The purpose of this paper is to apply inertial technique to string averaging projection method and block-iterative projection method in order to get two accelerated projection algorithms for solving convex feasibility problem. Compared with the existing accelerated methods for solving the problem, the inertial technique employs a parameter sequence and two previous iterations to get the next iteration and hence improves the flexibility of the algorithm. Theoretical asymptotic convergence results are presented under some suitable conditions. Numerical simulations illustrate that the new methods have better convergence than the general projection methods. The presented algorithms are inspired by the inertial proximal point algorithm for finding zeros of a maximal monotone operator.
A novel neural network based on iterated unscented Kalman filter (IUKF) algorithm is established to model and compensate for the fiber optic gyro (FOG) bias drift caused by temperature. In the network, FOG temperature and its gradient are set as input and the FOG bias drift is set as the expected output. A 2-5-1 network trained with IUKF algorithm is established. The IUKF algorithm is developed on the basis of the unscented Kalman filter (UKF). The weight and bias vectors of the hidden layer are set as the state of the UKF and its process and measurement equations are deduced according to the network architecture. To solve the unavoidable estimation deviation of the mean and covariance of the states in the UKF algorithm, iterative computation is introduced into the UKF after the measurement update. While the measurement noise R is extended into the state vectors before iteration in order to meet the statistic orthogonality of estimate and measurement noise. The IUKF algorithm can provide the optimized estimation for the neural network because of its state expansion and iteration. Temperature rise (–20–20?C) and drop (70–20?C) tests for FOG are carried out in an attemperator. The temperature drift model is built with neural network, and it is trained respectively with BP, UKF and IUKF algorithms. The results prove that the proposed model has higher precision compared with the backpropagation (BP) and UKF network models.
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.
This paper considers robust fault detection and diagnosis for input uncertain nonlinear systems. It proposes a multi-objective fault detection criterion so that the fault residual is sensitive to the fault but insensitive to the uncertainty as much as possible. Then the paper solves the proposed criterion by maximizing the smallest singular value of the transformation from faults to fault detection residuals while minimizing the largest singular value of the transformation from input uncertainty to the fault detection residuals. This method is applied to an aircraft which has a fault in the left elevator or rudder. The simulation results show the proposed method can detect the control surface failures rapidly and efficiently.
The method of establishing data structures plays an important role in the efficiency of parallel multilevel fast multipole algorithm (PMLFMA). Considering the main complements of multilevel fast multipole algorithm (MLFMA) memory, a new parallelization strategy and a modified data octree construction scheme are proposed to further reduce communication in order to improve parallel efficiency. For far interaction, a new scheme called dynamic memory allocation is developed. To analyze the workload balancing performance of a parallel implementation, the original concept of workload balancing factor is introduced and verified by numerical examples. Numerical results show that the above measures improve the parallel efficiency and are suitable for the analysis of electrical large-scale scattering objects.
To solve the uncertain multi-attribute group decisionmaking of unknown attribute weights, three optimal models are built to decide the corresponding ideal solution weights, standard deviation weights and mean deviation weights. The comprehensive attribute weights are gotten through the product of the above three kinds of weights. And each decision maker's weighted decision matrices are also received by using the integrated attribute weights. The closeness degrees are also gotten by use of technique for order preference by similarity to ideal solution (TOPSIS) through dealing with the weighted decision matrices. At the same time the group decision matrix and weighted group decision matrix are gotten by using each decision-maker’s closeness degree to every project. Then the vertical TOPSIS method is used to calculate the closeness degree of each project. So these projects can be ranked according to their values of the closeness degree. The process of the method is also given step by step. Finally, a numerical example demonstrates the feasibility and effectiveness of the approach.
A novel particle filter bandwidth adaption for kernel particle filter (BAKPF) is proposed. Selection of the kernel bandwidth is a critical issue in kernel density estimation (KDE). The plug-in method is adopted to get the global fixed bandwidth by optimizing the asymptotic mean integrated squared error (AMISE) firstly. Then, particle-driven bandwidth selection is invoked in the KDE. To get a more effective allocation of the particles, the KDE with adaptive bandwidth in the BAKPF is used to approximate the posterior probability density function (PDF) by moving particles toward the posterior. A closed-form expression of the true distribution is given. The simulation results show that the proposed BAKPF performs better than the standard particle filter (PF), unscented particle filter (UPF) and the kernel particle filter (KPF) both in efficiency and estimation precision.
The problem of two order statistics detection schemes for the detection of a spatially distributed target in white Gaussian noise are studied. When the number of strong scattering cells is known, we first show an optimal detector, which requires many processing channels. The structure of such optimal detector is complex. Therefore, a simpler quasi-optimal detector is then introduced. The quasi-optimal detector, called the strong scattering cells’ number dependent order statistics (SND-OS) detector, takes the form of an average of maximum strong scattering cells with a known number. If the number of strong scattering cells is unknown in real situation, the multi-channel order statistics (MC-OS) detector is used. In each channel, a various number of maximums scattered from target are averaged. Then, the false alarm probability analysis and thresholds sets for each channel are given, following the detection results presented by means of Monte Carlo simulation strategy based on simulated target model and three measured targets. In particular, the theoretical analysis and simulation results highlight that the MC-OS detector can efficiently detect range-spread targets in white Gaussian noise.
This article deals with the robust stability analysis and passivity of uncertain discrete-time Takagi-Sugeno (T-S) fuzzy systems with time delays. The T-S fuzzy model with parametric uncertainties can approximate nonlinear uncertain systems at any precision. A sufficient condition on the existence of robust passive controller is established based on the Lyapunov stability theory. With the help of linear matrix inequality (LMI) method, robust passive controllers are designed so that the closed-loop system is robust stable and strictly passive. urthermore, a convex optimization problem with LMI constraints is formulated to design robust passive controllers with the maximum dissipation rate. A numerical example illustrates the validity of the proposed method.
To estimate the spreading sequence of the direct sequence spread spectrum (DSSS) signal, a fast algorithm based on maximum likelihood function is proposed, and the theoretical derivation of the algorithm is provided. By simplifying the objective function of maximum likelihood estimation, the algorithm can realize sequence synchronization and sequence estimation via adaptive iteration and sliding window.since it avoids the correlation matrix computation, the algorithm significantly reduces the storage requirement and the computation complexity. Simulations show that it is a fast convergent algorithm, and can perform well in low signal to noise ratio (SNR).
Intuitionistic trapezoidal fuzzy numbers and their operational laws are defined. Based on these operational laws, some aggregation operators, including intuitionistic trapezoidal fuzzy weighted arithmetic averaging operator and weighted geometric averaging operator are proposed. Expected values, score function, and accuracy function of intuitionitsic trapezoidal fuzzy numbers are defined. Based on these, a kind of intuitionistic trapezoidal fuzzy multi-criteria decision making method is proposed. By using these aggregation operators, criteria values are aggregated and integrated intuitionistic trapezoidal fuzzy numbers of alternatives are attained. By comparing score function and accuracy function values of integrated fuzzy numbers, a ranking of the whole alternative set can be attained. An example is given to show the feasibility and availability of the method.
Doppler blind zone (DBZ) has a bad influence on the airborne early warning radar, although it has good detection performance for low altitude targets with pulse Doppler (PD) technology. In target tracking, the blind zone can cause target tracking breakage easily. In order to solve this problem, a parallel particle filter (PF) algorithm based on multi-hypothesis motion models (MHMMs) is proposed. The algorithm produces multiple possible target motion models according to the DBZ constraint. Particles are updated with the constraint in each motion model. Once the first measurement from the target which reappears from DBZ falls into the particle cloud formed by any model, the measurementtrack association succeeds and track breakage is avoided. The simulation results show that on the condition of different DBZ ranges, a high association ratio can be got for targets with different maneuverability levels, which accordingly improves the tracking quality.
The mechanical system with backlash is distinguished between a “backlash mode” and a “contact mode”. The inherent switching between the two operating modes makes the system a prime example of hybrid system. For eliminating the bad effect of backlash, a piecewise affine (PWA) model of the mechanical servo system with backlash is built. The optimal control of constrained PWA system is obtained by taking advantage of model predictive control (MPC) method, and the explicit solution of MPC in a look-up table form is figured out by combining the dynamic programming and multi-parametric quadratic programming, thereby establishing an explicit hybrid model predictive controller. Furthermore, a piecewise quadratic (PWQ) function for guaranteeing the stability of closed-loop control is found by formulating the search of PWQ function as a semi-definite programming problem. In the tracking experiments, it is demonstrated that the explicit hybrid model predictive controller has a good traction control effect on the mechanical system with backlash. The error meets the demands of real system. Further, compared to the direct on-line computation, the computation burden is reduced by the explicit solution, thereby being suitable for real-time control of system with short sampling time.
A joint power control and relay selection scheme is considered for a cooperative and cognitive radio system where a secondary network shares spectrum with the primary network. In the secondary network, two secondary users (SUs) communicate with each other via an assist relay. The main point is to provide the best system performance to SUs while maintaining the interference power at primary user (PU) under a certain level. Using convex optimization, a closed-form solution is obtained when optimizing the power allocation among the two nodes and relay. Based on this result, a joint power control and relay selection scheme with fewer variable dimensions is proposed to maximize the achievable rate of the secondary system. Simulation results demonstrate that the sum rate of the cognitive two-way relay network increases compared with a random relay selection and fixed power allocation.
An approach is proposed to realize a digital channelized receiver in the fractional Fourier domain (FRFD) for signal intercept applications. The presented architecture can be considered as a generalization of that in the traditional Fourier domain. Since the linear frequency modulation (LFM) signal has a good energy concentration in the FRFD, by choosing an appropriate fractional Fourier transform (FRFT) order, the presented architecture can concentrate the broadband LFM signal into only one sub-channel and that will prevent it from crossing several sub-channels. Thus the performance of the signal detection and parameter estimation after the sub-channel output will be improved significantly. The computational complexity is reduced enormously due to the implementation of the polyphase filter bank decomposition, thus the proposed architecture can be realized as efficiently as in the Fourier domain. The related simulation results are presented to verify the validity of the theories and methods involved in this paper.
The margin maximization problem in digital subscriber line (DSL) systems is investigated. The particle swarm optimization (PSO) theory is applied to the nonconvex margin optimization problem with the target power and rate constraints. PSO is a new evolution algorithm based on the social behavior of swarms, which can solve discontinuous, nonconvex and nonlinear problems efficiently. The proposed algorithm can converge to the global optimal solution, and numerical example demonstrates that the proposed algorithm can guarantee the fast convergence within a few iterations.
The goal of this research is to develop an emergency disaster relief mobilization tool that determines the mobilization levels of commodities, medical service and helicopters (which will be utilized as the primary means of transport in a mountain region struck by a devastating earthquake) at pointed temporary facilities, including helicopter-based delivery plans for commodities and evacuation plans for critical population, in which relief demands are considered as uncertain. The proposed mobilization model is a two-stage stochastic mixed integer program with two objectives: maximizing the expected fill rate and minimizing the total expenditure of the mobilization campaign. Scenario decomposition based heuristic algorithms are also developed according to the structure of the proposed model. The computational results of a numerical example, which is constructed from the scenarios of the Great Wenchuan Earthquake, indicate that the model can provide valuable decision support for the mobilization of post-earthquake relief, and the proposed algorithms also have high efficiency in computation.
This paper investigates the integrated fault detection and diagnosis (FDD) with fault tolerant control (FTC) method of the control system with recoverable faults. Firstly, a quasi-linear parameter-varying (QLPV) model is set up, in which effectiveness factors are modeled as time-varying parameters to quantify actuators and sensors faults. Based on the certainty equivalency principle, replacing the real time states in the nonlinear term of the QLPV model with the estimated states, the parameters and states can be estimated by a two-stage Kalman filtering algorithm. Then, a polynomial eigenstructure assignment (PEA) controller with time-varying parameters and states is designed to guarantee the performance of the system with recoverable faults. Finally, mathematical simulation is performed to validate the solution in a satellite closed-loop attitude control system, and simulation results show that the solution is fast and effective for on-orbit real-time computation.
An innovative method of cooperative frequency domain differential modulation and demodulation is presented. This method applies the prior knowledge of channel propagation to selecting the variable differential length and carrying out frequency domain modulation. This strategy optimizes the design of system parameters to effectively improve the anti-interference ability of the differential system in time-varied multipath channel circumstance without making the execution more complicating. The simulations and comparisons demonstrate the proposed method is effective, and the results show that it is especially suitable for the fading channel with strong propagation and fast time-variation.
Due to their characteristics of dynamic topology, wireless channels and limited resources, mobile ad hoc networks are particularly vulnerable to a denial of service (DoS) attacks launched by intruders. The effects of flooding attacks in network simulation 2 (NS2) and measured performance parameters are investigated, including packet loss ratio, average delay, throughput and average number of hops under different numbers of attack nodes, flooding frequency, network bandwidth and network size. Simulation results show that with the increase of the flooding frequency and the number of attack nodes, network performance sharply drops. But when the frequency of flooding attacks or the number of attack nodes is greater than a certain value, performance degradation tends to a stable value.
With applying the information technology to the military field, the advantages and importance of the networked combat are more and more obvious. In order to make full use of limited battlefield resources and maximally destroy enemy targets from arbitrary angle in a limited time, the research on firepower nodes dynamic deployment becomes a key problem of command and control. Considering a variety of tactical indexes and actual constraints in air defense, a mathematical model is formulated to minimize the enemy target penetration probability. Based on characteristics of the mathematical model and demands of the deployment problems, an assistance-based algorithm is put forward which combines the artificial potential field (APF) method with a memetic algorithm. The APF method is employed to solve the constraint handling problem and generate feasible solutions. The constrained optimization problem transforms into an optimization problem of APF parameters adjustment, and the dimension of the problem is reduced greatly. The dynamic deployment is accomplished by generation and refinement of feasible solutions. The simulation results show that the proposed algorithm is effective and feasible in dynamic situation.
The concept of rough communication based on bothbranch fuzzy set is proposed, in which the loss of information may exist, for each agent there has a different language and can not provide precise communication to each other. The method of information measure in a rough communication based on bothbranch fuzzy set is proposed. By using some concepts, such as |α|−both-branch rough communication cut, the relation theorem between rough communication based on both-branch fuzzy concept and rough communication based on classical concept is obtained. Finally, an example of rough communication based on both-branch fuzzy set is given.
Based on the idea of zeroing the line of sight rate (LOSR), a novel nonlinear differential geometric (DG) law for intercepting the agile target is proposed. In the first part, the DG formulations are utilized to describe the relatively kinematics model of missile and target, and the nonlinear DG guidance (DGG) law is proposed based on the nonlinear control theory to eliminate the influence brought by target. Further, the missile guidance commands are derived to overcome the information loss caused by decoupling condition, the new necessary initial condition is developed to guarantee capture the agile target. Then, the designed nonlinear DGG commands are transformed from an arc-length system to the time domain. A desirable aspect of the designed guidance law is that it does not require rigorous information about target acceleration. Representative numerical results show that the designed guidance law obtain a better performance than the traditional DGG law for agile target.
An adaptive integral dynamic surface control approach based on fully tuned radial basis function neural network (FTRBFNN) is presented for a general class of strict-feedback nonlinear systems, which may possess a wide class of uncertainties that are not linearly parameterized and do not have any prior knowledge of the bounding functions. FTRBFNN is employed to approximate the uncertainty online, and a systematic framework for adaptive controller design is given by dynamic surface control. The control algorithm has two outstanding features, namely, the neural network regulates the weights, width and center of Gaussian function simultaneously, which ensures the control system has perfect ability of restraining different unknown uncertainties and the integral term of tracking error introduced in the control law can eliminate the static error of the closed loop system effectively. As a result, high control precision can be achieved. All signals in the closed loop system can be guaranteed bounded by Lyapunov approach. Finally, simulation results demonstrate the validity of the control approach.
A distributed coordinated consensus problem for multiple networked Euler-Lagrange systems is studied. The communication between agents is subject to time delays, unknown parameters and nonlinear inputs, but only with their states available for measurement. When the communication topology of the system is connected, an adaptive control algorithm with self-delays and uncertainties is suggested to guarantee global full-state synchronization that the difference between the agent’s positions and velocities asymptotically converges to zero. Moreover, the distributed sliding-mode law is given for chaotic systems with nonlinear inputs to compensate for the effects of nonlinearity. Finally, simulation results show the effectiveness of the proposed control algorithm.
A two-dimensional direction-of-arrival (DOA) and polarization estimation algorithm for coherent sources using a linear vector-sensor array is presented. Two matrices are first constructed by the receiving data. The ranks of the two matrices are only related to the DOAs of the sources and independent of their coherency. Then the source’s elevation is resolved via the matrix pencil (MP) method, and the singular value decomposition (SVD) is used to reduce the noise effect. Finally, the source’s steering vector is estimated, and the analytics solutions of the source’s azimuth and polarization parameter can be directly computed by using a vector cross-product estimator. Moreover, the proposed algorithm can achieve the unambiguous direction estimates, even if the space between adjacent sensors is larger than a half-wavelength. Theoretical and numerical simulations show the effectiveness of the proposed algorithm.
Because the conventional ultra wideband (UWB) radar imaging algorithm cannot meet the demand in the capability of multiple targets detection, a novel UWB radar imaging algorithm based on the near field radiation theory of dipole is presented. On the foundation of researching the principle of a time domain imaging algorithm, the back projection (BP) algorithm is derived and analyzed. Firstly, the far field sampling data are transferred to the near field sampling data by using the near field radiation theory of dipole. Then the BP algorithm is applied to target detection. The capability of the new algorithm to detect the multi-target is verified by using the finite-difference time-domain method, and the threedimensional images of targets are obtained. The coupling effect between targets for imaging is analyzed. The simulation results show that the new UWB radar imaging algorithm based on the near field radiation theory of dipole could weaken the coupling effect for imaging, and as a result the quality of imaging is improved.
Ultrafast optoelectronic technology has been widely used in terahertz time domain spectrum, terahertz imaging technology, terahertz communication and so on, and great progress has been achieved in the past two decade. Recently, this innovative technology has been applied in radio metrology and supplied a potential and hopeful method to solve the existent challenges of calibration devices and equipments with bandwidth up to 100 GHz. This paper generally summarizes the emerging applications of the ultrafast optoelectronic technology in radio metrology. The main applications of this technology in calibrating broadband sampling oscilloscopes, the high-speed photodiodes and calibrating the electrical pulse generators are emphasized, and the testing of monolithic microwave integrated circuits is also presented.
The problem of stochastically allocating redundant components to increase the system lifetime is an important topic of reliability. An optimal redundancy allocation is proposed, which maximizes the expected lifetime of a reliability system with subsystems consisting of components in parallel. The constraints are minimizing the total resources and the sizes of subsystems. In this system, each switching is independent with each other and works with probability p. Two optimization problems are studied by an incremental algorithm and dynamic programming technique respectively. The incremental algorithm proposed could obtain an approximate optimal solution, and the dynamic programming method could generate the optimal solution.
An improved block matching approach to fast disparity estimation in machine vision applications is proposed, where the matching criterion is the sum of the absolute difference (SAD). By evaluating the lower bounds, which become increasingly tighter for the matching criteria, the method tries to successively terminate unnecessary computations of the matching criteria between the reference block in one image and the ineligible candidate blocks in another image. It also eliminates the ineligible blocks as early as possible, while ensuring the optimal disparity of each pixel. Also, the proposed method can further speed up the elimination of ineligible candidate blocks by efficiently using the continuous constraint of disparity to predict the initial disparity of each pixel. The performance of the new algorithm is evaluated by carrying out a theoretical analysis, and by comparing its performance with the disparity estimation method based on the standard block matching. Simulated results demonstrate that the proposed algorithm achieves a computational cost reduction of over 50.5% in comparision with the standard block matching method.
The distributed leadless consensus problem for multiple quadrotor systems under fixed and switching topologies is investigated. The objective is to design protocols achieving consensus for networked quadrotors’ positions and attitudes. Because the model of a quadrotor is a strong high-order nonlinear coupling system, the approach of feedback linearization is employed to transform the model into a group of four linear subsystems among which there is no coupling. Then, a consensus algorithm is proposed which consists of a local feedback controller and interactions from the finite neighbors under fixed undirected topologies. Especially, the problem of choosing the parameters in the consensus algorithm is also addressed, enlightened by the results of the robust control theory. Furthermore, it is proved that the proposed algorithm also guarantees the consensus under undirected switching topologies. Simulation results show the effectiveness of the proposed algorithm.
In order to calculate the cross-correlation of two color images treated as vector in a holistic manner, a rapid vertical/parallel decomposition algorithm for quaternion is resented. The calculation for decomposition is reduced from 21 times to 4 times real number multiplications with the same results. An algorithm for cross-correlation of color images based on decomposition in time domain is put forward, in which some properties pointed out in this paper can be utilized to reduce the computational complexity. Simulation results show the effectiveness and superiority of the proposed method.
The data-driven fault diagnosis methods can improve the reliability of analog circuits by using the data generated from it. The data have some characteristics, such as randomness and incompleteness, which lead to the diagnostic results being sensitive to the specific values and random noise. This paper presents a data-driven fault diagnosis method for analog circuits based on the robust competitive agglomeration (RCA), which can alleviate the incompleteness of the data by clustering with the competing process. And the robustness of the diagnostic results is enhanced by using the approach of robust statistics in RCA. A series of experiments are provided to demonstrate that RCA can classify the incomplete data with a high accuracy. The experimental results show that RCA is robust for the data needed to be classified as well as the parameters needed to be adjusted. The effectiveness of RCA in practical use is demonstrated by two analog circuits.
The iterative learning control (ILC) has been demonstrated to be capable of considerably improving the tracking performance of systems which are affected by the iteration-independent disturbance. However, the achievable performance is greatly degraded when iteration-dependent, stochastic disturbances are presented. This paper considers the robustness of the ILC algorithm for the nonlinear system in presence of stochastic measurement disturbances. The robust convergence of the P-type ILC algorithm is firstly addressed, and then an improved ILC algorithm with a decreasing gain is proposed. Theoretical analyses show that the proposed algorithm can guarantee that the tracking error of the nonlinear system tends to zero in presence of measurement disturbances. The analysis is also supported by a numerical example.
A novel tunable-quality-factor (tunable-Q) contourlet transform for geometric image representation is proposed. The Laplacian pyramid in original contourlet decomposes a signal into channels that have the same bandwidth on a logarithmic scale, and is not suitable for images with different behavior in frequency domain. We employ a new tunable-Q decomposition defined in the frequency domain by which one can flexibly tune the bandwidth of decomposition channels. With an acceptable redundancy, this tunable-Q contourlet is also anti-aliasing and its basis is sharply localized in the desired area of frequency and spatial domain. Our experiments in nonlinear approximation and denoising show that the contourlet using a better-suitable quality factor can achieve a more promising performance and often outperform wavelets and the previous contourlets both in visual quality and in terms of peak signal-to-noise ratio.
To study multi-radio multi-channel (MR-MC) Ad Hoc networks based on 802.11, an efficient cross-layer routing protocol with the function of joint channel assignment, called joint channel assignment and cross-layer routing (JCACR), is presented. Firstly, this paper introduces a new concept called channel utilization percentage (CUP), which is for measuring the contention level of different channels in a node’s neighborhood, and deduces its optimal value for determining whether a channel is overloaded or not. Then, a metric parameter named channel selection metric (CSM) is designed, which actually reflects not only the channel status but also corresponding node’s capacity to seize it. JCACR evaluates channel assignment by CSM, performs a local optimization by assigning each node a channel with the smaller CSM value, and changes the working channel dynamically when the channel is overloaded. Therefore, the network load balancing can be achieved. In addition, simulation shows that, when compared with the protocol of weighted cumulative expected transfer time (WCETT), the new protocol can improve the network throughput and reduce the end-to-end average delay with fewer overheads.
