Frame and frequency synchronization are essential for orthogonal frequency division multiplexing (OFDM) systems. The frame offset owing to incorrect start point position of the fast Fourier transform (FFT) window, and the carrier frequency offset (CFO) due to Doppler frequency shift or the frequencymismatch between the transmitter and receiver oscillators, can bring severe intersymbol interference(ISI) and inter-carrier interference (ICI) for the OFDM system. Relying on the relatively good correlation characteristic of the pseudo-noise (PN) sequence, a joint frame offset and normalized CFO estimation algorithm based on PN preamble in time domain is developed to realize the frame and frequency synchronization in the OFDM system. By comparison, the performances of the traditional algorithm and the improved algorithm are simulated under different conditions. The results indicate that the PN preamble based algorithm both in frame offset estimation and CFO estimation is more accurate, resource-saving and robust even under poor channel condition, such as low signal-to-noise ratio (SNR) and large normalized CFO.

This paper focuses on reducing the complexity of K-best sphere decoding (SD) algorithm for the detection of uncoded multiple input multiple output (MIMO) systems. The proposed algorithm utilizes the threshold-pruning method to cut nodes with partial Euclidean distances (PEDs) larger than the threshold. Both the known noise value and the unknown noise value are considered to generate the threshold, which is the sum of the two values. The known noise value is the smallest PED of signals in the detected layers. The unknown noise value is generated by the noise power, the quality of service (QoS) and the signal-to-noise ratio (SNR) bound. Simulation results show that by considering both two noise values, the proposed algorithm makes an efficient reduction while the performance drops little.

This paper deals with the blind separation of nonstationary sources and direction-of-arrival (DOA) estimation in the underdetermined case, when there are more sources than sensors. We assume the sources to be time-frequency (TF) disjoint to a certain extent. In particular, the number of sources presented at any TF neighborhood is strictly less than that of sensors. We can identify the real number of active sources and achieve separation in any TF neighborhood by the sparse representation method. Compared with the subspace-based algorithm under the same sparseness assumption, which suffers from the extra noise effect since it cannot estimate the true number of active sources, the proposed algorithm can estimate the number of active sources and their corresponding TF values in any TF neighborhood simultaneously. Another contribution of this paper is a new estimation procedure for the DOA of sources in the underdetermined case, which combines the TF sparseness of sources and the clustering technique. Simulation results demonstrate the validity and high performance of the proposed algorithm in both blind source separation (BSS) and DOA estimation.

Aimed at the abominable influences to blind equalization algorithms caused by complex time-space variability existing in underwater acoustic channels, a new self-adjusting decision feedback equalization (DFE) algorithm adapting to different underwater acoustic channel environments is proposed by changing its central tap position. Besides, this new algorithm behaves faster convergence speed based on the analysis of equalizers’ working rules, which is more suitable to implement communications in different unknown channels. Corresponding results and conclusions are validated by simulations and spot experiments.

A novel adaptive detection scheme both for point-like and distributed targets in the presence of Gaussian disturbance in the partially homogeneous environment (PHE) is proposed. The novel detection scheme is based on the orthogonal projection technique. Both the case of known covariance matrix structure and the case of unknown covariance matrix structure are considered. For the former case, the closed-form statistical property of the novel detectors is derived. When the covariance matrix is unknown, the corresponding detectors have higher probabilities of detection (PDs) than their natural competitors. Moreover, they ensure constant false alarm rate (CFAR) property.

This paper deals with system engineering and design methodology for super low altitude satellites in the view of the computational mission analysis. Due to the slight advance of imaging instruments, such as the focus of camera and the image element of charge coupled device (CCD), it is an innovative and economical way to improve the camera’s resolution to enforce the satellite to fly on the lower altitude orbit. DFH-3, the mature satellite bus developed by Chinese Academy of Space Technology, is employed to define the mass and power budgets for the computational mission analysis and the detailed engineering design for super low altitude satellites. An effective iterative algorithm is proposed to solve the ergodic representation of feasible mass and power budgets at the flight altitude under constraints. Besides, boundaries of mass or power exist for every altitude, where the upper boundary is derived from the maximum power, while the minimum thrust force holds the lower boundary before the power reaching the initial value. What’s more, an analytical algorithm is employed to numerically investigate the coverage percentage over the altitude, so that the nominal altitude could be selected from all the feasible altitudes based on both the mass and power budgets and the repetitive ground traces. The local time at the descending node is chosen for the nominal sun-synchronous orbit based on the average evaluation function. After determining the key orbital elements based on the computational mission analysis, the detailed engineering design on the configuration and other subsystems, like power, telemetry telecontrol and communication (TT&C), and attitude determination and control system (ADCS), is performed based on the benchmark bus, besides, some improvements to the bus are also implemented to accommodate the flight at a super low altitude. Two operation strategies, drag-free closed-loop mode and on/off open-loop mode, are presented to maintain the satellite’s altitude. Finally, a flight planning schedule for the satellite is demonstrated from its launch into the initial altitude at the very beginning to its decay to death in the end.

Cooperative jamming weapon-target assignment (CJWTA) problem is a key issue in electronic countermeasures (ECM). Some symbols which relevant to the CJWTA are defined firstly. Then, a formulation of jamming fitness is presented. Finally, a model of the CJWTA problem is constructed. In order to solve the CJWTA problem efficiently, a self-adaptive learning based discrete differential evolution (SLDDE) algorithm is proposed by introducing a self-adaptive learning mechanism into the traditional discrete differential evolution algorithm. The SLDDE algorithm steers four candidate solution generation strategies simultaneously in the framework of the self-adaptive learning mechanism. Computational simulations are conducted on ten test instances of CJWTA problem. The experimental results demonstrate that the proposed SLDDE algorithm not only can generate better results than only one strategy based discrete differential algorithms, but also outperforms two algorithms which are proposed recently for the weapontarget assignment problems.

This paper considers a project scheduling problem with the objective of minimizing resource availability costs appealed to finish all activities before the deadline. There are finish-start type precedence relations among the activities which require some kinds of renewable resources. We predigest the process of solving the resource availability cost problem (RACP) by using start time of each activity to code the schedule. Then, a novel heuristic algorithm is proposed to make the process of looking for the best solution efficiently. And then pseudo particle swarm optimization (PPSO) combined with PSO and path relinking procedure is presented to solve the RACP. Finally, comparative computational experiments are designed and the computational results show that the proposed method is very effective to solve RACP

A combination method of optimization of the background value and optimization of the initial item is proposed. The sequences of the unbiased exponential distribution are simulated and predicted through the optimization of the background value in grey differential equations. The principle of the new information priority in the grey system theory and the rationality of the initial item in the original GM(1,1) model are fully expressed through the improvement of the initial item in the proposed time response function. A numerical example is employed to illustrate that the proposed method is able to simulate and predict sequences of raw data with the unbiased exponential distribution and has better simulation performance and prediction precision than the original GM(1,1) model relatively.

This paper focuses on fast algorithm for computing the assignment reduct in inconsistent incomplete decision systems. It is quite inconvenient to judge the assignment reduct directly according to its definition. We propose the judgment theorem for the assignment reduct in the inconsistent incomplete decision system, which greatly simplifies judging this type reduct. On such basis, we derive a novel attribute significance measure and construct the fast assignment reduction algorithm (F-ARA), intended for computing the assignment reduct in inconsistent incomplete decision systems. Finally, we make a comparison between F-ARA and the discernibility matrix-based method by experiments on 13 University of California at Irvine (UCI) datasets, and the experimental results prove that F-ARA is efficient and feasible.

The time optimal problem for a two level quantum system is studied. We compare two different control strategies of bang-bang control and the geometric control, respectively, especially in the case of minimizing the time of steering the state from North Pole to South Pole on the Bloch sphere with bounded control. The time performances are compared for different parameters by the individual numerical simulation experiments, and the experimental results are analyzed. The results show that the geometric control spends less time than the bang-bang control does.

Traditional orthogonal strapdown inertial navigation system (SINS) cannot achieve satisfactory self-alignment accuracy in the stationary base: taking more than 5 minutes and all the inertial sensors biases cannot get full observability except the up-axis accelerometer. However, the full skewed redundant SINS (RSINS) can not only enhance the reliability of the system, but also improve the accuracy of the system, such as the initial alignment. Firstly, the observability of the system state includes attitude errors and all the inertial sensors biases are analyzed with the global perspective method: any three gyroscopes and three accelerometers can be assembled into an independent subordinate SINS (sub-SINS); the system state can be uniquely confirmed by the coupling connections of all the sub-SINSs; the attitude errors and random constant biases of all the inertial sensors are observable. However, the random noises of the inertial sensors are not taken into account in the above analyzing process. Secondly, the full-observable Kalman filter which can be applied to the actual RSINS containing random noises is established; the system state includes the position, velocity, attitude errors of all the sub-SINSs and the random constant biases of the redundant inertial sensors. At last, the initial selfalignment process of a typical four-redundancy full skewed RSINS is simulated: the horizontal attitudes (pitch, roll) errors and yaw error can be exactly evaluated within 80 s and 100 s respectively, while the random constant biases of gyroscopes and accelerometers can be precisely evaluated within 120 s. For the full skewed RSINS, the self-alignment accuracy is greatly improved, meanwhile the self-alignment time is widely shortened.

This paper describes a novel approach for identifying the Z-axis drift of the ring laser gyroscope (RLG) based on genetic algorithm (GA) and support vector regression (SVR) in the single-axis rotation inertial navigation system (SRINS). GA is used for selecting the optimal parameters of SVR. The latitude error and the temperature variation during the identification stage are adopted as inputs of GA-SVR. The navigation results show that the proposed GA-SVR model can reach an identification accuracy of 0.000 2 (?)/ h for the Z-axis drift of RLG. Compared with the radial basis function-neural network (RBF-NN) model, the GA-SVR model is more effective in identification of the Z-axis drift of RLG.

The design of robust H∞ filtering problem of polytopic uncertain linear time-delay systems is addressed. The uncertain parameters are supposed to reside in a polytope. A parameterdependent Lyapunov function approach is proposed for the design of filters that ensure a prescribed H∞ performance level for all admissible
uncertain parameters, which is different from the quadratic framework that entails fixed matrices for the entire uncertainty domain. This idea is realized by carefully selecting the structure of the matrices involved in the products with system matrices. An extended H∞ sufficient condition for the existence of robust estimators is formulated in terms of linear matrix inequalities, which can be solved via efficient interior-point algorithms.

This paper considers the problem of adaptive control for a class of multiple input multiple output (MIMO) nonlinear discrete-time systems based on input-output model with unknown interconnections between subsystems. Based on the Taylor expand technology, an equivalent model in affine-like form is derived for the original nonaffine nonlinear system. Then a direct adaptive neural network (NN) controller is implemented based on the affine-like model. By finding an orthogonal matrix to tune the NN weights, the closed-loop system is proven to be semiglobally uniformly ultimately bounded. The σ-modification technique is used to remove the requirement of persistence excitation during the adaptation. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters.

The asymptotic and stable properties of general stochastic functional differential equations are investigated by the multiple Lyapunov function method, which admits non-negative upper bounds for the stochastic derivatives of the Lyapunov functions, a theorem for asymptotic properties of the LaSalle-type described by limit sets of the solutions of the equations is obtained. Based on the asymptotic properties to the limit set, a theorem of asymptotic stability of the stochastic functional differential equations is also established, which enables us to construct the Lyapunov functions more easily in application. Particularly, the well-known classical theorem on stochastic stability is a special case of our result, the operator LV is not required to be negative which is more general to fulfil and the stochastic perturbation plays an important role in it. These show clearly the improvement of the traditional method to find the Lyapunov functions. A numerical simulation example is given to illustrate the usage of the method.

A novel strategy of probability density function (PDF) shape control is proposed in stochastic systems. The controller is designed whose parameters are optimally obtained through the improved particle swarm optimization algorithm. The parameters of the controller are viewed as the space position of a particle in particle swarm optimization algorithm and updated continually until the controller makes the PDF of the state variable as close as possible to the expected PDF. The proposed PDF shape control technique is compared with the equivalent linearization technique through simulation experiments. The results show the superiority and the effectiveness of the proposed method. The controller is excellent in making the state PDF follow the expected PDF and has the very small error between the state PDF and the expected PDF, solving the control problem of the PDF shape in stochastic systems effectively.

This paper focuses on the performance analysis of flexible reactive systems. The performance analysis consists of two phases: first system modeling, second performance evaluation. The paper models the flexible reactive system by the stochastic statecharts method, and uses the simulation method to evaluate the performance. To make use of the feature of event-triggered state transitions in the statecharts, a new method of simulation is proposed based on the techniques of the discrete-event system simulation. The new method solves the problem of computer implementation of stochastic events, probabilistic transition, concurrent states, parallel actions, and broadcast communication mechanism in the stochastic statecharts. An example of a flexible manufacturing system is presented. The simulation result of the example is consistent with the analytical result, which shows the feasibility of the proposed new simulation method.

In the field of supercomputing, one key issue for scalable shared-memory multiprocessors is the design of the directory which denotes the sharing state for a cache block. A good directory design intends to achieve three key attributes: reasonable memory overhead, sharer position precision and implementation complexity. However, researchers often face the problem that gaining one attribute may result in losing another. The paper proposes an elastic pointer directory (EPD) structure based on the analysis of shared-memory applications, taking the fact that the number of sharers for each directory entry is typically small. Analysis results show that for 4 096 nodes, the ratio of memory overhead to the full-map directory is 2.7%. Theoretical analysis and cycleaccurate execution-driven simulations on a 16 and 64-node cache coherence non uniform memory access (CC-NUMA) multiprocessor show that the corresponding pointer overflow probability is reduced significantly. The performance is observed to be better than that of a limited pointers directory and almost identical to the full-map directory, except for the slight implementation complexity. Using the directory cache to explore directory access locality is also studied. The experimental result shows that this is a promising approach to be used in the state-of-the-art high performance computing domain.

Total variation (TV) is widely applied in image processing. The assumption of TV is that an image consists of piecewise constants, however, it suffers from the so-called staircase effect. In order to reduce the staircase effect and preserve the edges when textures of image are extracted, a new image decomposition model is proposed in this paper. The proposed model is based on the total generalized variation method which involves and balances the higher order of the structure. We also derive a numerical algorithm based on a primal-dual formulation that can be effectively implemented. Numerical experiments show that the proposed method
can achieve a better trade-off between noise removal and texture extraction, while avoiding the staircase effect efficiently.