decorrelated, and the colored noise is partially alleviated. The correlation matrix formed from the cross correlations between subarray data and auxiliary vectors is computed. By exploring the second-order statistics property of the covariance matrix, a threshold based on Gerschgorin radii of the smoothing correlation matrix is set to estimate the number of sources. Simulations and experimental results validate that MTDSS has an effective performance under the condition of the colored noise background and coherent sources, and MTDSS is robust with the correlated factor of signals and noise.
An adaptive neural network output-feedback regulation approach is proposed for a class of multi-input-multi-output nonlinear time-varying delayed systems. Both the designed observer and controller are free from time delays. Different from the existing results, this paper need not the assumption that the upper bounding functions of time-delay terms are known, and only a neural network is employed to compensate for all the upper bounding functions of time-delay terms, so the designed controller procedure is more simplified. In addition, the resulting closed-loop system is proved to be semi-globally ultimately uniformly bounded, and the output regulation error converges to a small residual set around the origin. Two simulation examples are provided to verify the effectiveness of control scheme.
To preserve the sharp features and details of the synthetic aperture radar (SAR) image effectively when despeckling, a despeckling algorithm with edge detection in nonsubsampled second generation bandelet transform (NSBT) domain is proposed. First, the Canny operator is utilized to detect and remove edges from the SAR image. Then the NSBT which has an optimal approximation to the edges of images and a hard thresholding rule are used to approximate the details while despeckling the edge-removed image. Finally, the removed edges are added to the reconstructed image. As the edges are detected and protected, and the NSBT is used, the proposed algorithm reaches the state-of-the-art effect which realizes both despeckling and preserving edges and details simultaneously. Experimental results show that both the subjective visual effect and the mainly objective performance indexes of the proposed algorithm outperform that of both Bayesian wavelet shrinkage with edge detection and Bayesian least square-Gaussian scale mixture (BLS-GSM).
To design approximately linear-phase complex coefficient finite impulse response (FIR) digital filters with arbitrary magnitude and group delay responses, a novel neural network approach is studied. The approach is based on a batch back-propagation neural network algorithm by directly minimizing the real magnitude error and phase error from the linear-phase to obtain the filter’s coefficients. The approach can deal with both the real and complex coefficient FIR digital filters design problems. The main advantage of the proposed design method is the significant reduction in the group delay error. The effectiveness of the proposed method is illustrated with two optimal design examples.
A new procedure for a design of multi-range controllers for use with highly nonlinear systems is developed. The procedure involves obtaining the describing function models of the nonlinear plant by software followed by designing a controller at nominal conditions. Then, the controller parameters are optimized to yield a satisfactory closed-loop response at all operating regimes. Finally, the performance and stability of the closed-loop system comprised of the designed controller and the nonlinear plant are verified. The procedure and the associated software are applied to a nonlinear control problem of the sort encountered in aerospace, and the results are compared with two other approaches.
Concurrent multipath transfer (CMT) using stream control transmission protocol (SCTP) multihoming has become an appealing option to increase the throughput and improve the performance of increasingly bandwidth-hungry applications. To investigate the rate allocation for applications in CMT, this paper analyzes the capacities of paths shared by competing sources, then proposes the rate allocation model for elastic flows based on the framework of network utility maximization (NUM). In order to obtain the global optimum of the model, a distributed algorithm is presented which depends only on local available information. Simulation results confirm that the proposed algorithm can achieve the global optimum within reasonable convergence times.
The receding horizon control (RHC) problem is considered for nonlinear Markov jump systems which can be represented by Takagi-Sugeno fuzzy models subject to constraints both on control inputs and on observe outputs. In the given receding horizon, for each mode sequence of the T-S modeled nonlinear system with Markov jump parameter, the cost function is optimized by constraints on state trajectories, so that the optimization control input sequences are obtained in order to make the state into a terminal invariant set. Out of the receding horizon, the stability is guaranteed by searching a state feedback control law. Based on such stability analysis, a linear matrix inequality approach for designing receding horizon predictive controller for nonlinear systems subject to constraints both on the inputs and on the outputs is developed. The simulation shows the validity of this method.
For better interpretation of synthetic aperture radar (SAR) images, the speckle filtering is an important issue. In the area of speckle filtering, the proper averaging of samples with similar scattering characteristics is of great importance. However, existing filtering algorithms are either lack of a similarity judgment of scattering characteristics or using only intensity information for similarity judgment. A novel polarimetric SAR (PolSAR) speckle filtering algorithm based on the mean shift theory is proposed. As polarimetric covariance matrices or coherency matrices form Riemannian manifold, the pixels with similar scattering characteristics gather closely and those with different scattering characteristics separate in this hyperspace. By using the range-spatial joint mean shift theory in Riemannian manifold, the pixels chosen for averaging are ensured to be close not only in scattering characteristics but also in the spatial domain. German Aerospace Center (DLR) L-Band Experiment SAR (E-SAR) data and East China Research Institute of Electronic Engineering (ECRIEE) PolSAR data are used to demonstrate the efficiency of the proposed algorithm. The filtering results of two commonly used speckle filtering algorithms, refined Lee filtering algorithm and intensity driven adaptive neighborhood (IDAN) filtering algorithm, are also presented for the comparison purpose. Experiment results show that the proposed speckle filtering algorithm achieves a good performance in terms of speckle filtering, edge protection as well as polarimetric characteristics preservation.
Event region detection is the important application for wireless sensor networks (WSNs), where the existing faulty sensors would lead to drastic deterioration of network quality of service. Considering single-moment nodes fault-tolerance, a novel distributed fault-tolerant detection algorithm named distributed fault-tolerance based on weighted distance (DFWD) is proposed, which exploits the spatial correlation among sensor nodes and their redundant information. In sensor networks, neighborhood sensor nodes will be endowed with different relative weights respectively according to the distances between them and the central node. Having syncretized the weighted information of dual-neighborhood nodes appropriately, it is reasonable to decide the ultimate status of the central sensor node. Simultaneously, readings of faulty sensors would be corrected during this process. Simulation results demonstrate that the DFWD has a higher fault detection accuracy compared with other algorithms, and when the sensor fault probability is 10%, the DFWD can still correct more than 91% faulty sensor nodes, which significantly improves the performance of the whole sensor network.
Frequency-invariant beamformer (FIB) design is a key issue in wideband array signal processing. To use commonly wideband linear array with tapped delay line (TDL) structure and complex weights, the FIB design is provided according to the rule of minimizing the sidelobe level of the beampattern at the reference frequency while keeping the distortionless response constraint in the mainlobe direction at the reference frequency, the norm constraint of the weight vector and the amplitude constraint of the averaged spatial response variation (SRV). This kind of beamformer design problem can be solved with the interior-point method after being converted to the form of standard second order cone programming (SOCP). The computer simulations are presented which illustrate the effectiveness of our FIB design method for the wideband linear array with TDL structure and complex weights.
Ubiquitous radar is a new radar system that provides continuous and uninterrupted multifunction capability within a coverage volume. Continuous coverage from close-in “pop-up” targets in clutter to long-range targets impacts selection of waveform parameters. The coherent processing interval (CPI) must be long enough to achieve a certain signal-to-noise ratio (SNR) that ensures the efficiency of detection. The condition of detection in the case of low SNR is analyzed, and three different cases that would occur during integration are discussed and a method to determine the CPI is presented. The simulation results show that targets detection with SNR as low as −26 dB in the experimental system can possibly determine the CPI.
The problem on stabilization for the system with distributed delays is researched. The distributed time-delay under consideration is assumed to be a constant time-delay, but not known exactly. A design method is proposed for a memory proportional and integral (PI) feedback controller with adaptation to distributed time-delay. The feedback controller with memory simultaneously contains the current state and the past distributed information of the addressed systems. The design for adaptation law to distributed delay is very concise. The controller can be derived by solving a set of linear matrix inequalities (LMIs). Two numerical examples are given to illustrate the effectiveness of the design method.
The symbol-error-rate (SER) and power allocation for hybrid cooperative (HC) transmission system are investigated. Closed-form SER expression is derived by using the moment generating function (MGF)-based approach. However, the resultant SER contains an MGF of the harmonic mean of two independent random variables (RVs), which is not tractable in SER analysis. We present a simple MGF expression of the harmonic mean of two independent RVs which avoids the hypergeometric functions used commonly in previous studies. Using the simple MGF, closed-form SER for HC system with M-ary phase shift keying (M-PSK) signals is provided. Further, an approximation as well as an upper bound of the SER is presented. It is shown that the SER approximation is asymptotically tight. Based on the tight SER approximation, the power allocation of the HC system is investigated. It is shown that the optimal power allocation does not depend on the fading parameters of the source-destination (SD) channel and it only depends on the source-relay (SR) and relay-destination (RD) channels. Moreover, the performance gain of the power allocation depends on the ratio of the channel quality between RD and SR. With the increase of this ratio, more performance gain can be acquired.
The GPS multipath signal model is presented, which indicates that the coherent DLL outputs in multipath environment are the convolution between the ideal DLL outputs and the channel responses. So the channel responses can be estimated by a least square method using the observed curve of the DLL discriminator. In terms of the estimated multipath channels, two multipath mitigation methods are discussed, which are equalization filtering and multipath subtracting, respectively. It is shown, by computer simulation, that the least square method has a good performance in channels estimation and the multipath errors can be mitigated almost completely by either of the methods. However, the multipath subtracting method has relative small remnant errors than equalization filtering.
This paper proposes a particle swarm optimization (PSO) based particle filter (PF) tracking framework, the embedded PSO makes particles move toward the high likelihood area to find the optimal position in the state transition stage, and simultaneously incorporates the newest observations into the proposal distribution in the update stage. In the proposed approach, likelihood measure functions involving multiple features are presented to enhance the performance of model fitting. Furthermore, the multi-feature weights are self-adaptively adjusted by a PSO algorithm throughout the tracking process. There are three main contributions. Firstly, the PSO algorithm is fused into the PF framework, which can efficiently alleviate the particles degeneracy phenomenon. Secondly, an effective convergence criterion for the PSO algorithm is explored, which can avoid particles getting stuck in local minima and maintain a greater particle diversity. Finally, a multi-feature weight self-adjusting strategy is proposed, which can significantly improve the tracking robustness and accuracy. Experiments performed on several challenging public video sequences demonstrate that the proposed tracking approach achieves a considerable performance.
The consensus problem of the distributed attitude synchronization in the spacecraft formation flying is considered. Firstly, the attitude dynamics of a rigid body spacecraft is described by modified Rodriguez parameters (MRPs). Then global stable distributed cooperative attitude control laws are proposed for different cases. In the first case, the control law guarantees the state consensus during the attitude synchronization. In the second case, the control law ensures both the attitudes synchronizing to a desired constant attitude and the angular velocities converging at zero. In the third case, an attitude consensus control law with bounded control input is proposed. Finally, the effectiveness and validity of the control laws are demonstrated by simulations of six rigid bodies formation flying.
To solve the multi-class fault diagnosis tasks, decision tree support vector machine (DTSVM), which combines SVM and decision tree using the concept of dichotomy, is proposed. Since the classification performance of DTSVM highly depends on its structure, to cluster the multi-classes with maximum distance between the clustering centers of the two sub-classes, genetic algorithm is introduced into the formation of decision tree, so that the most separable classes would be separated at each node of decisions tree. Numerical simulations conducted on three datasets compared with “one-against-all” and “one-against-one” demonstrate the proposed method has better performance and higher generalization ability than the two conventional methods.
Target tracking is one of the applications of wireless sensor networks (WSNs). It is assumed that each sensor has a limited range for detecting the presence of the object, and the network is sufficiently dense so that the sensors can cover the area of interest. Due to the limited battery resources of sensors, there is a tradeoff between the energy consumption and tracking accuracy. To solve this problem, this paper proposes an energy efficient tracking algorithm. Based on the cooperation of dispatchers, sensors in the area are scheduled to switch their working mode to track the target. Since energy consumed in active mode is higher than that in monitoring or sleeping mode, for each sampling interval, a minimum set of sensors is woken up based on the select mechanism. Meanwhile, other sensors keep in sleeping mode. Performance analysis and simulation results show that the proposed algorithm provides a better performance than other existing approaches.
A new combinational technology is proposed, which is feasible to apply physical-layer network coding (PNC) to wireless fading channels by employing the harmful interference strategically. The key step of PNC is that sources broadcast signals simultaneously without orthogonal scheduling. Naturally, the signals overlap in the free space at the receivers. Since the signals from different sources are mutual independent, rooted on this rational assumption, an enhanced joint diagonalization separation named altering row diagonalization (ARD) algorithm is exploited to separate these signals by maximizing the cost function measuring independence among them. This ARD PNC (APNC) methodology provides an innovative way to implement signal-level network coding at the presence of interference and without any priori information about channels in fading environments. In conclusions, the proposed APNC performs well with higher bandwidth utility and lower error rate.
The problems of robust exponential stability in mean square and delayed state feedback stabilization for uncertain stochastic systems with time-varying delay are studied. By using Jensen’s integral inequality and combining with the free weighting matrix approach, new delay-dependent stability conditions and delayed state feedback stabilization criteria are obtained in terms of linear matrix inequalities. Meanwhile, the proposed delayed state feedback stabilization criteria are more convenient in application than the existing ones since fewer tuning parameters are involved. Numerical examples are given to illustrate the effectiveness of the proposed methods.
The class of multiple attribute decision making (MADM) problems is studied, where the attribute values are intuitionistic fuzzy numbers, and the information about attribute weights is completely unknown. A score function is first used to calculate the score of each attribute value and a score matrix is constructed, and then it is transformed into a normalized score matrix. Based on the normalized score matrix, an entropy-based procedure is proposed to derive attribute weights. Furthermore, the additive weighted averaging operator is utilized to fuse all the normalized scores into the overall scores of alternatives, by which the ranking of all the given alternatives is obtained. This paper is concluded by extending the above results to interval-valued intuitionistic fuzzy set theory, and an illustrative example is also provided.
In order to improve the survival ability and rapid response ability of the carrier craft, a new rapid transfer alignment method of the strapdown inertial navigation system (SINS) on a rocking base is put forward. In the method, the aircraft carrier does not need any form of movement. Meantime, interfering motions such as rolling, pitching, and yawing motions caused by sea waves are effectively used. Firstly, the deck flexure deformation model is made. Secondly, the state space model of transfer alignment is presented. Finally, the feasibility of this method is validated by the simulation. Simulation results show that the misalignment angle error can be estimated and reach an anticipated precision— 0.2 mrad in 5 s, while the deck deformation angle error can be estimated and reach a better precision— 0.1 mrad in 20 s.
