By using the sparsity of frequency hopping (FH) signals, an underdetermined blind source separation (UBSS) algorithm is presented. Firstly, the short time Fourier transform (STFT) is performed on the mixed signals. Then, the mixing matrix, hopping frequencies, hopping instants and the hooping rate can be estimated by the K-means clustering algorithm. With the estimated mixing matrix, the directions of arrival (DOA) of source signals can be obtained. Then, the FH signals are sorted and the FH pattern is obtained. Finally, the shortest path algorithm is adopted to recover the time domain signals. Simulation results show that the correlation coefficient between the estimated FH signal and the source signal is above 0.9 when the signal-to-noise ratio (SNR) is higher than 0 dB and hopping parameters of multiple FH signals in the synchronous orthogonal FH network can be accurately estimated and sorted under the underdetermined conditions.

A higher-order cumulant-based weighted least square (HOCWLS) and a higher-order cumulant-based iterative least square (HOCILS) are derived for multiple inputs single output(MISO) errors-in-variables (EIV) systems from noisy input/output data. Whether the noises of the input/output of the system are white or colored, the proposed algorithms can be insensitive to these noises and yield unbiased estimates. To realize adaptive parameter estimates, a higher-order cumulant-based recursive least square (HOCRLS) method is also studied. Convergence analysis of the HOCRLS is conducted by using the stochastic process theory and the stochastic martingale theory. It indicates that the parameter estimation error of HOCRLS consistently converges to zero under a generalized persistent excitation condition. The usefulness of the proposed algorithms is assessed through numerical simulations.

Over the past few decades, the world has witnessed a rapid growth in mobile and wireless networks (MWNs) which significantly change human life. However, proliferating mobile demands lead to several intractable challenges that MWN has to face. Software-defined network is expected as a promising way for future network and has captured growing attention. Network virtualization is an essential feature in software-defined wireless network (SDWN), and it brings two new entities, physical networks and virtual networks. Accordingly, efficiently assigning spectrum resource to virtual networks is one of the fundamental problems in SDWN. Directly orienting towards the spectrum resource allocation problem, firstly, the fluctuation features of virtual network requirements in SDWN are researched, and the opportunistic spectrum sharing method is introduced to SDWN. Then, the problem is proved as NP-hardness. After that, a dynamic programming and graph theory based spectrum sharing algorithm is proposed. Simulations demonstrate that the opportunistic spectrum sharing method conspicuously improves the system performance up to around 20%-30% in SDWN, and the proposed algorithm achieves more efficient performance.

A polynomial-rooting based fourth-order cumulant algorithm is presented for direction-of-arrival (DOA) estimation of second-order fully noncircular source signals, using a uniform linear array (ULA). This algorithm inherits all merits of its spectralsearching counterpart except for the applicability to arbitrary array geometry, while reducing considerably the computation cost. Simulation results show that the proposed algorithm outperforms the previously developed closed-form second-order noncircular ESPRIT method, in terms of processing capacity and DOA estimation accuracy, especially in the presence of spatially colored noise.

In a system based on the phase lock loop (PLL), a trade-off must be made between the tracking precision and the dynamic performance if constant parameters are adopted. To overcome this drawback, a new method called no phase slipping adaptive bandwidth (NPS-AB) is proposed, which can adjust the loop bandwidth adaptively for different working conditions. As a result, both the tracking precision and the dynamic performance can be achieved concurrently. NPS-AB has two features to keep the loop stable: one is the capability of quick response to dynamics; the other is a series of additional constraints when the bandwidth is switched. Compared with other methods, there is no phase slipping during the adjustment process for NPS-AB. The phase integer ambiguity can be avoided and the phase value is kept valid. It is meaningful for carrier ranging systems. Simulation results show that NPS-AB can deal with sudden dynamics and keep the pseudo-range value stable in the entire dynamic process.

Dezert-Smarandache (DSm) theory, a new information fusion theory, is widely applied in image processing, multiple targets tracking identification, and other areas for its excellent processing ability of imperfect information. However, earlier research on DSm theory mainly focused on one sort of questions. An evidence fusion procedure is proposed based on the hybrid DSm model to compensate for a lack of research on the entire information procedure of DSm theory. This paper analyzes the evidence fusion procedure, as well as correlative node input and output information. Key steps and detailed procedures of evidence fusion are also discussed. Finally, an experiment illustrates the efficiency of the proposed evidence fusion procedure.

In order to improve the survivability of the aircraft, conceptual design and radar cross section (RCS) performance research are done. The CATIA software is used to design the 3D digital model of the shipborne early warning aircraft, and some measures are taken to reduce the RCS characteristics of the early warning aircraft at the same time. Based on the physical optics method and the equivalent electromagnetic flow method, the aircraft's RCS characteristics and strength distribution characteristics are simulated numerically, and compared with the foreign advanced shipborne early warning aircraft. The simulation results show that under the X radar band, when the incident wave pitching angle is  0^?, compared with the foreign advanced shipborne early warning aircraft, the forward RCS average value of the conceptual shipborne early warning aircraft is reduced to 24.49%, the lateral RCS average value is reduced to 5.04%, and the backward RCS average value is reduced to 39.26%. The research results of this paper are expected to provide theoretical basis and technical support for the conceptual design and the stealth design of the shipborne early warning aircraft.

Imaging the spatial precession cone-shaped targets with narrowband radar is a new technical approach in mid-course recognition problem. However, most existing time-frequency methods still have some inevitable deficiencies for extracting micro-Doppler information in practical applications, which leads to blurring of the image. A new narrowband radar imaging algorithm for the precession cone-shaped targets is proposed. The instantaneous frequency of each scattering point is gained by using the improved Hilbert-Huang transform, then the positions of scattering points in the parameter domain are reconstructed. Numerical simulation and experiment results confirm the effectiveness and high precision of the proposed algorithm.

A grating lobes suppression method for chirp-subpulse stepped frequency (CSSF) signals is proposed, which is applied to deformation monitoring using the ground based synthetic aperture radar (GB-SAR) system. This method is based on accurate estimation and correction of the phase and amplitude error along two dimensions (range and azimuth), i.e., the error estimation inside the subpulse (in-subpulse error) and across the stepped frequency subpulses (cross-subpulse error) of transmitted CSSF signals. Validated both with simulated data and experimental data recorded in the deformation monitoring campaign, it can be seen that the method as well as the relative conclusions can be fully and effectively applied to most of the stepped frequency systems.

The interrupted sampling repeater jamming (ISRJ) is an effective deception jamming method for coherent radar, especially for the wideband linear frequency modulation (LFM) radar. An electronic counter-countermeasure (ECCM) scheme is proposed to remove the ISRJ-based false targets from the pulse compression result of the de-chirping radar. Through the time-frequency (TF) analysis of the radar echo signal, it can be found that the TF characteristics of the ISRJ signal are discontinuous in the pulse duration because the ISRJ jammer needs short durations to receive the radar signal. Based on the discontinuous characteristics a particular band-pass filter can be generated by two alternative approaches to retain the true target signal and suppress the ISRJ signal. The simulation results prove the validity of the proposed ECCM scheme for the ISRJ.

Sensor platforms with active sensing equipment such as radar may betray their existence, by emitting energy that will be intercepted by enemy surveillance sensors. The radar with less emission has more excellent performance of the low probability of intercept (LPI). In order to reduce the emission times of the radar, a novel sensor selection strategy based on an improved interacting multiple model particle filter (IMMPF) tracking method is presented. Firstly the IMMPF tracking method is improved by increasing the weight of the particle which is close to the system state and updating the model probability of every particle. Then a sensor selection approach for LPI takes use of both the target's maneuverability and the state's uncertainty to decide the radar's radiation time. The radar will work only when the target's maneuverability and the state's uncertainty exceed the control capability of the passive sensors. Tracking accuracy and LPI performance are demonstrated in the Monte Carlo simulations.

The uncertainty analysis is an effective sensitivity analysis method for system model analysis and optimization. However, the existing single-factor uncertainty analysis methods are not well used in the logistic support systems with multiple decision-making factors. The multiple transfer parameters graphical evaluation and review technique (MTP-GERT) is used to model the logistic support process in consideration of two important factors, support activity time and support activity resources, which are two primary causes for the logistic support process uncertainty. On this basis, a global sensitivity analysis (GSA) method based on covariance is designed to analyze the logistic support process uncertainty. The aircraft support process is selected as a case application which illustrates the validity of the proposed method to analyze the support process uncertainty, and some feasible recommendations are proposed for aircraft support decision making on carrier.

How to reduce downtime and improve availability of the complex equipment is very important. Although the unscheduled downtime (USDT) issues of the equipment are very complex, the self-organized criticality (SOC) is the right theory to study complex systems evolution and opens up a new window to the investigation of disasters, such as the sudden failure of the equipment. Firstly, SOC theory and its validation method are introduced. Then an SOC validation method for USDT of the equipment is proposed based on the above theory. Case study is done on bottleneck equipment in a factory and corresponding data pre-process work is done. The rescaled-range (R/S) analysis method is used to calculate the Hurst exponent of USDT time-series data in order to determine the long-range correlation of USDT data on time scale; at the same time the spatial power-law characteristic of USDT time series data is studied. The result shows that the characteristics of SOC are revealed in USDT data of the equipment according to the criterion of SOC. In addition, based on the characteristics of SOC, the overall framework of the prediction method for major sudden failure of the equipment is proposed based on SOC.

How to allocate a resource efficiently and fairly attracts the attention of both researchers and practitioners. Data envelopment analysis (DEA) has been brought to bear on its solution. The existing literature applies Gini coefficient to measure the fairness in the resource allocation process. However, the Gini coefficient is inapplicable in many applications. This paper proposes a novel centralized resource allocation model based on DEA that considers both the efficiency and the fairness. This paper adopts a notion of fairness, namely α-fairness that is well studied in welfare economics and is of practical significance. The new model integrates α-fairness with DEA to support resource allocation decisions. It aids decision makers in making a trade-off between the efficiency and the fairness. An illustrative application is used to validate the proposed approach.

For the gray attributes of the equipment program and its difficulty to carry out the quantitative assessment of the equipment program information, the gray relation projection method is simply reviewed. Combining the super-data envelopment analysis (DEA) model and the gray system theory, a new super-DEA for measuring the weight is proposed, and a gray relation projection model is established to rank the equipment programs. Finally, this approach is used to evaluate the equipment program. The results are verified valid and can provide a new way for evaluating the equipment program.

Transient performance for output regulation problems of linear discrete-time systems with input saturation is addressed by using the composite nonlinear feedback (CNF) control technique. The regulator is designed to be an additive combination of a linear regulator part and a nonlinear feedback part. The linear regulator part solves the regulation problem independently which produces a quick output response but large oscillations. The nonlinear feedback part with well-tuned parameters is introduced to improve the transient performance by smoothing the oscillatory convergence. It is shown that the introduction of the nonlinear feedback part does not change the solvability conditions of the linear discrete-time output regulation problem. The effectiveness of transient improvement is illustrated by a numeric example.

This paper studies the weighted average consensus problem for networks of agents with fixed directed asymmetric unbalance information exchange topology. We suppose that the classical distributed consensus protocol is destroyed by diverse time-delays which include communication time-delay and self time-delay. Based on the generalized Nyquist stability criterion and the Gerschgorin disk theorem, some sufficient conditions for the consensus of multi-agent systems are obtained. And we give the expression of the weighted average consensus value for our consensus protocol. Finally, numerical examples are presented to illustrate the theoretical results.

A new two-iteration sculling compensation mathematical framework is provided for modern-day strapdown inertial navigation system (SINS) algorithm design that utilizes a new concept in velocity updating. The principal structure of this framework includes twice sculling compensation procedure using incremental outputs from the inertial system sensors during the velocity updating interval. Then, the moderate algorithm is designed to update the velocity parameter. The analysis is conducted in the condition of sculling motion which indicates that the new mathematical framework error which is smaller than the conventional ones by at least two orders is far superior. Therefore, a summary is given for SINS software which can be designed with the new mathematical framework in velocity updating.

Based on image strip dividing, an effective and fast image retargeting algorithm is proposed for resizing images. First, we construct the image energy map using gradient magnitude of the pixels and calculate the accumulated energy of each column, dividing the image into several strips by integrating similar energy columns. The reduced amount of dimension is decided in inverse proportion to the average energy for each strip. Then we retarget the image combining scaling with cropping in terms of each strip’s reduced ratio. Experiment results show that the proposed algorithm is capable of implementing fast image retargeting and preserving both the local structures and the global visual effect of the image.

Removal of cloud cover on the satellite remote sensing image can effectively improve the availability of remote sensing images. For thin cloud cover, support vector value contourlet transform is used to achieve multi-scale decomposition of the area of thin cloud cover on remote sensing images. Through enhancing
coefficients of high frequency and suppressing coefficients of low frequency, the thin cloud is removed. For thick cloud cover, if the areas of thick cloud cover on multi-source or multi-temporal remote sensing images do not overlap, the multi-output support vector regression learning method is used to remove this kind of thick clouds. If the thick cloud cover areas overlap, by using the multi-output learning of the surrounding areas to predict the surface features of the overlapped thick cloud cover areas, this kind of thick cloud is removed. Experimental results show that the proposed cloud removal method can effectively solve the problems of the cloud overlapping and radiation difference among multi-source images. The cloud removal image is clear and smooth.

Overlapping community detection in a network is a challenging issue which attracts lots of attention in recent years. A notion of hesitant node (HN) is proposed. An HN contacts with multiple communities while the communications are not strong or even accidental, thus the HN holds an implicit community structure. However, HNs are not rare in the real world network. It is important to identify them because they can be efficient hubs which form the overlapping portions of communities or simple attached nodes to some communities. Current approaches have difficulties in identifying and clustering HNs. A density-based rough set model (DBRSM) is proposed by combining the virtue of densitybased algorithms and rough set models. It incorporates the macro perspective of the community structure of the whole network and the micro perspective of the local information held by HNs, which would facilitate the further "growth"of HNs in community. We offer a theoretical support for this model from the point of strength of the trust path. The experiments on the real-world and synthetic datasets show the practical significance of analyzing and clustering the HNs based on DBRSM. Besides, the clustering based on DBRSM promotes the modularity optimization.

In the recent research of network sampling, some sampling concepts are misunderstood, and the variance of subnets is not taken into account. We propose the correct definition of the sample and sampling rate in network sampling, as well as the formula for calculating the variance of subnets. Then, three commonly used sampling strategies are applied to databases of the connecting nearest-neighbor (CNN) model, random network and small-world network to explore the variance in network sampling.  As proved by the results, snowball sampling obtains the most variance of subnets, but does well in capturing the network structure. The variance of networks sampled by the hub and random strategy are much smaller. The hub strategy performs well in reflecting the property of the whole network, while random sampling obtains more accurate results in evaluating clustering coefficient.

Most sensors or cameras discussed in the sensor network community are usually 3D homogeneous, even though their 2D coverage areas in the ground plane are heterogeneous. Meanwhile, observed objects of camera networks are usually simplified as 2D points in previous literature. However in actual application scenes, not only cameras are always heterogeneous with different height and action radiuses, but also the observed objects are with 3D features (i.e., height). This paper presents a sensor planning formulation addressing the efficiency enhancement of visual tracking in 3D heterogeneous camera networks that track and detect people traversing a region. The problem of sensor planning consists of three issues: (i) how to model the 3D heterogeneous cameras; (ii) how to rank the visibility, which ensures that the object of interest is visible in a camera’s field of view; (iii) how to reconfigure the 3D viewing orientations of the cameras. This paper studies the geometric properties of 3D heterogeneous camera networks and addresses an evaluation formulation to rank the visibility of observed objects. Then a sensor planning method is proposed to improve the efficiency of visual tracking. Finally, the numerical results show that the proposed method can improve the tracking performance of the system compared to the conventional strategies.