A new polarization measurement algorithm by using the sum and difference beam differential property of mono-pulse radar is given. Based on the generation mechanism differences between the target scattering and multi-false-target jamming, the signal models of real targets and digital deceptive false target jamming for sum and delta channel are presented. The polarization discrimination parameters are designed, and the discrimination method and its performance are discussed. This novel method does not need the accurate estimation of the absolute value of full target polarization scattering matrix, but only requires the relative estimation of the orthogonal polarized component of the targets. Without the need to add additional polarization channels, the proposed method is more suitable for engineering realization. The simulation experiment verifies that the correctly identifying probability can be better than 90%.
An ant colony optimization (ACO)-simulated annealing (SA)-based algorithm is developed for the target assignment problem (TAP) in the air defense (AD) command and control (C2) system of surface to air missile (SAM) tactical unit. The accomplishment process of target assignment (TA) task is analyzed. A firing advantage degree (FAD) concept of fire unit (FU) intercepting targets is put forward and its evaluation model is established by using a linear weighted synthetic method. A TA optimization model is presented and its solving algorithms are designed respectively based on ACO and SA. A hybrid optimization strategy is presented and developed synthesizing the merits of ACO and SA. The simulation examples show that the model and algorithms can meet the solving requirement of TAP in AD combat.
Perturbation and robust controllability of the singular distributed parameter control system are discussed via functional analysis and the theory of GE-semigroup in Hilbert space. The perturbation principle of GE-semigroup and the sufficient condition concerning the robust controllability of the singular distributed parameter control system are obtained, in which the controllability for singular distributed parameter control system is not destroyed, if we perturb the equation by small bounded linear operator.
To solve discrete optimization difficulty of the spectrum allocation problem, a membrane-inspired quantum shuffled frog leaping (MQSFL) algorithm is proposed. The proposed MQSFL algorithm applies the theory of membrane computing and quantum computing to the shuffled frog leaping algorithm, which is an effective discrete optimization algorithm. Then the proposed MQSFL algorithm is used to solve the spectrum allocation problem of cognitive radio systems. By hybridizing the quantum frog colony optimization and membrane computing, the quantum state and observation state of the quantum frogs can be well evolved within the membrane structure. The novel spectrum allocation algorithm can search the global optimal solution within a reasonable computation time. Simulation results for three utility functions of a cognitive radio system are provided to show that the MQSFL spectrum allocation method is superior to some previous spectrum allocation algorithms based on intelligence computing.
The lattice-reduction (LR) has been developed to improve the performance of the zero-forcing (ZF) precoder in multiple input multiple output (MIMO) systems. Under the assumptions of uncorrelated flat fading channel model and perfect channel state information at the transmitter (CSIT), an LR-aided ZF precoder is able to collect the full transmit diversity. With the complex Lenstra-Lenstra-Lov´asz (LLL) algorithm and limited feedforward structure, an LR-aided linear minimum-mean-square-error (LMMSE) precoder for spatial correlated MIMO channels and imperfect CSIT is proposed to achieve lower bit error rate (BER). Assuming a time division duplexing (TDD) MIMO system, correlated block flat fading channel and LMMSE uplink channel estimator, it is proved that the proposed LR-aided LMMSE precoder can also obtain the full transmit diversity through an analytical approach. Furthermore, the simulation results show that with the quadrature phase shift keying (QPSK) modulation at the transmitter, the uncoded and coded BERs of the LR-aided LMMSE precoder are lower than that of the traditional LMMSE precoder respectively when Eb/N0 is greater than 10 dB and 12 dB at all correlation coefficients.
Stability analysis and stabilization for discrete-time singular delay systems are addressed, respectively. Firstly, a sufficient condition for regularity, causality and stability for discrete-time singular delay systems is derived. Then, by applying the skill of matrix theory, the state feedback controller is designed to guarantee the closed-loop discrete-time singular delay systems to be regular, casual and stable. Finally, numerical examples are given to demonstrate the effectiveness of the proposed method.
This paper deals with the problem of H∞ fault estimation for linear time-delay systems in finite frequency domain. First a generalized coordinate change is applied to the original system such that in the new coordinates all the time-delay terms are injected by the system’s input and output. Then an observer-based H∞ fault estimator with input and output injections is proposed for fault estimation with known frequency range. With the aid of Generalized Kalman-Yakubovich-Popov lemma, sufficient conditions on the existence of the H∞ fault estimator are derived and a solution to the observer gain matrices is obtained by solving a set of linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.
In rough communication, because each agent has a different language and can not provide precise communication to each other, the concept translated among multi-agents will loss some information, and this results in a less or rougher concept. With different translation sequences the amount of the missed knowledge is varied. The λ−optimal translation sequence of rough communication, which concerns both every agent and the last agent taking part in rough communication to get information as much as he (or she) can, is given. In order to get the λ−optimal translation sequence, a genetic algorithm is used. Analysis and simulation of the algorithm demonstrate the effectiveness of the approach.
This paper addresses color filter array (CFA) color reproduction problem where the aim is to utilize an image captured by the CFA to produce an image with full color information. First, conventional subband synthesis based color reproduction techniques do not consider the noise during image acquisition and assume that the CFA data are noiseless. To tackle the noisy CFA data, a novel approach is proposed by inserting a subband denoising scheme into the conventional subband synthesis framework. Second, conventional subband synthesis based techniques exploit the decimated wavelet transform that is not shift-invariant and could result in ringing artifacts in the result. To alleviate these artifacts, the directional cycle-spinning (DCS) technique is exploited. Furthermore, a new cycle-spinning pattern is proposed according to the sampling pattern of the Bayer CFA data. Extensive experiments are conducted to demonstrate that the proposed approach outperforms several approaches.
It is of great significance to rapidly detect targets in large-field remote sensing images, with limited computation resources. Employing relative achievements of visual attention in perception psychology, this paper proposes a hierarchical attention based model for target detection. Specifically, at the preattention stage, before getting salient regions, a fast computational approach is applied to build a saliency map. After that, the focus of attention (FOA) can be quickly obtained to indicate the salient objects. Then, at the attention stage, under the FOA guidance, the high-level visual features of the region of interest are extracted in parallel. Finally, at the post-attention stage, by integrating these parallel and independent visual attributes, a decision-template based classifier fusion strategy is proposed to discriminate the task-related targets from the other extracted salient objects. For comparison, experiments on ship detection are done for validating the effectiveness and feasibility of the proposed model.
For multi-agent systems based on the local information, the agents automatically converge to a common consensus state and the convergence speed is determined by the algebraic connectivity of the communication network. To study fast consensus seeking problems of multi-agent systems in undirected networks, a consensus protocol is proposed which considers the average information of the agents’ states in a certain time interval, and a consensus convergence criterion for the system is obtained. Based on the frequency-domain analysis and algebra graph theory, it is shown that if the time interval is chosen properly, then requiring the same maximum control effort the proposed protocol reaches consensus faster than the standard consensus protocol. Simulations are provided to demonstrate the effectiveness of these theoretical results.
For the joint time difference of arrival (TDOA) and angle of arrival (AOA) location scene, two methods are proposed based on the rectangular coordinates and the polar coordinates, respectively. The problem is solved perfectly by calculating the target position with the joint TDOA and AOA location. On the condition of rectangular coordinates, first of all, it figures out the radial range between target and reference stations, then calculates the location of the target. In the case of polar coordinates, first of all, it figures out the azimuth between target and reference stations, then figures out the radial range between target and reference stations, finally obtains the location of the target. Simultaneously, simulation analyses show that the theoretical analysis is correct, and the proposed methods also provide the application of the joint TDOA and AOA location algorithm with the theoretical basis.
An adaptive fuzzy sliding mode control (AFSMC) approach is proposed for a robotic airship. First, the mathematical model of an airship is derived in the form of a nonlinear control system. Second, an AFSMC approach is proposed to design the attitude control system of airship, and the global stability of the closed-loop system is proved by using the Lyapunov stability theorem. Finally, simulation results verify the effectiveness and robustness of the proposed control approach in the presence of model uncertainties and external disturbances.
Failure mode and effect analysis (FMEA) is a preventative risk evaluation method used to evaluate and eliminate failure modes within a system. However, the traditional FMEA method exhibits many deficiencies that pose challenges in practical applications. To improve the conventional FMEA, many modified FMEA models have been suggested. However, the majority of them inadequately address consensus issues and focus on achieving a complete ranking of failure modes. In this research, we propose a new FMEA approach that integrates a two-stage consensus reaching model and a density peak clustering algorithm for the assessment and clustering of failure modes. Firstly, we employ the interval 2-tuple linguistic variables (I2TLVs) to express the uncertain risk evaluations provided by FMEA experts. Then, a two-stage consensus reaching model is adopted to enable FMEA experts to reach a consensus. Next, failure modes are categorized into several risk clusters using a density peak clustering algorithm. Finally, the proposed FMEA is illustrated by a case study of load-bearing guidance devices of subway systems. The results show that the proposed FMEA model can more easily to describe the uncertain risk information of failure modes by using the I2TLVs; the introduction of an endogenous feedback mechanism and an exogenous feedback mechanism can accelerate the process of consensus reaching; and the density peak clustering of failure modes successfully improves the practical applicability of FMEA.
To improve the agility, dynamics, composability, reusability, and development efficiency restricted by monolithic federation object model (FOM), a modular FOM is proposed by high level architecture (HLA) evolved product development group. This paper reviews the state-of-the-art of HLA evolved modular FOM. In particular, related concepts, the overall impact on HLA standards, extension principles, and merging processes are discussed. Also permitted and restricted combinations, and merging rules are provided, and the influence on HLA interface specification is given. The comparison between modular FOM and base object model (BOM) is performed to illustrate the importance of their combination. The applications of modular FOM are summarized. Finally, the significance to facilitate compoable simulation both in academia and practice is presented and future directions are pointed out.
Mobile ad hoc networking (MANET) has become an exciting and important technology in recent years, because of the rapid proliferation of wireless devices. Mobile ad hoc networks is highly vulnerable to attacks due to the open medium, dynamically changing network topology, cooperative algorithms, and lack of centralized monitoring and management point. The traditional way of protecting networks with firewalls and encryption software is no longer sufficient and effective for those features. A distributed intrusion detection approach based on timed automata is given. A cluster-based detection scheme is presented, where periodically a node is elected as the monitor node for a cluster. These monitor nodes can not only make local intrusion detection decisions, but also cooperatively take part in global intrusion detection. And then the timed automata is constructed by the way of manually abstracting the correct behaviours of the node according to the routing protocol of dynamic source routing (DSR). The monitor nodes can verify the behaviour of every nodes by timed automata, and validly detect real-time attacks without signatures of intrusion or trained data. Compared with the architecture where each node is its own IDS agent, the approach is much more efficient while maintaining the same level of effectiveness. Finally, the intrusion detection method is evaluated through simulation experiments.
Pose manifold and tensor decomposition are used to represent the nonlinear changes of multi-view faces for pose estimation, which cannot be well handled by principal component analysis or multilinear analysis methods. A pose manifold generation method is introduced to describe the nonlinearity in pose subspace. And a nonlinear kernel based method is used to build a smooth mapping from the low dimensional pose subspace to the high dimensional face image space. Then the tensor decomposition is applied to the nonlinear mapping coefficients to build an accurate multi-pose face model for pose estimation. More importantly, this paper gives a proper distance measurement on the pose manifold space for the nonlinear mapping and pose estimation. Experiments on the identity unseen face images show that the proposed method increases pose estimation rates by 13.8% and 10.9% against principal component analysis and multilinear analysis based methods respectively. Thus, the proposed method can be used to estimate a wide range of head poses.
Local diversity AdaBoost support vector machine (LDAB-SVM) is proposed for large scale dataset classification problems. The training dataset is split into several blocks firstly, and some models based on these dataset blocks are built. In order to obtain a better performance, AdaBoost is used in each model building. In the boosting iteration step, the component learners which have higher diversity and accuracy are collected via the kernel parameters adjusting. Then the local models via voting method are integrated. The experimental study shows that LDAB-SVM can deal with large scale dataset efficiently without reducing the performance of the classifier.
Based on the fabricated 12-element cavity-backed microstrip sector cylinder array, a novel hybrid alternate projection algorithm (HAPA), which combines analytical method with numerical techniques effectively, is proposed for synthesizing the pattern of practical conformal array. The algorithm applies the variable direction aperture projection method with mutual coupling correction techniques to provide the good initial excitations of elements to the enhanced alternate projection algorithm (EAPA). In order to do further optimization, which improves the convergent speed of the algorithm significantly. Finally, the HAPA has been applied to the fabricated sector cylinder array with mutual coupling considered. The results of synthesized patterns, such as low sidelobe with null points formed pattern, beam scanning with low sidelobe pattern and the shaped beam pattern are presented. It demonstrates the validity of HAPA in practical conformal array synthesis.
Systems with a hidden degradation process are pervasive in the real world. Degrading critical components will undermine system performance and pose potential failures in the future. Prognostic aims at predicting potential failures before it evolves into faults. A prognostic procedure based on expectation maximization and unscented Kalman filter is proposed. System state, sensor measurement and hidden degradation process are viewed as data (incomplete or missing) in the expectation maximization method. System state and hidden degradation process are estimated by a unscented Kalman filter upon sensor measurements. Component-specific parameters in a degradation process are identified on the estimation of the degradation process. Residual life is measured by the median of estimated residual life distribution. The proposed procedure is verified by simulations on a first-order capacitor-resistance circuit with degrading resistance. Residual life estimation consists conservatively with the trend and is evaluated in terms of relative errors. Simulation results are reasonable. The proposed prognostic method expects applications in practice.
An implementation of adaptive filtering, composed of an unsupervised adaptive filter (UAF), a multi-step forward linear predictor (FLP), and an unsupervised multi-step adaptive predictor (UMAP), is built for suppressing impulsive noise in unknown circumstances. This filtering scheme, called unsupervised robust adaptive filter (URAF), possesses a switching structure, which ensures the robustness against impulsive noise. The FLP is used to detect the possible impulsive noise added to the signal. If the signal is “impulse-free”, the filter UAF can estimate the clean signal. If there exists impulsive noise, the impulse corrupted samples are replaced by predicted ones from the FLP, and then the UMAP estimates the clean signal. Both the simulation and experimental results show that the URAF has a better rate of convergence than the most recent universal filter, and is effective to restrict large disturbance like impulsive noise when the universal filter fails.
The technique of terahertz pulses generated from the photoconductive switches has been applied in the ultrafast electrical pulse metrology recently. A lumped-element theoretical model is established to describe the performance of the LT-GaAs ultrafast photoconductive switch used in the ultrafast pulse standard. The carrier transport processes of the photoexcited semiconductor, the attenuation and dispersion during terahertz pulse propagating are considered in the theoretical model. According to the experimental parameters, the waveforms of the generated terahertz pulses are calculated under optical excitations with different wavelengths of 840 nm and 450 nm, respectively. And comparisons between the theoretical results and the experimental results are carried out.
A new fuzzy support vector machine algorithm with dual membership values based on spectral clustering method is proposed to overcome the shortcoming of the normal support vector machine algorithm, which divides the training datasets into two absolutely exclusive classes in the binary classification, ignoring the possibility of “overlapping” region between the two training classes. The proposed method handles sample “overlap” efficiently with spectral clustering, overcoming the disadvantages of over-fitting well, and improving the data mining efficiency greatly. Simulation provides clear evidences to the new method.
In multi-user multiple input multiple output (MU-MIMO) systems, the outdated channel state information at the transmitter caused by channel time variation has been shown to greatly reduce the achievable ergodic sum capacity. A simple yet effective solution to this problem is presented by designing a channel extrapolator relying on Karhunen-Loeve (KL) expansion of timevarying channels. In this scheme, channel estimation is done at the base station (BS) rather than at the user terminal (UT), which thereby dispenses the channel parameters feedback from the UT to the BS. Moreover, the inherent channel correlation and the parsimonious parameterization properties of the KL expansion are respectively exploited to reduce the channel mismatch error and the computational complexity. Simulations show that the presented scheme outperforms conventional schemes in terms of both channel estimation mean square error (MSE) and ergodic capacity
Task scheduling for electro-magnetic detection satellite is a typical combinatorial optimization problem. The count of constraints that need to be taken into account is of large scale. An algorithm combined integer programming with constraint programming is presented. This algorithm is deployed in this problem through two steps. The first step is to decompose the original problem into master and sub-problem using the logic-based Benders decomposition; then a circus combines master and sub-problem solving process together, and the connection between them is general Benders cut. This hybrid algorithm is tested by a set of derived experiments. The result is compared with corresponding outcomes generated by the strength Pareto evolutionary algorithm and the pure constraint programming solver——GECODE, which is an open source software. These tests and comparisons yield promising effect.
Sparse representation has recently been proved to be a powerful tool in image processing and object recognition. This paper proposes a novel small target detection algorithm based on this technique. By modelling a small target as a linear combination of certain target samples and then solving a sparse 0-minimization problem, the proposed apporach successfully improves and optimizes the small target representation with innovation. Furthermore, the sparsity concentration index (SCI) is creatively employed to evaluate the coefficients of each block representation and simpfy target identification. In the detection frame, target samples are firstly generated to constitute an over-complete dictionary matrix using Gaussian intensity model (GIM), and then sparse model solvers are applied to finding sparse representation for each sub-image block. Finally, SCI lexicographical evalution of the entire image incorparates with a simple threshold locate target position. The effectiveness and robustness of the proposed algorithm are demonstrated by the exprimental results.
The mode switching between spatial multiplexing (SM) and space-time block code (STBC) diversity is investigated for the multiple-input multiple-output (MIMO) automatic repeat request (ARQ) system. Five important practical factors are considered in the proposed switching scheme: transmit correlation, ARQ technique, packet loss probability (PLP) constraint, discrete rate transmission (DRT) and channel coding. Under the spatially correlated channel, the distributions of the post signal-to-interference-plusnoise ratio (SINR) for the SM mode and the STBC mode are obtained by using Gamma approximations. Then this paper derives the closed-form expressions of the PLP and the throughput for different modes when the ARQ technique is employed, based on which the mode switching algorithm is proposed to improve the spectral efficency. In the simulation, the correction of the expressions is first verified. Then, the significant gain observed by the proposed algorithm is presented. Since the switching point is the key parameter to implement the mode switching, this paper also shows how the switching point is affected by the practical factors considered.
The problem of the quantized dynamic output feedback controller design for networked control systems is mainly discussed. By using the quantized information of the system measurement output and the control input, a novel networked control system model is described. This model includes many networkinduced features, such as multi-rate sampled-data, quantized signal, time-varying delay and packet dropout. By constructing suitable Lyapunov-Krasovskii functional, a less conservative stabilization criterion is established in terms of linear matrix inequalities. The quantized control strategy involves the updating values of the quantizer parameters μi(i = 1, 2)(μi take on countable sets of values which dependent on the information of the system measurement outputs and the control inputs). Furthermore, a numerical example is given to illustrate the effectiveness of the proposed method.
The learning Bayesian network (BN) structure from data is an NP-hard problem and still one of the most exciting challenges in the machine learning. In this work, a novel algorithm is presented which combines ideas from local learning, constraintbased, and search-and-score techniques in a principled and effective way. It first reconstructs the junction tree of a BN and then performs a K2-scoring greedy search to orientate the local edges in the cliques of junction tree. Theoretical and experimental results show the proposed algorithm is capable of handling networks with a large number of variables. Its comparison with the well-known K2 algorithm is also presented.
On the basis of scale invariant feature transform (SIFT) descriptors, a novel kind of local invariants based on SIFT sequence scale (SIFT-SS) is proposed and applied to target classification. First of all, the merits of using an SIFT algorithm for target classification are discussed. Secondly, the scales of SIFT descriptors are sorted by descending as SIFT-SS, which is sent to a support vector machine (SVM) with radial based function (RBF) kernel in order to train SVM classifier, which will be used for achieving target classification. Experimental results indicate that the SIFT-SS algorithm is efficient for target classification and can obtain a higher recognition rate than affine moment invariants (AMI) and multi-scale auto-convolution (MSA) in some complex situations, such as the situation with the existence of noises and occlusions. Moreover, the computational time of SIFT-SS is shorter than MSA and longer than AMI.
A self-adaptive large neighborhood search method for scheduling n jobs on m non-identical parallel machines with multiple time windows is presented. The problems’ another feature lies in oversubscription, namely not all jobs can be scheduled within specified scheduling horizons due to the limited machine capacity. The objective is thus to maximize the overall profits of processed jobs while respecting machine constraints. A first-infirst-out heuristic is applied to find an initial solution, and then a large neighborhood search procedure is employed to relax and reoptimize cumbersome solutions. A machine learning mechanism is also introduced to converge on the most efficient neighborhoods for the problem. Extensive computational results are presented based on data from an application involving the daily observation scheduling of a fleet of earth observing satellites. The method rapidly solves most problem instances to optimal or near optimal and shows a robust performance in sensitive analysis.
The pruning algorithms for sparse least squares support vector regression machine are common methods,and easily com- prehensible,but the computational burden in the training phase is heavy due to the retraining in performing the pruning process, which is not favorable for their applications.To this end,an im- proved scheme is proposed to accelerate sparse least squares support vector regression machine.A major advantage of this new scheme is based on the iterative methodology,which uses the previous training results instead of retraining,and its feasibility is strictly verified theoretically.Finally,experiments on bench- mark data sets corroborate a significant saving of the training time with the same number of support vectors and predictive accuracy compared with the original pruning algorithms,and this speedup scheme is also extended to classification problem.
