This paper proposes an environment-aware bestretransmission count selected optimization control scheme over IEEE 802.11 multi-hop wireless networks. The proposed scheme predicts the wireless resources by using statistical channel state and provides maximum retransmission count optimization based on wireless channel environment state to improve the packet delivery success ratio. The media access control (MAC) layer selects the best-retransmission count by perceiving the types of packet loss in wireless link and using the wireless channel characteristics and environment information, and adjusts the packet forwarding adaptively aiming at improving the packet retransmission probability. Simulation results show that the best-retransmission count selected scheme achieves a higher packet successful delivery percentage and a lower packet collision probability than the corresponding traditional MAC transmission control protocols.

In order to meet the demands for high transmission rates and high service quality in broadband wireless communication systems, orthogonal frequency division multiplexing (OFDM) has been adopted in some standards. However, the inter-block interference (IBI) and inter-carrier interference (ICI) in an OFDM
system affect the performance. To mitigate IBI and ICI, some pre-processing approaches have been proposed based on full channel state information (CSI), which improved the system performance. A pre-processing filter based on partial CSI at the transmitter is designed and investigated. The filter coefficient is given by the optimization processing, the symbol error rate (SER) is tested, and the computation complexity of the proposed scheme is analyzed. Computer simulation results show that the proposed pre-processing filter can effectively mitigate IBI and ICI and the performance can be improved. Compared with pre-processing approaches at the transmitter based on full CSI, the proposed scheme has high spectral efficiency, limited CSI feedback and low computation complexity.

The differential chaotic shift keying (DCSK) communication in multiple input multiple output (MIMO) multipath fading channels is considered. A simple MIMO-DCSK communication scheme based on orthogonal multi-codes (OMCs) and equal gain combination (EGC) is proposed, in which OMCs are used to spread the same information bit at each transmitting antenna and the information bit is detected by EGC at receiving antenna. The OMCs are constructed from one chaotic sequence by means of othogonal space-time block coding (OSTBC). The output signal-to-noise ratio (SNR) after EGC is given based on central limit theory (CLT), and it can effectively exploit the spatial diversity of the underlying MIMO system. Simulation results show that the full spatial diversity
gain is achieved without channel estimation in the MIMO-DCSK communication scheme and it performs better than MC-EGC for a large number of transmitting antennas.

For global navigation satellite system (GNSS) signals in Gaussian and Rayleigh fading channel, a novel signal detection algorithm is proposed. Under the low frequency uncertainty case, after performing discrete cosine transform (DCT) to the outputs of the partial matched filter (PMF) for every antenna, the high order components in the transforming domain will be filtered, then the equalgain (EG) combination for the inverse discrete cosine transform (IDCT) reconstructed signal would be done subsequently. Thus, due to the different frequency distribution characteristics between the noise and signals, after EG combination, the energy of signals has almost no loss and the noise energy is greatly reduced. The theoretical analysis and simulation results show that the detection algorithm can effectively improve the signal-to-noise ratio of the captured signal and increase the probability of detection under the same false alarm probability. In addition, it should be pointed out that this method can also be applied to Rayleigh fading channels with moving antenna.

We propose a new multipath mitigation technique based on cross-correlation function for the new cosine phased binary offset carrier (cosine-BOC) modulated signals, which will most likely be employed in both European Galileo system and Chinese Compass system. This technique is implemented to create an optimum cross-correlation function via designing the modulated symbols of the local signal. And the structure of the code tracking loop for cosine-BOC signals is quite simple including only two real correlators. Results demonstrate that the technique efficiently eliminates the ranging errors in the medium and long multipath regions with respect to the conventional receiver correlation techniques.

This paper presents an approach to the challenging issue of passive source localization in shallow water using a mobile short horizontal linear array with length less than ten meters. The short array can be conveniently placed on autonomous underwater vehicles and deployed for adaptive spatial sampling. However, the use of such small aperture passive sonar systems makes it difficult to acquire sufficient spatial gain for localizing long-range sources. To meet the requirement, a localization approach that employs matched-field based techniques that enable the short horizontal linear array is used to passively localize acoustic sources in shallow water. Furthermore, the broadband processing and inter-position processing provide robustness against ocean environmental mismatch and enhance the stability of the estimation process. The proposed approach’s ability to localize acoustic sources in shallow water at different signal-to-noise ratios is examined through the synthetic test cases where the sources are located at the endfire and some other bearing of the mobile short horizontal linear array. The presented results demonstrate that the positional parameters of the estimated source build up over time as the array moves at a low speed along a straight line at a constant depth.

Calibration is a processing procedure for across-track interferometric synthetic aperture radar (InSAR) to achieve an accurate three-dimensional location. A calibration technique, called weighted joint calibration, for the generation of wide-area geocoded digital elevation models (DEMs) is proposed. It calibrates multiple InSAR scenes simultaneously, and allows reducing the number of required ground control points (GCPs) by using tie points (TPs). This approach may ensure the continuity of threedimensional location among adjacent scenes, which is necessary for mosaic and fusion of data coming from different scenes. In addition, it introduces weights to calibration to discriminate GCPs and TPs with different coherences and locations. This paper presents the principles and methodology of this weighted joint calibration technique and illustrates its successful application in airborne In-SAR data.

This paper proposes a novel polarized radar seeker based on the polarized antenna array. A fully polarized signal processing method for the proposed radar seeker is studied under the environments with electromagnetic interferences. A dual polarized antenna array is employed to transmit and receive the radar signals. The instantaneous polarization signal processing technique is used to detect and recognize the targets. The direction of arrival (DOA) of the target is measured through the spatial spectrum with high resolution for the polarized array radar seeker system. The fully polarized signal model of the polarized array radar seeker is formulated and a specific signal processing algorithm is expounded. The theoretical research and numerical simulation results demonstrate that the proposed radar seeker has good performances in target detection and electronic warfare. The research results can provide an effective technical approach to develop and research the new generation radar seeker.

The test selection and optimization (TSO) can improve the abilities of fault diagnosis, prognosis and health-state evaluation for prognostics and health management (PHM) systems. Traditionally, TSO mainly focuses on fault detection and isolation, but they cannot provide an effective guide for the design for testability (DFT) to improve the PHM performance level. To solve the problem, a model of TSO for PHM systems is proposed. Firstly, through integrating the characteristics of fault severity and propagation time, and analyzing the test timing and sensitivity, a testability model based on failure evolution mechanism model (FEMM) for PHM systems is built up. This model describes the fault evolutiontest dependency using the fault-symptom parameter matrix and symptom parameter-test matrix. Secondly, a novel method of inherent testability analysis for PHM systems is developed based on the above information. Having completed the analysis, a TSO model, whose objective is to maximize fault trackability and minimize the test cost, is proposed through inherent testability analysis results, and an adaptive simulated annealing genetic algorithm (ASAGA) is introduced to solve the TSO problem. Finally, a case of a centrifugal pump system is used to verify the feasibility and effectiveness of the proposed models and methods. The results show that the proposed technology is important for PHM systems to select and optimize the test set in order to improve their performance level.

A new fully fuzzy linear programming (FFLP) problem with fuzzy equality constraints is discussed. Using deviation degree measures, the FFLP problem is transformed into a crisp δ-parametric linear programming (LP) problem. Giving the value of deviation degree in each constraint, the δ-fuzzy optimal solution of the FFLP problem can be obtained by solving this LP problem. An algorithm is also proposed to find a balance-fuzzy optimal solution between two goals in conflict: to improve the values of the objective function and to decrease the values of the deviation degrees. A numerical example is solved to illustrate the proposed method.

Abstract: The problem of passive detection discussed in this paper involves searching and locating an aerial emitter by dualaircraft using passive radars. In order to improve the detection probability and accuracy, a fuzzy Q learning algorithm for dualaircraft flight path planning is proposed. The passive detection task model of the dual-aircraft is set up based on the partition of the target active radar’s radiation area. The problem is formulated as a Markov decision process (MDP) by using the fuzzy theory to make a generalization of the state space and defining the transition functions, action space and reward function properly. Details of the path planning algorithm are presented. Simulation results indicate that the algorithm can provide adaptive strategies for dual-aircraft to control their flight paths to detect a non-maneuvering or maneuvering target.

An opportunistic maintenance model is presented for a continuously deteriorating series system with economical dependence. The system consists of two kinds of units, which are respectively subjected to the deterioration failure described by Gamma process and the random failure described by Poisson process. A two-level opportunistic policy defined by three decision parameters is proposed to coordinate the different maintenance actions and minimize the long-run maintenance cost rate of the system. A computable expression of the average cost rate is established by using the renewal property of the stochastic process of the maintained system state. The optimal values of three decision parameters are derived by an iteration approach based on the characteristic of Gamma process. The behavior of the proposed policy is illustrated through a numerical experiment. Comparative study with the widely used corrective maintenance policy demonstrates the advantage of the proposed opportunistic maintenance method in significantly reducing the maintenance cost. Simultaneously, the applicable area of this opportunistic model is discussed by the sensitivity analysis of the set-up cost and random failure rate.

The distributed leadless consensus problem for multiple quadrotor systems under fixed and switching topologies is investigated. The objective is to design protocols achieving consensus for networked quadrotors’ positions and attitudes. Because the model of a quadrotor is a strong high-order nonlinear coupling system, the approach of feedback linearization is employed to transform the model into a group of four linear subsystems among which there is no coupling. Then, a consensus algorithm is proposed which consists of a local feedback controller and interactions from the finite neighbors under fixed undirected topologies. Especially, the problem of choosing the parameters in the consensus algorithm is also addressed, enlightened by the results of the robust control theory. Furthermore, it is proved that the proposed algorithm also guarantees the consensus under undirected switching topologies. Simulation results show the effectiveness of the proposed algorithm.

An improved particle swarm algorithm based on the D-Tent chaotic model is put forward aiming at the standard particle swarm algorithm. The convergence rate of the late of proposed algorithm is improved by revising the inertia weight of global optimal particles and the introduction of D-Tent chaotic sequence. Through the test of typical function and the autotuning test of proportionalintegral- derivative (PID) parameter, finally a simulation is made to the servo control system of a permanent magnet synchronous motor (PMSM) under double-loop control of rotating speed and current by utilizing the chaotic particle swarm algorithm. Studies show that the proposed algorithm can reduce the iterative times and improve the convergence rate under the condition that the global optimal solution can be got.

A novel neural network based on iterated unscented Kalman filter (IUKF) algorithm is established to model and compensate for the fiber optic gyro (FOG) bias drift caused by temperature. In the network, FOG temperature and its gradient are set as input and the FOG bias drift is set as the expected output. A 2-5-1 network trained with IUKF algorithm is established. The IUKF algorithm is developed on the basis of the unscented Kalman filter (UKF). The weight and bias vectors of the hidden layer are set as the state of the UKF and its process and measurement equations are deduced according to the network architecture. To solve the unavoidable estimation deviation of the mean and covariance of the states in the UKF algorithm, iterative computation is introduced into the UKF after the measurement update. While the measurement noise R is extended into the state vectors before iteration in order to meet the statistic orthogonality of estimate and measurement noise. The IUKF algorithm can provide the optimized estimation for the neural network because of its state expansion and iteration. Temperature rise (–20–20?C) and drop (70–20?C) tests for FOG are carried out in an attemperator. The temperature drift model is built with neural network, and it is trained respectively with BP, UKF and IUKF algorithms. The results prove that the proposed model has higher precision compared with the backpropagation (BP) and UKF network models.

According to the delay property, linear time-delay (LTD) systems can be classified as LTD systems with dependent delays (LTD DD) and LTD systems with independent delays (LTD ID). This paper reveals that the stability condition for LTD ID systems can be applied to LTD DD systems, a sufficient stability condition for LTD DD systems is derived from it, while only fewer of the LTD DD systems can satisfy the stability condition due to the very strict limitation for the delays of the LTD DD systems. To solve the problem, based on two-dimensional (2-D) hybrid polynomials, some sufficient conditions for stability of LTD DD systems are proposed. Examples show that the proposed stability test algorithms are simple and valid.

The existing trajectory clustering (TRACLUS) is sensitive to the input parameters ε and MinLns. The parameter value is changed a little, but cluster results are entirely different. Aiming at this vulnerability, a shielding parameters sensitivity trajectory cluster (SPSTC) algorithm is proposed which is insensitive to the input parameters. Firstly, some definitions about the core distance and reachable distance of line segment are presented, and then the algorithm generates cluster sorting according to the core distance and reachable distance. Secondly, the reachable plots of line segment sets are constructed according to the cluster sorting and reachable distance. Thirdly, a parameterized sequence is extracted according to the reachable plot, and then the final trajectory  cluster based on the parameterized sequence is acquired. The parameterized sequence represents the inner cluster structure of trajectory data. Experiments on real data sets and test data sets show that the SPSTC algorithm effectively reduces the sensitivity to the input parameters, meanwhile it can obtain the better quality of the trajectory cluster.

Field computation, an emerging computation technique, has inspired passion of intelligence science research. A novel field computation model based on the magnetic field theory is constructed. The proposed magnetic field computation (MFC) model consists of a field simulator, a non-derivative optimization algorithm and an auxiliary data processing unit. The mathematical model is deduced and proved that the MFC model is equivalent to a quadratic discriminant function. Furthermore, the finite element prototype is derived, and the simulator is developed, combining with particle swarm optimizer for the field configuration. Two benchmark classification experiments are studied in the numerical experiment, and one notable advantage is demonstrated that less training samples are required and a better generalization can be achieved.

How to effectively reduce the energy consumption of large-scale data centers is a key issue in cloud computing. This paper presents a novel low-power task scheduling algorithm (LTSA) for large-scale cloud data centers. The winner tree is introduced to make the data nodes as the leaf nodes of the tree and the
final winner on the purpose of reducing energy consumption is selected. The complexity of large-scale cloud data centers is fully consider, and the task comparson coefficient is defined to make task scheduling strategy more reasonable. Experiments and performance analysis show that the proposed algorithm can effectively improve the node utilization, and reduce the overall power consumption of the cloud data center.