This paper links parallel factor (PARAFAC) analysis to the problem of nominal direction-of-arrival (DOA) estimation for coherently distributed (CD) sources and proposes a fast PARAFACbased algorithm by establishing the trilinear PARAFAC model. Relying on the uniqueness of the low-rank three-way array decomposition and the trilinear alternating least squares regression, the proposed algorithm achieves nominal DOA estimation and outperforms the conventional estimation of signal parameter via rotational technique CD (ESPRIT-CD) and propagator method CD (PM-CD) methods in terms of estimation accuracy. Furthermore, by means of the initialization via the propagator method, this paper accelerates the convergence procedure of the proposed algorithm with no estimation performance degradation. In addition, the proposed algorithm can be directly applied to the multiple-source scenario, where sources have different angular distribution shapes. Numerical simulation results corroborate the effectiveness and superiority of the proposed fast PARAFAC-based algorithm.

A new sparse channel estimation method of orthogonal frequency division multiplexing (OFDM) system based on intercarrier interference (ICI) self-cancellation is investigated. Firstly, based on the characteristic that the ICI generated by a subcarrier to the two adjacent subcarriers is approximately equal, a data pair with opposite sign and equal magnitude is modulated onto two adjacent subcarriers as pilot pair to eliminate the effect of ICI on pilots. Secondly, a new OFDM channel estimation model based on linear time-varying (LTV) model and compressed sensing (CS) is constructed, which obtains the mean of the gains of the multipath. Finally, a pilot pair optimization algorithm based on two layers loop is used to realize the minimization of the mutual coherence of the measurement matrix. For time-varying channel scenes with different numbers or delay of multipath and maximum Doppler frequency shift, the performances of several channel estimation methods are verified by simulation. The result shows that the new method has obvious advantage in both the performance of the channel estimation and the spectral efficiency.

This paper numerically investigates the radio wave scattering by the artificial acoustic disturbance in the atmospheric boundary layer. The numerical model is based on the finitedifference time-domain (FDTD) method for radio wave propagation and fluid simulation for atmospheric disturbance by acoustics waves. The characteristics of radio wave scattering propagation in the artificial acoustic perturbations are investigated by this numerical model. The numerical simulation results demonstrate that the radio wave propagation scattered by acoustic scatterer has the characteristic of forward tropospheric scatter. When the radio waves are scattered, they distribute in all directions; a majority of radio waves continues to propagate along the original direction, and only a small part of the energy is scattered. For the same acoustic scatterer, if we merely change the radio wave emission elevation, the horizontal spans of forward scattering radio wave packets centers gradually decrease with the increasing of emission elevations; and the energy of wave packets increases firstly and then decreases with launching elevation, reaching the maximum at a certain angle. If we merely change the wave emitting position, the horizontal spans decrease with the increasing of emission positions, and the energy of wave packets also increases firstly and then decreases with launch position, reaching the maximum at a certain position. This approach can be very promising for atmospheric scatter communications.

Wireless sensor networks (WSN) provide an approach to collecting distributed monitoring data and transmiting them to the sink node. This paper proposes a WSN-based multi-hop network infrastructure, to increase network lifetime by optimizing the routing strategy. First, a network model is established, an operating control strategy is devised, and energy consumption characteristics are analyzed. Second, a fast route-planning algorithm is proposed to obtain the original path that takes into account the remaining energy of communicating nodes and the amount of energy consumed in data transmission. Next, considering the amount of energy consumed by an individual node and the entire network, a criterion function is established to describe node performance and to evaluate data transmission ability. Finally, a route optimizing algorithm is proposed to increase network lifetime by adjusting the transmission route in protection of the weak node (the node with low transmission ability). Simulation and comparison experimental results demonstrate the good performance of the proposed algorithms to increase network lifetime.

In this paper, a new partial transmit sequence (PTS) scheme with low computational complexity is proposed for the problems of high computational complexity in the conventional PTS method. By analyzing the relationship of candidate sequences in the PTS method under the interleaved partition method, it has been discovered that some candidate sequences generated by phase factor sequences have the same peak average power ratio (PAPR). Hence, phase factor sequences can be optimized to reduce their searching times. Then, the computational process of generating candidate sequences can be simplified by improving the utilization of data and minimizing the calculations of complex multiplication. The performance analysis shows that, compared with the conventional PTS scheme, the proposed approach significantly decreases the computational complexity and has no loss of PAPR performance.

In tensor theory, the parallel factorization (PARAFAC) decomposition expresses a tensor as the sum of a set of rank-1 tensors. By carrying out this numerical decomposition, mixed sources can be separated or unknown system parameters can be identified, which is the so-called blind source separation or blind identification. In this paper we propose a numerical PARAFAC decomposition algorithm. Compared to traditional algorithms, we speed up the decomposition in several aspects, i.e., search direction by extrapolation, suboptimal step size by Gauss-Newton approximation, and linear search by n steps. The algorithm is applied to polarization sensitive array parameter estimation to show its usefulness. Simulations verify the correctness and performance of the proposed numerical techniques.

The investigation of the effect of electrical and mechanical errors on the performance of a large active phased array antenna is studied. These errors can decrease the antenna performance, for instance, the gain reduction, side lobe level enhancement, and incorrect beam direction. In order to improve the performance of the antenna in the presence of these errors, phase error correction of large phased array antennas using the genetic algorithm (GA) is implemented. By using the phase compensation method, the antenna overall radiation pattern is recovered close to the ideal radiation pattern without error. By applying the simulation data to a 32×40 array of elements with a square grid at the frequency of S-band and measurement of the radiation pattern, the effectiveness of the proposed method is verified.

In view of the low performance of adaptive asymmetric joint diagonalization (AAJD), especially its failure in tracking high maneuvering targets, an adaptive asymmetric joint diagonalization with deflation (AAJDd) algorithm is proposed. The AAJDd algorithm improves performance by estimating the direction of departure (DOD) and direction of arrival (DOA) directly, avoiding the reuse of the previous moment information in the AAJD algorithm. On this basis, the idea of sequential estimation of the principal component is introduced to turn the matrix operation into a constant operation, reducing the amount of computation and speeding up the convergence. Meanwhile, the eigenvalue is obtained, which can be used to estimate the number of targets. Then, the estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm is improved to realize the automatic matching and association of DOD and DOA. The simulation results show that the AAJDd algorithm has higher tracking performance than the AAJD algorithm, especially when the high maneuvering target is tracked. The efficiency of the proposed method is verified.

For coping with the multiple target tracking in the presence of complex time-varying environments and unknown target information, a time resource management scheme based on chance-constraint programming (CCP) employing fuzzy logic priority is proposed for opportunistic array radar (OAR). In this scheme, the total beam illuminating time is minimized by effective time resource allocation so that the desired tracking performance is achieved. Meanwhile, owing to the randomness of radar cross section (RCS), the CCP is used to balance tracking accuracy and time resource conditioned on the specified confidence level. The adaptive fuzzy logic prioritization, imitating the human decision-making process for ranking radar targets, can realize the full potential of radar. The Bayesian Cramer-Rao lower bound (BCRLB) provides ′ us with a low bound of localization estimation root-mean-square error (RMSE), and equally important, it can be calculated predictively. Consequently, it is employed as an optimization criterion for the time resource allocation scheme. The stochastic simulation is integrated into the genetic algorithm (GA) to compose a hybrid intelligent optimization algorithm to solve the CCP optimization problem. The simulation results show that the time resource is saved strikingly and the radar performance is also improved.

Manned combat aerial vehicles (MCAVs), and unmanned combat aerial vehicles (UCAVs) together form a cooperative engagement system to carry out operational mission, which will be a new air engagement style in the near future. On the basis of analyzing the structure of the MCAV/UCAV cooperative engagement system, this paper divides the unique system into three hierarchical levels, respectively, i.e., mission level, task-cluster level and task level. To solve the formation and adjustment problem of the latter two levels, three corresponding mathematical models are established. To solve these models, three algorithms called quantum artificial bee colony (QABC) algorithm, greedy strategy (GS) and two-stage greedy strategy (TSGS) are proposed. Finally, a series of simulation experiments are designed to verify the effectiveness and superiority of the proposed algorithms.

A method is proposed to resolve the typical problem of air combat situation assessment. Taking the one-to-one air combat as an example and on the basis of air combat data recorded by the air combat maneuvering instrument, the problem of air combat situation assessment is equivalent to the situation classification problem of air combat data. The fuzzy C-means clustering algorithm is proposed to cluster the selected air combat sample data and the situation classification of the data is determined by the data correlation analysis in combination with the clustering results and the pilots' description of the air combat process. On the basis of semi-supervised naive Bayes classifier, an improved algorithm is proposed based on data classification confidence, through which the situation classification of air combat data is carried out. The simulation results show that the improved algorithm can assess the air combat situation effectively and the improvement of the algorithm can promote the classification performance without significantly affecting the efficiency of the classifier.

The classic data envelopment analysis (DEA) model is used to evaluate decision-making units' (DMUs) efficiency under the assumption that all DMUs are evaluated with the same criteria setting. Recently, new researches begin to focus on the efficiency analysis of non-homogeneous DMU arose by real practices such as the evaluation of departments in a university, where departments argue for the adoption of different criteria based on their disciplinary characteristics. A DEA procedure is proposed in this paper to address the efficiency analysis of two non-homogeneous DMU groups. Firstly, an analytical framework is established to compromise diversified input and output (IO) criteria from two nonhomogenous groups. Then, a criteria fusion operation is designed to obtain different DEA analysis strategies. Meanwhile, Friedman test is introduced to analyze the consistency of all efficiency results produced by different strategies. Next, ordered weighted averaging (OWA) operators are applied to integrate different information to reach final conclusions. Finally, a numerical example is used to illustrate the proposed method. The result indicates that the proposed method relaxes the restriction of the classical DEA model, and can provide more analytical flexibility to address different decision analysis scenarios arose from practical applications.

In military service joint operations, when there are more operational forces, more multifarious materials are consumed, the support is more complex and fuzzy, the deployment of personnel is more rapid, and the support provided by wartime military material support powers can be more effective. When the principles, requirements, influencing factors and goals of military material support forces are deployed in wartime, an evaluation indicator system is established. Thus, a new combined empowerment method based on an analytic hierarchy process (AHP) is developed to calculate the subjective weights, and the rough entropy method is used to calculate the objective weights. Combination weights can be obtained by calculating the weight preference coefficient error, which is determined by combining the cooperative game method and the minimum deviation into objectives. This approach can determine the grey relation projection coefficient and synthesize the measure scheme superiority to finally optimize the deployment plan using the grey relation projection decision-making method. The results show that the method is feasible and effective; it can provide a more scientific and practical decision-making basis for the military material support power deployment in wartime.

A global approximation based adaptive radial basis function (RBF) neural network control strategy is proposed for the trajectory tracking control of supercavitating vehicles (SV). A nominal model is built firstly with the unknown disturbance. Next, the control scheme is established consisting of a computed torque controller (CTC) for the practical vehicle and an RBF neural network controller to estimate model error between the practical vehicle and the nominal model. The network weights are adapted by employing a Lyapunov-based design. Then it is shown by the Lyapunov theory that the trajectory tracking errors asymptotically converge to a small neighborhood of zero. The control performance of the proposed controller is illustrated by simulation.

Micro-satellite cluster enables a whole new class of missions for communications, remote sensing, and scientific research for both civilian and military purposes. Synchronizing the time of the satellites in a cluster is important for both cluster sensing capabilities and its autonomous operating. However, the existing time synchronization methods are not suitable for microsatellite cluster, because it requires too many human interventions and occupies too much ground control resource. Although, data post-process may realize the equivalent time synchronization, it requires processing time and powerful computing ability on the ground, which cannot be implemented by cluster itself. In order to autonomously establish and maintain the time benchmark in a cluster, we propose a compact time difference compensation system (TDCS), which is a kind of time control loop that dynamically adjusts the satellite reference frequency according to the time difference. Consequently, the time synchronization in the cluster can be autonomously achieved on-orbit by synchronizing the clock of other satellites to a chosen one's. The experimental result shows that the standard deviation of time synchronization is about 102 ps when the carrier to noise ratio (CNR) is 95 dBHz, and the standard deviation of corresponding frequency difference is approximately 0.36 Hz.

To ensure multiple unmanned aerial vehicles (UAVs) reach stable formation quickly, a cooperative guidance law based on the back-stepping-like approach is designed in this paper. Adopting the guidance mechanism of virtue leader vehicle, the dynamic equation of tracking errors for each UAV is built. The communication interactive relationships are described based on graph theory, and the guidance law for formation reaching is obtained by the back-stepping-like approach. The formation stability is analyzed by constructing an appropriate Lyapunov function. The simulation results have shown that this guidance and control law can make each UAV converge to the trajectory of the virtue leader ultimately, and has the quicker rate of convergence and lower tracking error.

The stochastic convergence of the cubature Kalman filter with intermittent observations (CKFI) for general nonlinear stochastic systems is investigated. The Bernoulli distributed random variable is employed to describe the phenomenon of intermittent observations. According to the cubature sample principle, the estimation error and the error covariance matrix (ECM) of CKFI are derived by Taylor series expansion, respectively. Afterwards, it is theoretically proved that the ECM will be bounded if the observation arrival probability exceeds a critical minimum observation arrival probability. Meanwhile, under proper assumption corresponding with real engineering situations, the stochastic stability of the estimation error can be guaranteed when the initial estimation error and the stochastic noise terms are sufficiently small. The theoretical conclusions are verified by numerical simulations for two illustrative examples; also by evaluating the tracking performance of the optical-electric target tracking system implemented by CKFI and unscented Kalman filter with intermittent observations (UKFI) separately, it is demonstrated that the proposed CKFI slightly outperforms the UKFI with respect to tracking accuracy as well as real time performance.

Composite nonlinear feedback (CNF) control technique for tracking control problems is extended to the output regulation problem of singular linear systems with input saturation. A state feedback CNF control law and an output feedback CNF control law are constructed respectively for the output regulation problem of singular linear systems with input saturation. It is shown that the output regulation problem by CNF control is solvable under the same solvability conditions of the output regulation problem by linear control. However, with the virtue of the CNF control, the transient performance of the closed-loop system can be improved by carefully designing the linear part and the nonlinear part of the CNF control law. The design procedure and the improvement of the transient performance of the closed-loop system are illustrated with a numerical simulation.

In order to achieve a high precision in three-dimensional (3D) multi-camera measurement system, an efficient multi-camera calibration method is proposed. A stitching method of large scale calibration targets is deduced, and a fundamental of multi-camera calibration based on the large scale calibration target is provided. To avoid the shortcomings of the method, the vector differences of reprojection error with the presence of the constraint condition of the constant rigid body transformation is modelled, and minimized by the Levenberg-Marquardt (LM) method. Results of the simulation and observation data calibration experiment show that the accuracy of the system calibrated by the proposed method reaches 2 mm when measuring distance section of 20 000 mm and scale section of 7 000 mm × 7 000 mm. Consequently, the proposed method of multi-camera calibration performs better than the fundamental in stability. This technique offers a more uniform error distribution for measuring large scale space.

The particle filter (PF) algorithm is one of the most commonly used algorithms for maneuvering target tracking. The traditional PF maps from multi-dimensional information to onedimensional information during particle weight calculation, and the incorrect transmission of information leads to the fact that the particle prediction information does not match the weight information, and its essence is the reduction of the information entropy of the useful information. To solve this problem, a dual channel independent filtering method is proposed based on the idea of equalization mapping. Firstly, the particle prediction performance is described by particle manipulations of different dimensions, and the accuracy of particle prediction is improved. The improvement of particle degradation of this algorithm is analyzed in the aspects of particle weight and effective particle number. Secondly, according to the problem of lack of particle samples, the new particles are generated based on the filtering results, and the particle diversity is increased. Finally, the introduction of the graphics processing unit (GPU) parallel computing the platform, the "channel-level" and "particlelevel" parallel computing the program are designed to accelerate the algorithm. The simulation results show that the algorithm has the advantages of better filtering precision, higher particle efficiency and faster calculation speed compared with the traditional algorithm of the CPU platform.

In order to make systems that are based on unreliable components reliable, the design of fault tolerant architectures will be necessary. Inspired by von Neumann's negative AND (NAND) multiplexing and William's interwoven redundant logic, this paper presents a fault tolerant technique based on redundancy-modified NAND gates for future nanocomputers. Bifurcation theory is used to analyze fault tolerant ability of the system and the simulation results show that the new system has a much higher fault tolerant ability than the conventional multiplexing based on NAND gates. According to the evaluation, the proposed architecture can tolerate a device error rate of up to 10^?1, with multiple redundant components. This fault tolerant technique is potentially useful for future nanoelectronics.

Damage detection in structures is performed via vibration based structural identification. Modal information, such as frequencies and mode shapes, are widely used for structural damage detection to indicate the health conditions of civil structures. The deep learning algorithm that works on a multiple layer neural network model termed as deep autoencoder is proposed to learn the relationship between the modal information and structural stiffness parameters. This is achieved via dimension reduction of the modal information feature and a non-linear regression against the structural stiffness parameters. Numerical tests on a symmetrical steel frame model are conducted to generate the data for the training and validation, and to demonstrate the efficiency of the proposed approach for vibration based structural damage detection.