In order to avoid the system performance deterioration caused by the wireless fading channel and imperfect channel estimation in cognitive radio networks, the spectrum sharing problem with the consideration of feedback control information from the primary user is analyzed. An improved spectrum sharing algorithm based on the combination of the feedback control information and the optimization algorithm is proposed. The relaxation method is used to achieve the approximate spectrum sharing model, and the spectrum sharing strategy that satisfies the individual outage probability constraints can be obtained iteratively with the observed outage probability. Simulation results show that the proposed spectrum sharing algorithm can achieve the spectrum sharing strategy that satisfies the outage probability constraints and reduce the average outage probability without causing maximum transmission rate reduction of the secondary user.
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 new iterative algorithm is proposed to reconstruct an unknown sparse signal from a set of projected measurements. Unlike existing greedy pursuit methods which only consider the atoms having the highest correlation with the residual signal, the proposed algorithm not only considers the higher correlation atoms but also reserves the lower correlation atoms with the residual signal. In the lower correlation atoms, only a few are correct which usually impact the reconstructive performance and decide the reconstruction dynamic range of greedy pursuit methods. The others are redundant. In order to avoid redundant atoms impacting the reconstructive accuracy, the Bayesian pursuit algorithm is used to eliminate them. Simulation results show that the proposed algorithm can improve the reconstructive dynamic range and the reconstructive accuracy. Furthermore, better noise immunity compared with the existing greedy pursuit methods can be obtained.
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.
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.
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.
An opportunistic maintenance model is presented for a continuously deteriorating series system with economical dependence. The system consists of two kinds of units, which are respectively subjected to the deterioration failure described by Gamma process and the random failure described by Poisson process. A two-level opportunistic policy defined by three decision parameters is proposed to coordinate the different maintenance actions and minimize the long-run maintenance cost rate of the system. A computable expression of the average cost rate is established by using the renewal property of the stochastic process of the maintained system state. The optimal values of three decision parameters are derived by an iteration approach based on the characteristic of Gamma process. The behavior of the proposed policy is illustrated through a numerical experiment. Comparative study with the widely used corrective maintenance policy demonstrates the advantage of the proposed opportunistic maintenance method in significantly reducing the maintenance cost. Simultaneously, the applicable area of this opportunistic model is discussed by the sensitivity analysis of the set-up cost and random failure rate.
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.
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.
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.
A cued search algorithm with uncertain detection performance is proposed for phased array radars. Firstly, a target search model based on the information gain criterion is presented with known detection performance, and the statistical characteristic of the detection probability is calculated by using the fluctuant model of the target radar cross section (RCS). Secondly, when the detection probability is completely unknown, its probability density function is modeled with a beta distribution, and its posterior probability distribution with the radar observation is derived based on the Bayesian theory. Finally simulation results show that the cued search algorithm with a known RCS fluctuant model can achieve the best performance, and the algorithm with the detection probability modeled as a beta distribution is better than that with a random selected detection probability because the model parameters can be updated by the radar observation to approach to the real value of the detection probability.
For global navigation satellite system (GNSS) signals in Gaussian and Rayleigh fading channel, a novel signal detection algorithm is proposed. Under the low frequency uncertainty case, after performing discrete cosine transform (DCT) to the outputs of the partial matched filter (PMF) for every antenna, the high order components in the transforming domain will be filtered, then the equalgain (EG) combination for the inverse discrete cosine transform (IDCT) reconstructed signal would be done subsequently. Thus, due to the different frequency distribution characteristics between the noise and signals, after EG combination, the energy of signals has almost no loss and the noise energy is greatly reduced. The theoretical analysis and simulation results show that the detection algorithm can effectively improve the signal-to-noise ratio of the captured signal and increase the probability of detection under the same false alarm probability. In addition, it should be pointed out that this method can also be applied to Rayleigh fading channels with moving antenna.
Time-frequency-based methods are proven to be effective for parameter estimation of linear frequency modulation (LFM) signals. The smoothed pseudo Winger-Ville distribution (SPWVD) is used for the parameter estimation of multi-LFM signals, and a method of the SPWVD binarization by a dynamic threshold based on the Otsu algorithm is proposed. The proposed method is effective in the demand for the estimation of different parameters and the unknown signal-to-noise ratio (SNR) circumstance. The performance of this method is confirmed by numerical simulation.
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.
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.
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.
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.
A novel multi-observer passive localization algorithm based on the weighted restricted total least square (WRTLS) is proposed to solve the bearings-only localization problem in the presence of observer position errors. Firstly, the unknown matrix perturbation information is utilized to form the WRTLS problem. Then, the corresponding constrained optimization problem is transformed into an unconstrained one, which is a generalized Rayleigh quotient minimization problem. Thus, the solution can be got through the generalized eigenvalue decomposition and requires no initial state guess process. Simulation results indicate that the proposed algorithm can approach the Cramer-Rao lower bound (CRLB), and the localization solution is asymptotically unbiased.
The state prediction based on the unscented Kalman filter (UKF) for nonlinear stochastic discrete-time systems with linear measurement equation is investigated. Predicting future states by using the information of available measurements is an effective method to solve time delay problems. It not only helps the system operator to perform security analysis, but also allows more time for operator to take better decision in case of emergency. In addition, predictive state can make the system implement real-time monitoring and achieve good robustness. UKF has been popular in state prediction because of its advantages in handling nonlinear systems. However, the accuracy of prediction degrades notably once a filter uses a much longer future prediction. A confidence interval (CI) is proposed to overcome the problem. The advantages of CI are that it provides the information about states coverage, which is useful for treatment-plan evaluation, and it can be directly used to specify the margin to accommodate prediction errors. Meanwhile, the CI of prediction errors can be used to correct the predictive state, and thereby it improves the prediction accuracy. Simulations are provided to demonstrate the effectiveness of the theoretical results.
A Bayesian sequential testing method is proposed to evaluate system reliability index with reliability growth during development. The method develops a reliability growth model of repairable systems for failure censored test, and figures out the approach to determine the prior distribution of the system failure rate by applying the reliability growth model to incorporate the multistage test data collected from system development. Furthermore, the procedure for the Bayesian sequential testing is derived for the failure rate of the exponential life system, which enables the decision to terminate or continue development test. Finally, a numerical example is given to illustrate the efficiency of the proposed model and procedure.
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.
For the robustness problem of open-loop P-type iterative learning control under the influence of measurement noise which is inevitable in actual systems, an adaptive adjustment algorithm of iterative learning nonlinear gain matrix based on error amplitude is proposed and two nonlinear gain functions are given. Then with the help of Bellman-Gronwall lemma, the robustness proof is derived. At last, an example is simulated and analyzed. The results show that when there exists measurement noise, the proposed learning law adjusts the learning gain matrix on line based on error amplitude, thus can make a compromise between learning convergence rate and convergence accuracy to some extent: the fast convergence rate is achieved with high gain in initial learning stage, the strong robustness and high convergence accuracy are achieved at the same time with small gain in the end learning stage, thus better learning results are obtained.
This paper addresses the problem of sensor search scheduling in the complicated space environment faced by the low-earth orbit constellation. Several search scheduling methods based on the commonly used information gain are compared via simulations first. Then a novel search scheduling method in the scenarios of uncertainty observation is proposed based on the global Shannon information gain and beta density based uncertainty model. Simulation results indicate that the beta density model serves a good option for solving the problem of target acquisition in the complicated space environments.
Space time trellis coding (STTC) techniques have been proposed to achieve both diversity and coding gains in multiple input multiple output (MIMO) fading channels. But with more transmit antennas STTCs suffer from the design difficulty and complexity increasing. This paper proposes a scheme, named parallel concatenated space time trellis codes (PC-STTC), to achieve the tradeoff between the performances and complexity of STTCs for a large number of transmit antennas. Simulation results and complexity comparison are provided to demonstrate the performance and superiority of the proposed scheme over conventional schemes in fast fading channels in low signal-to-noise ratio (SNR) regions. And an EXIT (extrinsic information transform) chart is given to analyze the iterative convergence of the proposed scheme. It shows that PC-STTC has better iterative convergence in low SNR regions.
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.
Many Bayesian learning approaches to the multi-layer perceptron (MLP) parameter optimization have been proposed such as the extended Kalman filter (EKF). This paper uses the unscented Kalman particle filter (UPF) to train the MLP in a selforganizing state space (SOSS) model. This involves forming augmented state vectors consisting of all parameters (the weights of the MLP) and outputs. The UPF is used to sequentially update the true system states and high dimensional parameters that are inherent to the SOSS model for the MLP simultaneously. Simulation results show that the new method performs better than traditional optimization methods.
The code tracking loop is a key component for user positioning. The pseudorange information of BeiDou B1 signals has been fused and changed for vector tracking, so a correlation output model for complex scenarios is designed to prevent the propagation of error and valuate the signal performance. The relevant software and hardware factors that affect the output are analyzed. A single channel time-division multiplexing (TDM) method for multicorrelation data extraction is proposed. Statistical characteristics of the correlation output data for both vector and scalar structures are evaluated. Simulation results show that correlation outputs for both structures follow normal or Chi-squared distributions in normal conditions, and the Gamma distribution in harsh conditions. It is shown that a tracking model based on the multi-channel fusion hardly changes the probability distribution of the correlation output in the normal case, but it reduces the ranging error of the code loop, and hence the tracking ability of the code loop for weak signals is improved. Furthermore, vector tracking changes the pseudorange characteristics of channels anytime, and affects the mutual correlation outputs of the code loops in the abnormal case. This study provides a basis for the subsequent design of autonomous integrity algorithms for vector tracking.
Recently, the code division multiple access (CDMA) waveform exists in the large area across the world. However, when using the CDMA system as the illuminator of opportunity for the passive bistatic radar (PBR), there exists interference not only from the base station used as the illuminator of opportunity but also from other base stations with the same frequency. And because in the CDMA system, the signal transmitted by each base station is different, using the direct signal of one base station can not cancel the interference from other base stations. A CDMA-based PBR using an 8-element linear array antenna as both the reference antenna and surveillance antenna is introduced. To deal with the interference in this PBR system, an adaptive temporal cancellation algorithm is used to remove the interference from the base station used as the illuminator of opportunity firstly. And then a robust adaptive beamformer is used to suppress the interference from other base stations. Finally, the preliminary experiment results demonstrate the feasibility of using CDMA signals as a radar waveform.
Opportunistic array radar (OAR) is a new generation radar system based on the stealth of the platform, which can improve the modern radar performance effectively. Designing the orthogonal code sets with low autocorrelation and cross-correlation is a key issue for OAR. This paper proposes a novel hybrid genetic algorithm (HGA) and designs the polyphase orthogonal code sets with low autocorrelation and cross-correlation properties, which can be used in the OAR system. The novel algorithm combines with simulated annealing (SA) and genetic algorithm (GA), adds in keeping best individuals and competition in small scope, and introduces grey correlation evaluation to evaluate fitness function. These avoid the premature convergence problem existed in GA and enhance the global searching capability. At last, the genetic results are optimized to obtain the best solution by using greedy algorithm. The simulation results show that the proposed algorithm is effective for the design of orthogonal phase signals used in OAR systems.
Modeling technology has been introduced into software testing field. However, how to carry through the testing modeling effectively is still a difficulty. Based on combination of simulation modeling technology and embedded real-time software testing method, the process of simulation testing modeling is studied first. And then, the supporting environment of simulation testing modeling is put forward. Furthermore, an approach of embedded real-time software simulation testing modeling including modeling of cross-linked equipments of system under testing (SUT), test case, testing scheduling, and testing system service is brought forward. Finally, the formalized description and execution system of testing models are given, with which we can realize real-time, closed loop, and automated system testing for embedded real-time software.
An efficient compensation scheme combining a timedomain Gaussian elimination (GE) channel estimator and a frequency-domain GE equalizer is proposed for orthogonal frequency division multiplexing (OFDM) systems with frequencydependent in-phase and quadrature-phase (IQ) imbalances at both transmitter and receiver. Compared with the traditional least square and least mean square compensation schemes, the proposed compensation scheme achieves the same bit error rate as the ideal IQ branches by using only two training OFDM symbols instead of about 20 OFDM symbols.
Multi-view laser radar (ladar) data registration in obscure environments is an important research field of obscured target detection from air to ground. There are few overlap regions of the observational data in different views because of the occluder, so the multi-view data registration is rather difficult. Through indepth analyses of the typical methods and problems, it is obtained that the sequence registration is more appropriate, but needs to improve the registration accuracy. On this basis, a multi-view data registration algorithm based on aggregating the adjacent frames, which are already registered, is proposed. It increases the overlap region between the pending registration frames by aggregation and further improves the registration accuracy. The experiment results show that the proposed algorithm can effectively register the multi-view ladar data in the obscure environment, and it also has a greater robustness and a higher registration accuracy compared with the sequence registration under the condition of equivalent operating efficiency.
An adaptive repetitive control scheme is presented for a class of nonlinearly parameterized systems based on the fuzzy basis function network (FBFN). The parameters of the fuzzy rules are tuned with adaptive schemes. To attenuate chattering effectively, the discontinuous control term is approximated by an adaptive PI control structure. The bound of the discontinuous control term is assumed to be unknown and estimated by an adaptive mechanism. Based on the Lyapunov stability theory, an adaptive repetitive control law is proposed to guarantee the closed-loop stability and the tracking performance. By means of FBFNs, which avoid the nonlinear parameterization from entering into the adaptive repetitive control, the controller singularity problem is solved. The proposed approach does not require an exact structure of the system dynamics, and the proposed controller is utilized to control a model of permanent-magnet linear synchronous motor subject to significant disturbances and parameter uncertainties. The simulation results demonstrate the effectiveness of the proposed method.
This article introduces a fleet composition algorithm for a fleet of intermediate carriers, which should deliver a swarm of miniature unmanned aerial vehicles (mini-UAVs) to a mission area. The algorithm is based on the sequential solution of several knapsack problems with various constraints. The algorithm allows both to form an initial set of required types of intermediate carriers, and to generate a fleet of intermediate carriers. The formation of a fleet of intermediate carriers to solve a suppression of enemy air defense (SEAD) problem is presented to illustrate the proposed algorithm.
The elements of network profile are proposed. Based on the network traffic distribution model, the network profile includes the application request rate, the branch transfer probability, the ratio of application requests, and the probability distribution of the requested objects. Based on the evaluation method of network performance reliability, four simulation cases are constructed in OPNET software, and the results show the four elements of profile have impacts on the network reliability.
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.
A new type of estimator is developed for the satellite formation to track and inspect on-orbit targets. The follower satellite in the formation works without relative sensors, and its target pointing commands are derived based on relative orbital dynamics. The centralized estimator based on truth measurement is designed, however, this estimator is proved unstable because of the lack of necessary measurement information. After that, an alternative estimator based on pseudo measurement is designed, and its observability and controllability are analyzed to qualitatively evaluate the convergence performance. Finally, an on-orbit target inspection scenario is numerically simulated to verify the performance of the estimator based on pseudo measurement.
