This paper investigates the maximum network throughput for resource-constrained space networks based on the delay and disruption-tolerant networking (DTN) architecture. Specifically, this paper proposes a methodology for calculating the maximum network throughput of multiple transmission tasks under storage and delay constraints over a space network. A mixed-integer linear programming (MILP) is formulated to solve this problem. Simulations results show that the proposed methodology can successfully calculate the optimal throughput of a space network under storage and delay constraints, as well as a clear, monotonic relationship between end-to-end delay and the maximum network throughput under storage constraints. At the same time, the optimization results shine light on the routing and transport protocol design in space communication, which can be used to obtain the optimal network throughput.

The transmission upper limit of a double-layer frequency selective surface (FSS) with two infinitely thin metal arrays is presented based on the study of the general equivalent transmission line model of a double-layer FSS. Results of theoretical analyses, numerical simulations and experiments show that this transmission upper limit is independent of the array and the element, which indicates that it is impossible to achieve a transmission upper limit higher than this one under a given incident and dielectricsupporting condition by the design of the periodic array. Both the applicable condition and the possible application of the transmission upper limit are discussed. The results show that the transmission upper limit not only has a good reachability, but also provides a key to effectively improve the transmission performance of a double-layer FSS or more complex frequency selective structures.

To remove the scalar ambiguity in conventional blind channel estimation algorithms, totally blind channel estimation (TBCE) is proposed by using multiple constellations. To estimate the unknown scalar, its phase is decomposed into a fractional phase and an integer phase. However, the maximum-likelihood (ML) algorithm for the fractional phase does not have closed-form solutions and suffers from high computational complexity. By exploring the structures of widely used constellations, this paper proposes a low-complexity fractional phase estimation algorithm which requires no exhaustive search. Analytical expressions of the asymptotic mean squared error (MSE) are also derived. The theoretical analysis and simulation results indicate that the proposed fractional phase estimation algorithm exhibits almost the same performance as the ML algorithm but with significantly reduced computational burden.

The pressure and horizontal particle velocity combined descriptions in the very low frequency acoustic field of shallow water integrated with the concept of effective depth of Pekeris waveguide is proposed, especially the active component of the pressure and horizontal particle velocity cross-spectrum, also called horizontal complex cross acoustic intensity, when only two normal modes are trapped in the waveguide. Both the approximate theoretic analysis and the numerical results show that the sign of the horizontal complex cross acoustic intensity active component is independent of the range when vertically deployed receiving dual sensors are placed in appropriate depths, the sum of which is equal to the waveguide effective depth, so it can be used to tell whether the sound source is near the surface or underwater; while the range rate is expected to be measured by utilizing the sign distribution characteristic of the reactive component. The further robustness analysis of the depth classification algorithm shows that the existence of shear waves in semi infinite basement and the change of acoustic velocity profiles have few effects on the application of this method, and the seabed attenuation will limit the detection range, but the algorithm still has a good robustness in the valid detection range.

It is potentially useful to perform deception jamming using the digital image synthesizer (DIS) since it can form a two-dimensional (2D) decoy but suffers from multiple decoys generation. Inspired by the intermittent sampling repeater jamming (ISRJ), the generation of inverse synthetic aperture radar (ISAR) decoys is addressed, associated with the DIS and the ISRJ. Radar pulses are sampled intermittently and modulated by the scattering model of a false target by mounting the jammer on a moving platform, and then the jamming signals are retransmitted to the radar and a train of decoys are induced after ISAR imaging. A scattering model of Yak-42 is adopted as the false-target modulation model to verify the effectiveness of the jamming method based on the standard ISAR motion compensation and image formation procedure.

Radar radio frequency (RF) stealth is very important in electronic war (EW), and waveform design and selection. Existing evaluation rules of radar RF stealth include too many parameters of radar and interceptors, such as Schleher interception factor, which makes it difficult to evaluate radar RF stealth technologies
if interceptor parameters are unknown. In communication, security capacity has been presented to describe the possible ability to communicate in complete security. Since the essential of the security capacity is to have the interceptor get none valued information from the emitter, this paper is proposed to study security information factors taking advantage of mutual information to evaluate radar RF stealth under some conditions. Through analyzing mutual information obtained by the radar and the interceptor, this paper defines the security information factor with and without cooperative jamming. Furthermore, this paper deduces the ratio of the match filter to the match incoherent filter and discuss mutual information received by the interceptor. Numerical simulations illustrate radar RF stealth effects based on the security information factor concept under different conditions.

For radar targets flying at low altitude, multiple pathways produce fade or enhancement relative to the level that would be expected in a free-space environment. In this paper, a new detection method based on a wide-ranging multi-frequency radar for low angle targets is proposed. Sequential transmitting multiple pulses with different frequencies are first applied to decorrelate the coherence of the direct and reflected echoes. After receiving all echoes, the multi-frequency samples are arranged in a sort descending according to the amplitude. Some high amplitude echoes in the same range cell are accumulated to improve the signal-to-noise ratio and the optimal number of high amplitude echoes is analyzed and given by experiments. Finally, simulation results are presented to verify the effectiveness of the method.

Based on the squint mode, a high resolution wide swath revisit synthetic aperture radar (SAR) imaging mode is proposed. The transmitting antennas are configured as the single phase center multiple azimuth beams (SPC MAB). The formed two beams point to two different directions to obtain two images of the observed scenario. The receiving antennas are configured as displaced phase center multiple azimuth beams (DPC MAB) to decrease the required pulse repetition frequency (PRF). The decreased PRF can ensure the high resolution wide swath imaging. Based on the analysis of the character of the return signal, a processing method named multiple beam multiple channel algorithm (MBMCA) is proposed to separate the aliased sub-beams’ echoes. The separated echoes are focused respectively to get the revisit imaging to the observed scenario. The simulation experiments verify the validity and correctness of the proposed imaging mode and processing algorithm.

Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network (BN), which is also necessary in promoting the application of BN. This paper proposes an immune algorithm based method (BN-IA) for the learning of the BN structure with the idea of vaccination. Furthermore, the methods on how to extract the effective vaccines from local optimal structure and root nodes are also described in details. Finally, the simulation studies are implemented with the helicopter convertor BN model and the car start BN model. The comparison results show that the proposed vaccines and the BN-IA can learn the BN structure effectively and efficiently.

Equipment systems-of-systems (SoS) effectiveness evaluation can provide important reference for construction and optimization of the equipment SoS. After discussing the basic theory and methods of parallel experiments, we depict an SoS effectiveness analysis and evaluation method using parallel experiments theory in detail. A case study is carried out which takes the missile defense system as an example. An artificial system of the missile defense system is constructed with the multi-agent modeling method. Then, single factor, multiple factors and defense position deployment computational experiments are carried out and evaluated with the statistical analysis method. Experiment results show that the altitude of the secondary interception missile is not the key factor which affects SoS effectiveness and putting the defense position ahead will increase defense effectiveness. The case study demonstrates the feasibility of the proposed method.

Command, control, communication, computing, intelligence, surveillance and reconnaissance (C4ISR) in information age is a complex system whose structure always changes actively or passively during the warfare. Therefore, it is important to optimize the structure, especially in ambiguous and quick-tempo modern warfare. This paper proposes an adaptive evolvement mechanism for the C⁴ISR structure to survive the changeable warfare. Firstly, the information age C⁴ISR structure is defined and modeled based on the complex network theory. Secondly, taking the observe, orient, decide and act (OODA) model into consideration, four kinds of loops in the C⁴ISR structure are proposed and their coefficient of networked effects (CNE) is further defined. Then, the adaptive evolvement mechanisms of the four kinds of loops are presented respectively. Finally, taking the joint air-defense C⁴ISR as an example, simulation experiments are implemented, which validate the evolvement mechanism and show that the information age C⁴ISR structure has some characteristics of small-world network and scale-free network.

A new meta-heuristic method is proposed to enhance current meta-heuristic methods for global optimization and test scheduling for three-dimensional (3D) stacked system-on-chip (SoC) by hybridizing grey wolf optimization with differential evolution (HGWO). Because basic grey wolf optimization (GWO) is easy to fall into stagnation when it carries out the operation of attacking prey, and differential evolution (DE) is integrated into GWO to update the previous best position of grey wolf Alpha, Beta and Delta, in order to force GWO to jump out of the stagnation with DE’s strong searching ability. The proposed algorithm can accelerate the convergence speed of GWO and improve its performance. Twenty-three well-known benchmark functions and an NP hard problem of test scheduling for 3D SoC are employed to verify the performance of the proposed algorithm. Experimental results show the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration.

A kind of multiple attribute group decision making (MAGDM) problem is discussed from the perspective of statistic decision-making. Firstly, on the basis of the stability theory, a new idea is proposed to solve this kind of problem. Secondly, a concrete method corresponding to this kind of problem is proposed. The main tool of our research is the technique of the jackknife method. The main advantage of the new method is that it can identify and determine the reliability degree of the existed decision making information. Finally, a traffic engineering example is given to show the effectiveness of the new method.

A relative position and attitude coupled sliding mode controller is proposed by combining the standard super twisting (ST) control and basic linear algorithm for autonomous rendezvous and docking. It is schemed for on-orbit servicing to a tumbling noncooperative target spacecraft subjected to external disturbances. A coupled dynamic model is established including both kinematical and dynamic coupled effect of relative rotation on relative translation, which illustrates the relative movement between the docking port located in target spacecraft and another in service spacecraft. The modified super twisting (MST) control algorithm containing linear compensation items is schemed to manipulate the relative position and attitude synchronously. The correction provides more robustness and convergence velocity for dealing with linearly growing perturbations than the ST control algorithm. Moreover, the stability characteristic of closed-loop system is analyzed by Lyapunov method. Numerical simulations are adopted to verify the analysis with the comparison between MST and ST control algorithms. Simulation results demonstrate that the proposed MST controller is characterized by high precision, strong robustness and fast convergence velocity to attenuate the linearly increasing perturbations.

Traditional strapdown inertial navigation system (SINS) algorithm studies are based on ideal measurements from gyros and accelerometers, while in the actual strapdown inertial measurement unit (SIMU), time-asynchrony between each inertial sensor is inevitable. Testing principles and methods for timeasynchrony parameter identification are studied. Under the singleaxis swaying environment, the relationships between the SINS platform drift rate and the gyro time-asynchrony are derived using the SINS attitude error equation. It is found that the gyro timeasynchrony error can be considered as a kind of pseudo-coning motion error caused by data processing. After gyro testing and synchronization, the single-axis tumble test method is introduced for the testing of each accelerometer time-asynchrony with respect to the ideal gyro triad. Accelerometer time-asynchrony parameter identification models are established using SINS specific force equation. Finally, all of the relative time-asynchrony parameters between inertial sensors are well identified by using fiber optic gyro SIMU as experimental verification.

Focusing on the three-dimensional guidance problem in case of target maneuvers and response delay of the autopilot, the missile guidance law utilizing active disturbance rejection control (ADRC) is proposed. Based on the nonlinear three-dimensional missile target engagement kinematics, the guidance model is established. The target acceleration is treated as a disturbance and the dynamics of the autopilot is considered by using a first-order model. A nonlinear continuous robust guidance law is designed by using a cascaded structure ADRC controller. In this method the disturbance is estimated by using the extended state observer (ESO) and compensated during each sampling period. Simulation results show that the proposed cascaded loop structure is a viable solution to the guidance law design and has strong robustness with respect to target maneuvers and response delay of the autopilot.

The blurred image restoration method can dramatically highlight the image details and enhance the global contrast, which is of benefit to improvement of the visual effect during practical applications. This paper is based on the dark channel prior principle and aims at the prior information absent blurred image degradation situation. A lot of improvements have been made to estimate the transmission map of blurred images. Since the dark channel prior principle can effectively restore the blurred image at the cost of a large amount of computation, the total variation (TV) and image morphology transform (specifically top-hat transform and bottomhat transform) have been introduced into the improved method. Compared with original transmission map estimation methods, the proposed method features both simplicity and accuracy. The estimated transmission map together with the element can restore the image. Simulation results show that this method could inhibit the ill-posed problem during image restoration, meanwhile it can greatly improve the image quality and definition.

The trustworthiness analysis and evaluation are the bases of the trust chain transfer. In this paper the formal method of trustworthiness analysis of a system based on the noninterference (NI) theory of the information flow is studied. Firstly, existing methods cannot analyze the impact of the system states on the trustworthiness of software during the process of trust chain transfer. To solve this problem, the impact of the system state on trustworthiness of software is investigated, the run-time mutual interference behavior of software entities is described and an interference model of the access control automaton of a system is established. Secondly, based on the intransitive noninterference (INI) theory, a formal analytic method of trustworthiness for trust chain transfer is proposed, providing a theoretical basis for the analysis of dynamic trustworthiness of software during the trust chain transfer process. Thirdly, a prototype system with dynamic trustworthiness on a platform with dual core architecture is constructed and a verification algorithm of the system trustworthiness is provided. Finally, the monitor hypothesis is extended to the dynamic monitor hypothesis, a theorem of static judgment rule of system trustworthiness is provided, which is useful to prove dynamic trustworthiness of a system at the beginning of system construction. Compared with previous work in this field, this research proposes not only a formal analytic method for the determination of system trustworthiness, but also a modeling method and an analysis algorithm that are feasible for practical implementation.

Identifying influential nodes in complex networks is still an open issue. In this paper, a new comprehensive centrality measure is proposed based on the Dempster-Shafer evidence theory. The existing measures of degree centrality, betweenness centrality and closeness centrality are taken into consideration in the proposed method. Numerical examples are used to illustrate the effectiveness of the proposed method.

The artificial bee colony (ABC) algorithm is a competitive stochastic population-based optimization algorithm. However, the ABC algorithm does not use the social information and lacks the knowledge of the problem structure, which leads to insufficiency in both convergent speed and searching precision. Archimedean copula estimation of distribution algorithm (ACEDA) is a relatively simple, time-economic and multivariate correlated EDA. This paper proposes a novel hybrid algorithm based on the ABC algorithm and ACEDA called Archimedean copula estimation of distribution based on the artificial bee colony (ACABC) algorithm. The hybrid algorithm utilizes ACEDA to estimate the distribution model and then uses the information to help artificial bees to search more efficiently in the search space. Six benchmark functions are introduced to assess the performance of the ACABC algorithm on numerical function optimization. Experimental results show that the ACABC algorithm converges much faster with greater precision compared with the ABC algorithm, ACEDA
and the global best (gbest)-guided ABC (GABC) algorithm in most of the experiments.

An accelerated proportional degradation hazards-odds model is proposed. It is a non-parametric model and thus has pathfree and distribution-free properties, avoiding the errors caused by faulty assumptions of degradation paths or distribution of degradation measurements. It is established based on a link function which combines the degradation cumulative hazard rate function and the degradation odds function through a transformation parameter, and this makes the accelerated proportional degradation hazards model and the accelerated proportional degradation odds model special cases of it. Hypothesis tests are discussed, and the proposed model is applicable when some model assumptions are satisfied. This model is utilized to estimate the reliability of miniature bulbs under low stress levels based on the degradation data obtained under high stress levels to validate the effectiveness of this model.

Recent studies have shown that software is one of the main reasons for computer systems unavailability. A growing accumulation of software errors with time causes a phenomenon called software aging. This phenomenon can result in system performance degradation and eventually system hang/crash. To cope with software aging, software rejuvenation has been proposed. Software rejuvenation is a proactive technique which leads to removing the accumulated software errors by stopping the system, cleaning up its internal state, and resuming its normal operation. One of the main challenges of software rejuvenation is accurately predicting the time to crash due to aging factors such as memory leaks. In this paper, different machine learning techniques are compared to accurately predict the software time to crash under different aging scenarios. Finally, by comparing the accuracy of different techniques, it can be concluded that the multilayer perceptron neural network has the highest prediction accuracy among all techniques studied.

The exchanged hypercube EH(s, t) (where s ≥1 and t ≥1) is obtained by systematically reducing links from a regular hypercube Q_s+t+1. One-step diagnosis of exchanged hypercubes which involves only one testing phase during which processors test each other is discussed. The diagnosabilities of exchanged hypercubes are studied by using the pessimistic one-step diagnosis strategy under two kinds of diagnosis models: the PMC model and the MM* model. The main results presented here are the two proofs that the degree of diagnosability of the EH(s, t) under pessimistic one-step t1/t1 fault diagnosis strategy is 2s where 1 ≤ s ≤ t (respectively, 2t, where 1 ≤ t ≤ s) based on the PMC model and that it is also 2s where 1 ≤ s ≤ t (respectively, 2t, where 1 ≤ t ≤ s) based on the MM* model.