How to obtain accurate channel state information (CSI) at the transmitter with less pilot overhead for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system is a challenging issue due to the large number of antennas. To reduce the overwhelming pilot overhead, a hybrid orthogonal and non-orthogonal pilot distribution at the base station (BS), which is a generalization of the existing pilot distribution scheme, is proposed by exploiting the common sparsity of channel due to the compact antenna arrangement. Then the block sparsity for antennas with hybrid pilot distribution is derived respectively and can be used to obtain channel impulse response. By employing the theoretical analysis of block sparse recovery, the total coherence criterion is proposed to optimize the sensing matrix composed by orthogonal pilots. Due to the huge complexity of optimal pilot acquisition, a genetic algorithm based pilot allocation (GAPA) algorithm is proposed to acquire optimal pilot distribution locations with fast convergence. Furthermore, the Cramer Rao lower bound is derived for non-orthogonal pilot-based channel estimation and can be asymptotically approached by the prior support set, especially when the optimized pilot is employed.

This paper presents the derivation of Gauss-Newton filter in linear cases and an analysis of its properties. Based on the minimum variance theorem, the Gauss-Newton filter is constructed and derived, including its state transition equation, observation equation and filtering process. Then, the delicate relationship between the Gauss-Aitken filter and the Kalman filter is discussed and it is verified that without process noise the two filters are equivalent. Finally, some simulations are conducted. The result shows that the Gauss-Aitken filter is superior to the Kalman filter in some aspects.

The ionosphere, as the largest and least predictable error source, its behavior cannot be observed at all places simultaneously. The confidence bound, called the grid ionospheric vertical error (GIVE), can only be determined with the aid of a threat model which is used to restrict the expected ionospheric behavior. However, the spatial threat model at present widespread used, which is based on fit radius and relative centroid metric (RCM), is too conservative or the resulting GIVEs will be too large and will reduce the availability of satellite-based augmentation system (SBAS). In this paper, layered two-dimensional parameters, the vertical direction double RCMs, are introduced based on the spatial variability of the ionosphere. Comparing with the traditional threat model, the experimental results show that the user ionospheric vertical error (UIVE) average reduction rate reaches 16%. And the 95% protection level of conterminous United States (CONUS) is 28%, even under disturbed days, which reaches about 5% reduction rates. The results show that the system service performance has been improved better.

To realize high-resolution digital beamforming (DBF) of ultra-wideband (UWB) signals, we propose a DBF method based on Caratheodory representation for delay compensation ′ and array extrapolation. Delay compensation by Caratheodory ′ representation could achieve high interpolation accuracy while using the single channel sampling technique. Array extrapolation by Caratheodory representation reformulates and extends each ′ snapshot, consequently extends the aperture of the original uniform linear array (ULA) by several times and provides a better realtime performance than the existing aperture extrapolation utilizing vector extrapolation based on the two dimensional autoregressive (2-D AR) model. The UWB linear frequency modulated (LFM) signal is used for simulation analysis. Simulation results demonstrate that the proposed method is featured by a much higher spatial resolution than traditional DBF methods and lower sidelobes than using Lagrange fractional filters.

This paper investigates the node localization problem for wireless sensor networks in three-dimension space. A distributed localization algorithm is presented based on the rigid graph. Before location, the communication radius is adaptively increasing to add the localizability. The localization process includes three steps: firstly, divide the whole globally rigid graph into several small rigid blocks; secondly, set up the local coordinate systems and transform them to global coordinate system; finally, use the quadrilateration iteration technology to locate the nodes in the wireless sensor network. This algorithm has the advantages of low energy consumption, low computational complexity as well as high expandability and high localizability. Moreover, it can achieve the unique and accurate localization. Finally, some simulations are provided to demonstrate the effectiveness of the proposed algorithm.

A scheduling algorithm is presented aiming at the task scheduling problem in the phased array radar. Rather than assuming the scheduling interval (SI) time, which is the update interval of the radar invoking the scheduling algorithm, to be a fixed value, it is modeled as a fuzzy set to improve the scheduling flexibility. The scheduling algorithm exploits the fuzzy set model in order to intelligently adjust the SI time. The idle time in other SIs is provided for SIs which will be overload. Thereby more request tasks can be accommodated. The simulation results show that the proposed algorithm improves the successful scheduling ratio by 16%, the threat ratio of execution by 16% and the time utilization ratio by 15% compared with the highest task mode priority first (HPF) algorithm.

According to the oversampling imaging characteristics, an infrared small target detection method based on deep learning is proposed. A 7-layer deep convolutional neural network (CNN) is designed to automatically extract small target features and suppress clutters in an end-to-end manner. The input of CNN is an original oversampling image while the output is a cluttersuppressed feature map. The CNN contains only convolution and non-linear operations, and the resolution of the output feature map is the same as that of the input image. The L1-norm loss function is used, and a mass of training data is generated to train the network effectively. Results show that compared with several baseline methods, the proposed method improves the signal clutter ratio gain and background suppression factor by 3 – 4 orders of magnitude, and has more powerful target detection performance.

Unmanned combat air vehicles (UCAVs) mission planning is a fairly complicated global optimum problem. Military attack missions often employ a fleet of UCAVs equipped with weapons to attack a set of known targets. A UCAV can carry different weapons to accomplish different combat missions. Choice of different weapons will have different effects on the final combat effectiveness. This work presents a mixed integer programming model for simultaneous weapon configuration and route planning of UCAVs, which solves the problem optimally using the IBM ILOG CPLEX optimizer for simple missions. This paper develops a heuristic algorithm to handle the medium-scale and large-scale problems. The experiments demonstrate the performance of the heuristic algorithm in solving the medium scale and large scale problems. Moreover, we give suggestions on how to select the most appropriate algorithm to solve different scale problems.

Sensors deployment optimization has become one of the most attractive fields in recent years. However, most of the previous work focused on the deployment problem in 2D space. Compared to the traditional form, sensors deployment in multidimensional space has greater research significance and practical potential to satisfy the detecting needs in complex environment. Aiming at solving this issue, a multi-dimensional space sensor network model is established, and the radar system is selected as an example. Considering the possible working mode of the radar system (e.g., searching and tracking), two distinctive deployment models are proposed based on maximum coverage area and maximum target detection probability in the attack direction respectively. The latter one is usually ignored in the previous literature. For uncovering the optimal deployment of the sensor network, the particle swarm optimization (PSO) algorithm is improved using the proposed weights determination scheme, in which the linear decreasing, the pooling strategy and the cloud theory are combined for weights updating. Experimental results illustrate the effectiveness of the proposed method.

Platform planning is one of the important problems in the command and control (C2) field. Hereto, we analyze the platform planning problem and present nonlinear optimal model aiming at maximizing the task completion qualities. Firstly, we take into account the relation among tasks and build the single task nonlinear optimal model with a set of platform constraints. The Lagrange relaxation method and the pruning strategy are used to solve the model. Secondly, this paper presents optimization-based planning algorithms for efficiently allocating platforms to multiple tasks. To achieve the balance of the resource assignments among tasks, the m-best assignment algorithm and the pair-wise exchange (PWE) method are used to maximize multiple tasks completion qualities. Finally, a series of experiments are designed to verify the superiority and effectiveness of the proposed model and algorithms.

Prior research on the resilience of critical infrastructure usually utilizes the network model to characterize the structure of the components so that a quantitative representation of resilience can be obtained. Particularly, network component importance is addressed to express its significance in shaping the resilience performance of the whole system. Due to the intrinsic complexity of the problem, some idealized assumptions are exerted on the resilience-optimization problem to find partial solutions. This paper seeks to exploit the dynamic aspect of system resilience, i.e., the scheduling problem of link recovery in the post-disruption phase. The aim is to analyze the recovery strategy of the system with more practical assumptions, especially inhomogeneous time cost among links. In view of this, the presented work translates the resilience-maximization recovery plan into the dynamic decisionmaking of runtime recovery option. A heuristic scheme is devised to treat the core problem of link selection in an ongoing style. Through Monte Carlo simulation, the link recovery order rendered by the proposed scheme demonstrates excellent resilience performance as well as accommodation with uncertainty caused by epistemic knowledge.

A dynamic hesitant fuzzy linguistic group decisionmaking (DHFLGDM) problem is studied from the perspective of information reliability based on the theory of hesitant fuzzy linguistic term sets (HFLTSs). First, an approach is applied to transform the dynamic HFLTSs (DHFLTSs) into a set of proportional linguistic terms to eliminate the time dimension. Second, expert reliability is measured by considering both group similarity and degree of certainty, and an optimization method is employed to quantify the linguistic terms by maximizing the group similarity. Third, through computing the attribute stability as well as its reliability, a combination rule which considers both reliability and weight is proposed to aggregate the information, and then the aggregated grade values and degree of stability are used to make a selection. Finally, the application and feasibility of the proposed method are verified through a case study and method comparison.

Reaction control system (RCS) is a powerful and efficient actuator for space vehicles attitude control, which is typically characterized as a pulsed unilateral effector only with two states (off/on). Along with inevitable internal uncertainties and external disturbances in practice, this inherent nonlinear character always hinders space vehicles autopilot from pursuing precise tracking performance. Compared to most of pre-existing methodologies that passively suppress the uncertainties and disturbances, a design based on predictive functional control (PFC) and generalized extended state observer (GESO) is firstly proposed for three-axis RCS control system to actively reject that with no requirement for additional fuel consumption. To obtain a high fidelity predictive model on which the performance of PFC greatly depends, the nonlinear coupling multiple-input multiple-output (MIMO) flight dynamics model is parameterized as a state-dependent coefficient form. And based on that, a MIMO PFC algorithm in state space domain for a plant of arbitrary orders is deduced in this paper. The internal uncertainties and external disturbances are lumped as a total disturbance, which is estimated and cancelled timely to further enhance the robustness. The continuous control command synthesised by above controller-rejector tandem is finally modulated by pulse width pulse frequency modulator (PWPF) to on-off signals to meet RCS requirement. The robustness and feasibility of the proposed design are validated by a series of performance comparison simulations with some prominent methods in the presence of significant perturbations and disturbances, as well as measurement noise.

The impact angle control over guidance (IACG) law against stationary targets is proposed by using feedback linearization control (FLC) and finite time control (FTC). First, this paper transforms the kinematics equation of guidance systems into the feedbackable linearization model, in which the guidance law is obtained without considering the impact angle via FLC. For the purpose of the line of sight (LOS) angle and its rate converging to the desired values, the second-order LOS angle is considered as a double-integral system. Then, this paper utilizes FTC to design a controller which can guarantee the states of the double-integral system converging to the desired values. Numerical simulation illustrates the performance of the IACG, in contrast to the existing guidance law.

Without assumptions made on motion states of missile and target, an extended differential geometric guidance law is derived. Through introducing a line of sight rotation coordinate system, the derivation is simplified and has more explicit physical significances. The extended law is theoretically applicable to any engagement scenarios. Then, on basis of the extended law, a modified one is designed without the requirement of target acceleration and an approach is proposed to determining the applied direction of commanded missile acceleration. Qualitative analysis is carried out to study the capture performance and a criterion for capture is given. Simulation results indicate the two laws are effective and make up the deficiency that pure proportional navigation suitable for endoatmospheric interceptions cannot deal with high-speed maneuvering targets. Furthermore, the correctness of the criterion is validated.

Focusing on obstacle avoidance in three-dimensional space for unmanned aerial vehicle (UAV), the direct obstacle avoidance method in dynamic space based on three-dimensional velocity obstacle spherical cap is proposed, which quantifies the influence of threatening obstacles through velocity obstacle spherical cap parameters. In addition, the obstacle avoidance schemes of any point on the critical curve during the multi-obstacles avoidance are given. Through prediction, the insertion point for the obstacle avoidance can be obtained and the flight path can be replanned. Taking the Pythagorean Hodograph (PH) curve trajectory re-planning as an example, the three-dimensional direct obstacle avoidance method in dynamic space is tested. Simulation results show that the proposed method can realize the online obstacle avoidance trajectory re-planning, which increases the flexibility of obstacle avoidance greatly.

Accurate prediction of server load is important to cloud systems for improving the resource utilization, reducing the energy consumption and guaranteeing the quality of service (QoS). This paper analyzes the features of cloud server load and the advantages and disadvantages of typical server load prediction algorithms, integrates the cloud model (CM) and the Markov chain (MC) together to realize a new CM-MC algorithm, and then proposes a new server load prediction algorithm based on CM-MC for cloud systems. The algorithm utilizes the historical data sample training method of the cloud model, and utilizes the Markov prediction theory to obtain the membership degree vector, based on which the weighted sum of the predicted values is used for the cloud model. The experiments show that the proposed prediction algorithm has higher prediction accuracy than other typical server load prediction algorithms, especially if the data has significant volatility. The proposed server load prediction algorithm based on CM-MC is suitable for cloud systems, and can help to reduce the energy consumption of cloud data centers.

To overcome the shortcomings of the Lee image enhancement algorithm and its improvement based on the logarithmic image processing (LIP) model, this paper proposes what we believe to be an effective image enhancement algorithm. This algorithm introduces fuzzy entropy, makes full use of neighborhood information, fuzzy information and human visual characteristics. To enhance an image, this paper first carries out the reasonable fuzzy-3 partition of its histogram into the dark region, intermediate region and bright region. It then extracts the statistical characteristics of the three regions and adaptively selects the parameter α according to the statistical characteristics of the image’s gray-scale values. It also adds a useful nonlinear transform, thus increasing the ubiquity of the algorithm. Finally, the causes for the gray-scale value overcorrection that occurs in the traditional image enhancement algorithms are analyzed and their solutions are proposed. The simulation results show that our image enhancement algorithm can effectively suppress the noise of an image, enhance its contrast and visual effect, sharpen its edge and adjust its dynamic range.

Under Type-II progressively hybrid censoring, this paper discusses statistical inference and optimal design on stepstress partially accelerated life test for hybrid system in presence of masked data. It is assumed that the lifetime of the component in hybrid systems follows independent and identical modified Weibull distributions. The maximum likelihood estimations (MLEs) of the unknown parameters, acceleration factor and reliability indexes are derived by using the Newton-Raphson algorithm. The asymptotic variance-covariance matrix and the approximate confidence intervals are obtained based on normal approximation to the asymptotic distribution of MLEs of model parameters. Moreover, two bootstrap confidence intervals are constructed by using the parametric bootstrap method. The optimal time of changing stress levels is determined under D-optimality and A-optimality criteria. Finally, the Monte Carlo simulation study is carried out to illustrate the proposed procedures.

For the large number of nonlinear degradation devices existing in a project, the existing methods have not systematically studied the effects of random effect on the remaining lifetime (RL), the accuracy and efficiency of the parameters estimation are not high, and the current degradation state of the target device is not accurately estimated. In this paper, a nonlinear Wiener degradation model with random effect is proposed and the corresponding probability density function (PDF) of the first hitting time (FHT) is deduced. A parameter estimation method based on modified expectation maximum (EM) algorithm is proposed to obtain the estimated value of fixed coefficient and the priori value of random coefficient in the model. The posterior value of the random coefficient and the current degradation state of target device are updated synchronously by the state space model (SSM) and the Kalman filter algorithm. The PDF of RL with random effect is deduced. A simulation example is analyzed to verify that the proposed method has the obvious advantage over the existing methods in parameter estimation error and RL prediction accuracy.