Dual-frequency multi-constellation (DFMC) satellitebased augmentation system (SBAS) does not broadcast fast correction, which is important in reducing range error in L1-only SBAS. Meanwhile, the integrity bound of a satellite at low elevation is so loose that the service availability is decreased near the boundary of the service area. Therefore, the computation of satellite clockephemeris (SCE) augmentation parameters needs improvement. We propose a method introducing SCE prediction to eliminate most of the SCE error resulting from global navigation satellite system GNSS broadcast message. Compared with the signal-inspace (SIS) after applying augmentation parameters broadcast by the wide area augmentation system (WAAS), SIS accuracy after applying augmentation parameters computed by the proposed algorithm is improved and SIS integrity is ensured. With global positioning system (GPS) only, the availability of category-I (CAT-I) with a vertical alert level of 15 m in continental United States is about 90%, while the availability in the other part of the WAAS service area is markedly improved. With measurements made by the stations from the crustal movement observation network of China, users in some part of China can obtain CAT-I (vertical alert limit is 15 m) service with GPS and global navigation satellite system (GLONASS).

This paper presents a hardware architecture using mixed pipeline and parallel processing for complex division based on dichotomous coordinate descent (DCD) iterations. The objective of the proposed work is to achieve low-latency and resource optimized complex divider architecture in adaptive weight computation stage of minimum variance distortionless response (MVDR) algorithm. In this work, computation of complex division is modeled as a 2×2 linear equation solution problem and the DCD algorithm allows linear systems of equations to be solved with high degree of computational efficiency. The operations in the existing DCD algorithm are suitably parallel pipelined and the performance is optimized to 2 clock cycles per iteration. To improve the degree of parallelism, a parallel column vector read architecture is devised. The proposed work is implemented on the field programmable gate array (FPGA) platform and the results are compared with state-of-art literature. It concludes that the proposed architecture is suitable for complex division in adaptive weight computation stage of MVDR beamformer. We demonstrate the performance of the proposed architecture for MVDR beamformer employed in medical ultrasound imaging applications.

Two improved structures of high resolution digital pulse width modulator (DPWM) control circuit are proposed. Embedded digital clock manager (DCM) blocks and digital programmable delay circuits are employed as the basic resources to construct the field-programmable gate array (FPGA)-based DPWM implementations. Detailed schemes are illustrated and the circuits have been successfully implemented on the Artix-7 FPGA device developed by Xilinx. Experimental results show that when the basic clock operates at the frequency of 200 MHz, the resolutions of the two approaches can reach 625 ps and 500 ps, respectively. Besides, the presented schemes possess other merits including flexible resolution, strong versatility and relatively good stability.

In this paper, a new approach of maneuvering target tracking algorithm based on the autoregressive extended Viterbi (AREV) model is proposed. In contrast to weakness of traditional constant velocity (CV) and constant acceleration (CA) models to noise effect reduction, the autoregressive (AR) part of the new model which changes the structure of state space equations is proposed. Also using a dynamic form of the state transition matrix leads to improving the rate of convergence and decreasing the noise effects. Since AR will impose the load of overmodeling to the computations, the extended Viterbi (EV) method is incorporated to AR in two cases of EV1 and EV2. According to most probable paths in the interacting multiple model (IMM) during nonmaneuvering and maneuvering parts of estimation, EV1 and EV2 respectively can decrease load of overmodeling computations and improve the AR performance. This new method is coupled with proposed detection schemes for maneuver occurrence and termination as well as for switching initializations. Appropriate design parameter values are derived for the detection schemes of maneuver occurrences and terminations. Finally, simulations demonstrate that the performance of the proposed model is better than the other older linear and also nonlinear algorithms in constant velocity motions and also in various types of maneuvers.

This paper extends the application of compressive sensing (CS) to the radar reconnaissance receiver for receiving the multi-narrowband signal. By combining the concept of the block sparsity, the self-adaption methods, the binary tree search, and the residual monitoring mechanism, two adaptive block greedy algorithms are proposed to achieve a high probability adaptive reconstruction. The use of the block sparsity can greatly improve the efficiency of the support selection and reduce the lower boundary of the sub-sampling rate. Furthermore, the addition of binary tree search and monitoring mechanism with two different supports self-adaption methods overcome the instability caused by the fixed block length while optimizing the recovery of the unknown signal. The simulations and analysis of the adaptive reconstruction ability and theoretical computational complexity are given. Also, we verify the feasibility and effectiveness of the two algorithms by the experiments of receiving multi-narrowband signals on an analogto-information converter (AIC). Finally, an optimum reconstruction characteristic of two algorithms is found to facilitate efficient reception in practical applications.

The total cross-eye gain of multiple-element retrodirective cross-eye jamming (MRCJ) in the presence of the platform skin return is a distribution rather than a constant value, due to the random variation in the phase of the skin return. Although the median value of the total cross-eye gain distribution had been analyzed in previous studies, the extreme values providing useful indications of the upper and lower bounds of the total cross-eye gain have not been analyzed until now. In this paper, the cumulative distribution function and the extreme values of the total cross-eye gain of MRCJ are derived. The angular error induced in threat monopulse radar as a figure of merit is used to analyze the performance of MRCJ system. Simulation results demonstrate the variation of the angular error and discuss the proper value of jamming-to-signal ratio (JSR) making the MRCJ system more effective in consideration of the whole distribution of the total cross-eye gain.

To reduce complexity, the combat effectiveness simulation system (CESS) is often decomposed into static structure, physical behavior, and cognitive behavior, and model abstraction is layered onto domain invariant knowledge (DIK) and application variant knowledge (AVK) levels. This study concentrates on the specification of CESS's physical behaviors at the DIK level of abstraction, and proposes a model driven framework for efficiently developing simulation models within model-driven engineering (MDE). Technically, this framework integrates the four-layer metamodeling architecture and a set of model transformation techniques with the objective of reducing model heterogeneity and enhancing model continuity. As a proof of concept, a torpedo example is illustrated to explain how physical models are developed following the proposed framework. Finally, a combat scenario is constructed to demonstrate the availability, and a further verification is shown by a reasonable agreement between simulation results and field observations.

An uncertain multi-objective programming problem is a special type of mathematical multi-objective programming involving uncertain variables. This type of problem is important because there are several uncertain variables in real-world problems. Therefore, research on the uncertain multi-objective programming problem is highly relevant, particularly those problems whose objective functions are correlated. In this paper, an approach that solves an uncertain multi-objective programming problem under the expected-variance value criterion is proposed. First, we define the basic framework of the approach and review concepts such as a Pareto efficient solution and expected-variance value criterion using an order relation between various uncertain variables. Second, the uncertain multi-objective problem is converted into an uncertain single-objective programming problem via a linear weighted method or ideal point method. Then the problem is transformed into a deterministic single objective programming problem under the expected-variance value criterion. Third, four lemmas and two theorems are proved to illustrate that the optimal solution of the deterministic single-objective programming problem is an efficient solution to the original uncertainty problem. Finally, two numerical examples are presented to validate the effectiveness of the proposed approach.

Ordering based search methods have advantages over graph based search methods for structure learning of Bayesian networks in terms on the efficiency. With the aim of further increasing the accuracy of ordering based search methods, we first propose to increase the search space, which can facilitate escaping from the local optima. We present our search operators with majorizations, which are easy to implement. Experiments show that the proposed algorithm can obtain significantly more accurate results. With regard to the problem of the decrease on efficiency due to the increase of the search space, we then propose to add path priors as constraints into the swap process. We analyze the coefficient which may influence the performance of the proposed algorithm, the experiments show that the constraints can enhance the efficiency greatly, while has little effect on the accuracy. The final experiments show that, compared to other competitive methods, the proposed algorithm can find better solutions while holding high efficiency at the same time on both synthetic and real data sets.

In order to assure quality and control process in the development of the aircraft collaborative design software, a maturity assessment model is proposed. The requirements designing— house of quality is designed to evaluate the maturity degree of the solution, and the evaluation results can help to manage and control the development process. Furthermore, a fuzzy evaluation method based on the minimum deviation is proposed to deal with the fuzzy information. The quantitative evaluation result of the maturity degree can be calculated by optimizing the semantic discount factor aim for the minimum deviation. Finally, this model is illustrated and analyzed by an example study of the aircraft collaborative design software.

Uncertain and hesitant information, widely existing in the real-world qualitative decision making problems, brings great challenges to decision makers. Hesitant fuzzy linguistic term sets (HFLTSs), an effective linguistic computational tool in modeling and eliciting such information, have hence aroused many scholars' interests and some extensions have been introduced recently. However, these methods are based on the discrete linguistic term framework with the limited expression domain, which actually depict qualitative information using several single values. Therefore, it is hard to ensure the integrity of the semantics representation and the accuracy of the computation results. To deal with this problem, a semantics basis framework called complete linguistic term set (CLTS) is designed, which adopts a separation structure of linguistic scale and expression domain, enriching semantics representation of decision makers. On this basis the concept of fuzzy interval linguistic sets (FILSs) is put forward that employs the interval linguistic term with probability to increase the flexibility of eliciting and representing uncertain and hesitant qualitative information. For practical applications, a fuzzy interval linguistic technique for order preference by similarity to ideal solution (FILTOPSIS) method is developed to deal with multi-attribute group decision making (MAGDM) problems. Through the cases of movie and enterprise resource planning (ERP) system selection, the effectiveness and validity of the proposed method are illustrated.

A composited integrated guidance and control (IGC) algorithm is presented to tackle the problem of the IGC design in the dive phase for the bank-to-turn (BTT) vehicle with the inaccuracy information of the line-of-sight (LOS) rate. For the sake of theoretical derivation, an IGC model in the pitch plane is established. The high-order finite-time state observer (FTSO), with the LOS angle as the single input, is employed to reconstruct the states of the system online. Besides, a composited IGC algorithm is presented via the fusion of back-stepping and dynamic inverse. Compared with the traditional IGC algorithm, the proposed composited IGC method can attenuate effectively the design conservation of the flight control system, while the LOS rate is mixed with noise. Extensive experiments have been performed to demonstrate that the proposed approach is globally finite-time stable and strongly robust against parameter uncertainty.

This paper proposes a multiple-constraints-guaranteed midcourse guidance law for the interception of the hypersonic targets. In traditional midcourse law design, the constraints of the aero-thermal heating are rarely taken into consideration. The performance of the infrared detection system may be degraded and the instability of the flight control system may be induced. To address this problem, a state-constrained model predictive static programming method is introduced such that both terminal constraints (position and angle) and optimal energy consumption can be ensured. As a result, a sub-optimal midcourse guidance, guaranteeing the aforementioned multiple-constraints to be never violated, is synthesized. Simulation results demonstrate the effectiveness of the proposed method.

The path-following control of the asymmetry underactuated unmanned surface vehicle (USV) under external disturbances such as unknown constant and irrational ocean currents is discussed, and an adaptive sliding-mode path-following control system is proposed, which comprises a path-variable updated law, a modified integral line-of-sight (ILOS) guidance law based on a time-varying lookahead distance and adaptive feedback linearizing controllers combined with sliding-mode technique. A more accurate USV model without the assumption of having diagonal inertia and damping matrices is first presented, aiming at improving the performance of the path-following control. Next, the coordinate transformation is adopted to decouple the sway dynamic from the rudder angle, and the path-following errors dynamics without non-singular problem are presented in the moving Frenet-Serret frame. Then, based on the cascaded theorem and the adaptive sliding-mode method, the adaptive control law of position errors and course error are designed, among which the lookahead distance and integral gain are all computed as different functions of cross-track error to estimate and compensate the sideslip angle caused by external disturbances adaptively. Finally, according to the Lyapunov and cascaded theorem, the control system proposed is proved to be uniform globally asymptotic stability (UGAS) and uniform semiglobal exponential stability (USGES) when the control objectives are all achieved. Simulation results illustrate the precision and high-quality performance of this new controller.

Aiming at handling complicated maneuvers or other unpredicted emergencies for hypersonic glide vehicle tracking, three coupled dynamic models of state estimation based on the priori information between guidance variables and aerodynamics are presented. Firstly, the aerodynamic acceleration acting on the target is analyzed to reveal the essence of the target's motion. Then three coupled structures for modeling aerodynamic parameters are developed by different ideas: the spiral model with a harmonic oscillator, the bank model with trigonometric functions of the bank angle and the guide model with the changing rule of guidance variables. Meanwhile, the comparison discussion is concluded to show the novelty and advantage of these models. Finally, a performance assessment in different simulation cases is presented and detailed analysis is revealed. The results show that the proposed models perform excellent properties. Moreover, the guide model produces the best tracking performance and the bank model shows the second; however, the spiral model does not outperform the maneuvering reentry vehicle (MaRV) model markedly.

Unmanned aerial vehicles (UAVs) may play an important role in data collection and offloading in vast areas deploying wireless sensor networks, and the UAV's action strategy has a vital influence on achieving applicability and computational complexity. Dynamic programming (DP) has a good application in the path planning of UAV, but there are problems in the applicability of special terrain environment and the complexity of the algorithm. Based on the analysis of DP, this paper proposes a hierarchical directional DP (DDP) algorithm based on direction determination and hierarchical model. We compare our methods with Q-learning and DP algorithm by experiments, and the results show that our method can improve the terrain applicability, meanwhile greatly reduce the computational complexity.

This paper concerns the problem of object segmentation in real-time for picking system. A region proposal method inspired by human glance based on the convolutional neural network is proposed to select promising regions, allowing more processing is reserved only for these regions. The speed of object segmentation is significantly improved by the region proposal method. By the combination of the region proposal method based on the convolutional neural network and superpixel method, the category and location information can be used to segment objects and image redundancy is significantly reduced. The processing time is reduced considerably by this to achieve the real time. Experiments show that the proposed method can segment the interested target object in real time on an ordinary laptop.

In order to achieve the information fusion in the time domain based on the evidence theory, an evidence combination method in the time domain based on reliability and importance is proposed according to the idea of evidence discount. Firstly, the distortion of the time-domain evidence is judged based on single exponential smoothing. The real-time reliability of the evidence at the adjacent time is obtained by the real-time reliability assessment method of the evidence based on the credibility decay model. Then, the relative importance of the evidence at the adjacent time is obtained by comprehensively considering improved conflict degree and uncertainty. Finally, based on the criterion of evidence discount and the Dempster's rule of combination, the evidence combination is carried out to achieve the sequential combination of time-domain evidence. The numerical simulation and analysis show that this method has fully embodied the dynamic characteristics of time-domain evidence combination, and it has strong processing ability for conflict information and anti-disturbing ability. The proposed method has good applicability to information fusion in the time domain.

The present study proposed an enhanced cuckoo search (ECS) algorithm combined with artificial neural network (ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and convergence rate of the original cuckoo search (CS) algorithm, the main parameters namely, abandon probability of worst nests p_aand search step size α₀ are dynamically adjusted via nonlinear control equations. In addition, a global-best guided equation incorporating the information of global best nest is introduced to the ECS to enhance its exploitation. Then, the proposed ECS is linked to the well-trained ANN model for structural reliability analysis. The computational capability of the proposed algorithm is validated using five typical structural reliability problems and an engineering application. The comparison results show the efficiency and accuracy of the proposed algorithm.

The wheel brake system safety is a complex problem which refers to its technical state, operating environment, human factors, etc., in aircraft landing taxiing process. Usually, professors consider system safety with traditional probability techniques based on the linear chain of events. However, it could not comprehensively analyze system safety problems, especially in operating environment, interaction of subsystems, and human factors. Thus, we consider system safety as a control problem based on the system-theoretic accident model, the processes (STAMP) model and the system theoretic process analysis (STPA) technique to compensate the deficiency of traditional techniques. Meanwhile, system safety simulation is considered as system control simulation, and Monte Carlo methods are used which consider the range of uncertain parameters and operation deviation to quantitatively study system safety influence factors in control simulation. Firstly, we construct the STAMP model and STPA feedback control loop of the wheel brake system based on the system functional requirement. Then four unsafe control actions are identified, and causes of them are analyzed. Finally, we construct the Monte Carlo simulation model to analyze different scenarios under disturbance. The results provide a basis for choosing corresponding process model variables in constructing the context table and show that appropriate brake strategies could prevent hazards in aircraft landing taxiing.