A new monostatic array system taking advantage of diverse waveforms to improve the performance of underwater tar- get localization is proposed.Unlike the coherent signals between different elements in common active array,the transmitted signals from different elements here are spatially orthogonal waveforms which allow for array processing in the transit mode and result in an extension of array aperture.The mathematical derivation of Capon estimator for this sonar system is described in detail.And the performance of this orthogonal-waveform based sonar is an- alyzed and compared with that of its phased-array counterpart by water tank experiments.Experimental results show that this sonar system could achieve 12 dB?15 dB additional array gain over its phased-array counterpart,which means a doubling of maximum detection range.Moreover,the angular resolution is significantly improved at lower SNR.
In imaging on moving target,it is easy to get space- variant blurred image.In order to recover the image and gain recognizable target,an approach to recover the space-variant blurred image is presented based on image segmentation.Be- cause of motion blur’s convolution process,the pixels of observed image’s target and background will be displaced and piled up to produce two superposition regions.As a result,the neighbor- ing pixels in the superposition regions will have similar grey level change.According to the pixel’s motion-blur character,the target’s blurred edge of superposition region could be detected.Canny operator can be recurred to detect the target edge which parallels the motion blur direction.Then in the segmentation process,the whole target image which has the character of integral convolution between motion blur and real target image can be obtained.At last,the target image is restored by deconvolution algorithms with adding zeros.The restoration result indicates that the approach can effectively solve the kind of problem of space-variant motion blurred image restoration.
In order to improve the measurement-precision of the gyro, the gyro experiment is completed based on gyro servo technology. The error sources of gyro servo technology are analyzed in the process of measurement, and the impact of these error sources on measurement is evaluated. To eliminate interference signal existing in the sampled data of the measurement, a modified wavelet threshold filtering method is presented. The results of the simulation and measurement show that the estimation-precision of the proposed method is improvement remarkably compared with the fast Fourier transform method, and the calculation work is reduced compared with the conventional wavelet threshold filtering methods, furthermore, the phenomenon of a common threshold of "killing" is solved thoroughly.
A new approach to knowledge acquisition in incomplete information system with fuzzy decisions is proposed. In such incomplete information system, the universe of discourse is classified by the maximal tolerance classes, and fuzzy approximations are defined based on them. Three types of relative reducts of maximal tolerance classes are then proposed, and three types of fuzzy decision rules based on the proposed attribute description are defined. The judgment theorems and approximation discernibility functions with respect to them are presented to compute the relative reduct by using Boolean reasoning techniques, from which we can derive optimal fuzzy decision rules from the systems. At last, three types of relative reducts of the system and their computing methods are given.
A novel approach for engineering application to human error probability quantification is presented based on an overview of the existing human reliability analysis methods. The set of performance shaping factors is classified as two subsets of dominant factors and adjusting factors respectively. Firstly, the dominant factors are used to determine the probabilities of three behavior modes. The basic probability and its interval of human error for each behavior mode are given. Secondly, the basic probability and its interval are modified by the adjusting factors, and the total probability of human error is calculated by a total probability formula. Finally, a simple example is introduced, and the consistency and validity of the presented approach are illustrated.
An adaptive algorithm named low complexity phase offset estimation (LC-POE) is proposed for orthogonal frequency division multiplexing (OFDM) signals. Depending on the requirement, the estimation procedure is divided into several scales to accelerate the adaptive convergence speed and ensure the estimation accuracy. The true phase offset is estimated through shrinking the detection range and raising the resolution scale step by step. Both the convergence performance and complexity are discussed in the paper. Simulation results show the effectiveness of the proposed algorithm. The LC-POE algorithm is promising in the application of OFDM systems.
The conventional data envelopment analysis (DEA) measures the relative efficiencies of a set of decision making units with exact values of inputs and outputs. In real-world problems, however, inputs and outputs typically have some levels of fuzziness. To analyze a decision making unit (DMU) with fuzzy input/output data, previous studies provided the fuzzy DEA model and proposed an associated evaluating approach. Nonetheless, numerous deficiencies must still be improved, including the α- cut approaches, types of fuzzy numbers, and ranking techniques. Moreover, a fuzzy sample DMU still cannot be evaluated for the Fuzzy DEA model. Therefore, this paper proposes a fuzzy DEA model based on sample decision making unit (FSDEA). Five evaluation approaches and the related algorithm and ranking methods are provided to test the fuzzy sample DMU of the FSDEA model. A numerical experiment is used to demonstrate and compare the results with those obtained using alternative approaches.
The existing constructions of quasi-cyclic low-density parity-check (QC-LDPC) codes do not consider the problems of small stopping sets and small girth together in the Tanner graph, while their existences will lead to the bit error rate (BER) performance of QC-LDPC codes being much poorer than that of randomly constructed LDPC codes even decoding failure. To solve the problem, some theorems of the specific chosen parity-check matrix of QC-LDPC codes without small stopping sets and small girth are proposed. A novel construction for QC-LDPC codes with long block lengths is presented by multiplying mmin or the multiple of mmin, which is the minimum order of the identity matrix for the chosen parity-check matrix. The simulation results show that the specific chosen parity-check matrix of QC-LDPC codes can effectively avoid specified stopping sets and small girth and exhibit excellent BER performance than random LDPC codes with the same longer codes length.
Based on the target scatterer density, the range-spread target detection of high-resolution radar is addressed in additive non-Gaussian clutter, which is modeled as a spherically invariant random vector. Firstly, for sparse scatterer density, the detection of target scatterer in each range cell is derived, and then an M/K detector is proposed to detect the whole range-spread target. Secondly, an integrating detector is devised to detect a range-spread target with dense scatterer density. Finally, to make the best of the advantages of M/K detector and integrating detector, a robust detector based on scatterer density (DBSD) is designed, which can reduce the probable collapsing loss or quantization error effectively. Moreover, the density decision factor of DBSD is also determined. The formula of the false alarm probability is derived for DBSD. It is proved that the DBSD ensures a constant false alarm rate property. Furthermore, the computational results indicate that the DBSD is robust to different clutter one-lag correlations and target scatterer densities. It is also shown that the DBSD outperforms the existing scatterer-density-dependent detector.
This paper considers the problem of applying data mining techniques to aeronautical field. The truncation method, which is one of the techniques in the aeronautical data mining, can be used to efficiently handle the air-combat behavior data. The technique of air-combat behavior data mining based on the truncation method is proposed to discover the air-combat rules or patterns. The simulation platform of the air-combat behavior data mining that supports two fighters is implemented. The simulation experimental results show that the proposed air-combat behavior data mining technique based on the truncation method is feasible whether in efficiency or in effectiveness.
A dual-channel access mechanism to overcome the drawback of traditional single-channel access mechanism for network-on-chip (NoC) is proposed. In traditional single-channel access mechanism, every Internet protocol (IP) has only one channel to access the on-chip network. When the network is relatively idle, the injection rate is too small to make good use of the network resource. When the network is relatively busy, the ejection rate is so small that the packets in the network cannot leave immediately, and thus the probability of congestion is increased. In the dual-channel access mechanism, the injection rate of IP and the ejection rate of the network are increased by using two optional channels in network interface (NI) and local port of routers. Therefore, the communication performance is improved. Experimental results show that compared with traditional single-channel access mechanism, the proposed scheme greatly increases the throughput and cuts down the average latency with reasonable area increase.
Orthogonal frequency division multiplexing (OFDM) radar with multicarrier phase-coded waveforms has been recently introduced to achieve high range resolution. The conventional method for obtaining the high resolution range profile (HRRP) is based on matched filters. A method of synthesizing HRRP based on the fast Fourier transform (FFT) and decoding is proposed. The mathematical expressions of HRRP are derived by assuming an elementary scenario of point-scattering targets. Based on the characteristic of OFDM multicarrier signals, it mainly analyzes the influence on HRRP exerted by several factors, such as velocity compensation errors, the sampling frequency offset, and so on. The conclusions are significant for the design of the OFDM imaging radar. Finally, the simulation results demonstrate the validity of the conclusions.
The curvature factor of the parallel-track bistatic SAR is range dependent, even without variation of the effective velocity. Accounting for this new characteristic, a parallel-track chirp scaling algorithm (CSA) is derived, by introducing the method of removal of range walk (RRW) in the time domain. Using the RRW before the CSA, this method can reduce the varying range of the curvature factor, without increasing the computation load obviously. The azimuth dependence of the azimuth-FM rate, resulting from the RRW, is compensated by the nonlinear chirp scaling factor. Therefore, the algorithm is extended into stripmap imaging. The realization of the method is presented and is verified by the simulation results.
According to the aggregation method of experts' evaluation information in group decision-making, the existing methods of determining experts' weights based on cluster analysis take into account the expert's preferences and the consistency of expert's collating vectors, but they lack of the measure of information similarity. So it may occur that although the collating vector is similar to the group consensus, information uncertainty is great of a certain expert. However, it is clustered to a larger group and given a high weight. For this, a new aggregation method based on entropy and cluster analysis in group decision-making process is provided, in which the collating vectors are classified with information similarity coefficient, and the experts' weights are determined according to the result of classification, the entropy of collating vectors and the judgment matrix consistency. Finally, a numerical example shows that the method is feasible and effective.
A new incremental support vector machine (SVM) algorithm is proposed which is based on multiple kernel learning. Through introducing multiple kernel learning into the SVM incremental learning, large scale data set learning problem can be solved effectively. Furthermore, different punishments are adopted in allusion to the training subset and the acquired support vectors, which may help to improve the performance of SVM. Simulation results indicate that the proposed algorithm can not only solve the model selection problem in SVM incremental learning, but also improve the classification or prediction precision.
Group decision making problems are investigated with uncertain multiplicative linguistic preference relations. An unbalanced multiplicative linguistic label set is introduced, which can be used by the experts to express their linguistic preference information over alternatives. The uncertain linguistic weighted geometric mean operator is utilized to aggregate all the individual uncertain multiplicative linguistic preference relations into a collective one, and then a simple approach is developed to determine the experts' weights by utilizing the consensus degrees among the individual uncertain multiplicative linguistic preference relations and the collective uncertain multiplicative linguistic preference relations. Furthermore, a practical interactive procedure for group decision making is proposed based on uncertain multiplicative linguistic preference relations, in which a possibility degree formula and a complementary matrix are used to rank the given alternatives. Finally, the proposed procedure is applied to solve the group decision making problem of a manufacturing company searching the best global supplier for one of its most critical parts used in assembling process.
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.
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.
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.
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.
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.
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.
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.
The delayed-state-derivative feedback (DSDF) is introduced into the existing consensus protocol to simultaneously improve the robustness to communication delay and accelerate the convergence speed of achieving the consensus. The frequency-domain analysis, together with the algebra graph theory, is employed to derive the sufficient and necessary condition guaranteeing the average consensus. It is shown that introducing the DSDF with the proper intensity in the existing consensus protocol can improve the robustness to communication delay. By analyzing the effect of DSDF on the closed-loop poles, this paper proves that for a supercritical-delay multi-agent system, this strategy can also accelerate the convergence speed of achieving the consensus with provided the proper intensity of the DSDF. Simulations are provided to demonstrate the effectiveness of the theoretical results.
This paper considers the problem of generating a flight trajectory for a single fixed-wing unmanned combat aerial vehicle (UCAV) performing an air-to-surface multi-target attack (A/SMTA) mission using satellite-guided bombs. First, this problem is formulated as a variant of the traveling salesman problem (TSP), called the dynamic-constrained TSP with neighborhoods (DCTSPN). Then, a hierarchical hybrid approach, which partitions the planning algorithm into a roadmap planning layer and an optimal control layer, is proposed to solve the DCTSPN. In the roadmap planning layer, a novel algorithm based on an updatable probabilistic roadmap (PRM) is presented, which operates by randomly sampling a finite set of vehicle states from continuous state space in order to reduce the complicated trajectory planning problem to planning on a finite directed graph. In the optimal control layer, a collision-free state-to-state trajectory planner based on the Gauss pseudospectral method is developed, which can generate both dynamically feasible and optimal flight trajectories. The entire process of solving a DCTSPN consists of two phases. First, in the offline preprocessing phase, the algorithm constructs a PRM, and then converts the original problem into a standard asymmetric TSP (ATSP). Second, in the online querying phase, the costs of directed edges in PRM are updated first, and a fast heuristic searching algorithm is then used to solve the ATSP. Numerical experiments indicate that the algorithm proposed in this paper can generate both feasible and near-optimal solutions quickly for online purposes.
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.
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%.
Particle swarm optimization (PSO) is a new heuristic algorithm which has been applied to many optimization problems successfully. Attribute reduction is a key studying point of the rough set theory, and it has been proven that computing minimal reduction of decision tables is a non-derterministic polynomial (NP)-hard problem. A new cooperative extended attribute reduction algorithm named Co-PSAR based on improved PSO is proposed, in which the cooperative evolutionary strategy with suitable fitness functions is involved to learn a good hypothesis for accelerating the optimization of searching minimal attribute reduction. Experiments on Benchmark functions and University of California, Irvine (UCI) data sets, compared with other algorithms, verify the superiority of the Co-PSAR algorithm in terms of the convergence speed, efficiency and accuracy for the attribute reduction.
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.
Testing is the premise and foundation of realizing equipment health management (EHM). To address the problem that the static periodic test strategy may cause deficient test or excessive test, a dynamic sequential test strategy (DSTS) for EHM is presented. Considering the situation that equipment health state is not completely observable in reality, a DSTS optimization method based on partially observable semi-Markov decision process (POSMDP) is proposed. Firstly, an equipment health state degradation model is constructed by Markov process, and the control limit maintenance policy is also introduced. Secondly, POSMDP is formulated in great detail. And then, POSMDP is converted to completely observable belief semi-Markov decision process (BSMDP) through belief state. The optimal equation and the corresponding optimal DSTS, which minimize the long-run expected average cost per unit time, are obtained with BSMDP. The results of application in complex equipment show that the proposed DSTS is feasible and effective.
By utilizing the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements of signals received at a number of receivers, a constrained least-square (CLS) algorithm for estimating the position and velocity of a moving source is proposed. By utilizing the Lagrange multipliers technique, the known relation between the intermediate variables and the source location coordinates could be exploited to constrain the solution. And without requiring apriori knowledge of TDOA and FDOA measurement noises, the proposed algorithm can satisfy the demand of practical applications. Additionally, on basis of convolute and polynomial rooting operations, the Lagrange multipliers can be obtained efficiently and robustly allowing real-time implementation and global convergence. Simulation results show that the proposed estimator achieves remarkably better performance than the two-step weighted least square (WLS) approach especially for higher measurement noise level.
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.
A new constant false alarm rate (CFAR) target detector for synthetic aperture radar (SAR) images is developed. For each pixel under test, both the local probability density function (PDF) of the pixel and the clutter PDF in the reference window are estimated by the non-parametric density estimation. The target detector is defined as the mean square error (MSE) distance between the two PDFs. The CFAR detection in SAR images having multiplicative noise is achieved by adaptive kernel bandwidth proportional to the clutter level. In addition, for obtaining a threshold with respect to a given probability of false alarm (PFA), an unsupervised null distribution fitting method with outlier rejection is proposed. The effectiveness of the proposed target detector is demonstrated by the experiment result using the RADATSAT-2 SAR image.
