In consultative committee for space data systems (CCSDS) file delivery protocol (CFDP) recommendation of reliable transmission, there are no detail transmission rocedure and delay calculation of prompted negative acknowledge and asynchronous negative acknowledge models. CFDP is designed to provide data and storage management, story and forward, custody transfer and reliable end-to-end delivery over deep space characterized by huge latency, intermittent link, asymmetric bandwidth and big bit error rate (BER). Four reliable transmission models are analyzed and an expected file-delivery time is calculated with different transmission rates, numbers and sizes of packet data units, BERs and frequencies of external events, etc. By comparison of four CFDP models, the requirement of BER for typical missions in deep space is obtained and rules of choosing CFDP models under different uplink state informations are given, which provides references for protocol models selection, utilization and  odification.

To minimize the total transmit power for multicast service in an orthogonal frequency division multiplexing (OFDM) downlink system, resource allocation algorithms that adaptively allocate subcarriers and bits are proposed. The proposed algorithms select users with good channel conditions for each subcarrier to reduce the transmit power, while guaranteeing each user’s instantaneous minimum rate requirement. The resource allocation problem is first formulated as an integer programming (IP) problem, and then, a full search algorithm that achieves an optimal solution is presented. To reduce the computation load, a suboptimal algorithm is proposed. This suboptimal algorithm decouples the joint resource allocation problem by separating subcarrier and bit allocation. Greedy-like algorithms are employed in both procedures. Simulation results illustrate that the proposed algorithms can significantly reduce the transmit power compared with the conventional multicast approach and the performance of the suboptimal algorithm is close to the optimum.

Traditionally, beamforming using fractional Fourier transform (FrFT) involves a trial-and-error based FrFT order selection which is impractical. A new numerical order selection scheme is presented based on fractional power spectra (FrFT moment) of the linear chirp signal. This method can adaptively determine the optimum FrFT order by maximizing the second-order central FrFT moment. This makes the desired chirp signal substantially concentrated whereas the noise is rejected considerably. This improves the mean square error minimization beamformer by reducing effectively the signal-noise cross terms due to the finite data length de-correlation operation.  Simulation results show that the new method works well under a wide range of signal to noise ratio and signal to interference ratio.

A modified pseudo-noise (PN) code regeneration method is proposed to improve the clock tracking accuracy without impairing the code acquisition time performance. Thus, the method can meet the requirement of high accuracy ranging measurements in short time periods demanded by radio-science missions. The tracking error variance is derived by linear analysis. For some existing PN codes, which can be acquired rapidly, the tracking error variance performance of the proposed method is about 2.6 dB better than that of the JPL scheme (originally proposed by Jet Propulsion Laboratory), and about 1.5 dB better than that of the traditional double loop scheme.

A phase-domain blind estimator of symbol duration based on Haar wavelet transform (HWT) is proposed. It can estimate the symbol duration of phase modulated signals, such as M-ary phase-shift keying (MPSK) signals and polyphase coded signals. The closed form of the spectrum of HWT is derived. Theoretical analysis shows the frequency of the first spectral peak is equal to the symbol rate, which is the reciprocal of symbol duration. Thus the symbol duration can be extracted from the spectrum. Subsequently, the optimum wavelet scale is determined according to the maximum output signal to noise ratio (OSNR) criterion. MATLAB simulations show that this algorithm can blindly estimate the symbol duration without any prior knowledge. This estimator need not estimate the carrier frequency and has the characteristics of low computation complexity and high accuracy.

A novel class of periodically changing features hidden in radar pulse sequence environment, named G features, is proposed. Combining fractal theory and Hilbert-Huang transform, the features are extracted using changing characteristics of pulse parameters in radar emitter signals. The features can be applied in modern complex electronic warfare environment to address the issue of signal sorting when radar emitter pulse signal parameters severely or even completely overlap. Experiment results show that the proposed feature class and feature extraction method can discriminate periodically changing pulse sequence signal sorting features from radar pulse signal flow with complex variant features, therefore provide a new methodology for signal sorting.

A method to detect airports in large space-borne synthetic aperture radar (SAR) imagery is studied. First, the large SAR imagery is segmented according to amplitude characteristics using maximum a posteriori (MAP) estimator based on the heavytailed Rayleigh model. The attention is then paid on the object of interest (OOI) extracted from the large images. The minimumarea enclosing rectangle (MER) of OOI is created via a rotating calipers algorithm. The projection histogram (PH) of MER for OOI is then computed and the scale and rotation invariant feature for OOI are extracted from the statistical characteristics of PH. A support vector machine (SVM) classifier is trained using those feature parameters and the airport is detected by the SVM classifier and Hough transform. The application in space-borne SAR images demonstrates the effectiveness of the proposed method.

Using super resolution direction of arrival (DOA) estimation algorithm to reduce the resolution angle is an effective method for passive radar seeker (PRS) to antagonize non-coherent radar decoy. Considering the power and correlation property between radar and non-coherent decoy, an improved subspace DOA estimation method based on traditional subspace algorithm is proposed. Because this new method uses the invariance property of noise subspace, compared with traditional MUSIC algorithm, it shows not only better resolution in condition of closely spaced sources, but also superior performance in case of different power or partially correlated sources. Using this new method, PRS can distinguish radar and non-coherent decoy with good performance. Both the simulation result and the experimental data confirm the performance of the method.

The proposed Doppler measurement technique shows that the Doppler measurements can be accomplished by a single pulse with multiple frequency components through optical fibre delay lines. Range and velocity ambiguity can be removed, and the velocity resolution can be improved dramatically by using long optical fibre delay lines. Furthermore, the velocity resolution can be modified by adjusting the length of optical fibre delay lines. In addition, the proposed radar can achieve high range resolution by using a single wideband pulse. As a result, the new approach can improve radar performance significantly.

Group decision making problems are investigated with uncertain multiplicative linguistic preference relations. An unbalanced multiplicative linguistic label set is introduced, which can be used by the experts to express their linguistic preference information over alternatives. The uncertain linguistic weighted geometric mean operator is utilized to aggregate all the individual uncertain multiplicative linguistic preference relations into a collective one, and then a simple approach is developed to determine the experts' weights by utilizing the consensus degrees among the individual uncertain multiplicative linguistic preference relations and the collective uncertain multiplicative linguistic preference relations. Furthermore, a practical interactive procedure for group decision making is proposed based on uncertain multiplicative linguistic preference relations, in which a possibility degree formula and a complementary matrix are used to rank the given alternatives. Finally, the proposed procedure is applied to solve the group decision
making problem of a manufacturing company searching the best global supplier for one of its most critical parts used in assembling process.

This paper is concerned with a technique for order performance by similarity to ideal solution (TOPSIS) method for fuzzy multi-attribute decision making, in which the information about attribute weights is partly known and the attribute values take form of triangular fuzzy numbers. Considering the fact that the triangular fuzzy TOPSIS results yielded by different distance measures are different from others, a comparative analysis of triangular fuzzy TOPSIS ranking from each distance measure is illustrated with discussion on standard deviation. By applying the most reasonable distance, the deviation degrees between attribute values are measured. A linear programming model based on the maximal deviation of weighted attribute values is established to obtain the attribute weights. Therefore, alternatives are ranked by using TOPSIS method. Finally, a numerical example is given to show the feasibility and effectiveness of the method.

To solve the uncertain multi-attribute group decisionmaking of unknown attribute weights, three optimal models are built to decide the corresponding ideal solution weights, standard deviation weights and mean deviation weights. The comprehensive attribute weights are gotten through the product of the above three kinds of weights. And each decision maker's weighted decision matrices are also received by using the integrated attribute weights. The closeness degrees are also gotten by use of technique for order preference by similarity to ideal solution (TOPSIS) through dealing with the weighted decision matrices. At the same time the group decision matrix and weighted group decision matrix are gotten by using each decision-maker’s closeness degree to every project. Then the vertical TOPSIS method is used to calculate the closeness degree of each project. So these projects can be ranked according to their values of the closeness degree. The process of the method is also given step by step. Finally, a numerical example demonstrates the feasibility and effectiveness of the approach.

This paper introduces a new aggregation model by using induced and heavy aggregation operators in distances measures such as the Hamming distance. It is called the induced heavy ordered weighted averaging (OWA) distance (IHOWAD) operator. This paper studies some of its main properties and a wide range of particular cases such as the induced heavy OWA (IHOWA) operator, the induced OWA distance (IOWAD) operator and the heavy OWA distance (HOWAD) operator. This approach is generalized by using generalized and quasi-arithmetic means obtaining the induced generalized IHOWAD (IGHOWAD) operator and the Quasi-IHOWAD operator. An application of the new approach in a decision making problem regarding the selection of strategies is developed.

In supply chain management, an important research direction is to coordinate the supply chain through introducing flexible contracts. A supply chain contract is flexible if it can satisfy two conditions at the same time: the supply chain is coordinated, and the total profit of the supply chain can be arbitrarily divided between the supply chain members. This paper puts out two contracts, a flexible return contract and a flexible wholesale price discount contract. In contrast to many of literature, the supply chain contracts with an endogenous wholesale price is specifically considered, and a detailed sensitivity analysis of the contract parameters is given. The paper also discusses the application of the contract in vendor-managed inventory (VMI) mode. The results show that the supply chain’s performance is improved after introducing above contracts. All the findings are illustrated by numerical examples.

The coordinated Bayesian optimization algorithm (CBOA) is proposed according to the characteristics of the function independence, conformity and supplementary between the electronic countermeasure (ECM) and the firepower attack systems. The selection criteria are combinations of probabilities of individual fitness and coordinated degree and can select choiceness individual to construct Bayesian network that manifest population evolution by producing the new chromosome. Thus the CBOA cannot only guarantee the effective pattern coordinated decision-making mechanism between the populations, but also maintain the population multiplicity, and enhance the algorithm performance. The simulation result confirms the algorithm validity.

An extended compromise ratio method (CRM) based on fuzzy distances is developed to solve fuzzy multi-attribute group decision making problems in which weights of attributes and ratings of alternatives on attributes are expressed with values of linguistic variables parameterized using triangular fuzzy numbers. A compromise solution is determined by introducing the ranking index based on the concept that the chosen alternative should be as close as possible to the positive ideal solution and as far away from the negative ideal solution as possible simultaneously. This proposed method is compared with other existing methods to show its feasibility and effectiveness and illustrated with an example of the military route selection problem as one of the possible applications.

Ultrafast optoelectronic technology has been widely used in terahertz time domain spectrum, terahertz imaging technology, terahertz communication and so on, and great progress has been achieved in the past two decade. Recently, this innovative technology has been applied in radio metrology and supplied a potential and hopeful method to solve the existent challenges of calibration devices and equipments with bandwidth up to 100 GHz. This paper generally summarizes the emerging applications of the ultrafast optoelectronic technology in radio metrology. The main applications of this technology in calibrating broadband sampling oscilloscopes, the high-speed photodiodes and calibrating the electrical pulse generators are emphasized, and the testing of monolithic microwave integrated circuits is also presented.

This paper is focused on developing a tracking controller for a hypersonic cruise vehicle using tangent linearization approach. The design of flight control systems for air-breathing hypersonic vehicles is a highly challenging task due to the unique characteristics of the vehicle dynamics. Motivated by recent results on tangent linearization control, the tracking control problem for the hypersonic cruise vehicle is reduced to that of a feedback stabilizing controller design for a linear time-varying system which can be accomplished by a standard design method of frozen-time control. Through a proper model transformation, it can be proven that the tracking error of the designed closed-loop system decays exponentially. Simulation studies are conducted for trimmed cruise conditions of 110 000 ft and Mach 15 where the responses of the vehicle to step changes in altitude and velocity are evaluated. The effectiveness of the controller is demonstrated by simulation results.

The robust H∞ control problems for stochastic fuzzy neutral Markov jump systems (MJSs) with parameters uncertainties and multiple time-delays are considered. The delays are respectively considered as constant and time varying, and the uncertain parameters are assumed to be norm bounded. By means of Takagi-Sugeno fuzzy models, the overall closed-loop fuzzy dynamics are constructed through selected membership functions. By selecting the appropriate Lyapunov-Krasovskii functions, the sufficient condition is given such that the uncertain fuzzy neutral MJSs are stochastically stability for all admissible uncertainties and satisfies the given H∞ control index. The stability and H∞ control criteria are formulated in the form of linear matrix inequalities, which can be easily checked in practice. Practical examples illustrate the effectiveness of the developed techniques.

By analyzing the traditional star-tracking correction technology, this paper studies the star-tracking direct correction method which is based on star sensor dynamic separation initial displacement angles and main errors of inertial measure unit (IMU) in order to carry out automatic navigation and improve the hitting accuracy and quick reaction capability of near-earth flight vehicles. Through comprehensive application and improvement of the best correction factor method, this paper proposes the startracking comprehensive correction method which combines the two methods above-mentioned and the accelerometer dynamic error separation technology. The simulation results show that obvious effects can be achieved by using the star-tracking integrated correction method.

A new procedure for a design of multi-range controllers for use with highly nonlinear systems is developed. The procedure involves obtaining the describing function models of the nonlinear plant by software followed by designing a controller at nominal conditions. Then, the controller parameters are optimized to yield a satisfactory closed-loop response at all operating regimes. Finally, the performance and stability of the closed-loop system comprised of the designed controller and the nonlinear plant are verified. The procedure and the associated software are applied to a nonlinear control problem of the sort encountered in aerospace, and the results are compared with two other approaches.

The problem of robust H∞ guaranteed cost satisfactory fault-tolerant control with quadratic D stabilizability against actuator failures is investigated for a class of discrete-time systems with value-bounded uncertainties existing in both the state and control input matrices. Based on a more practical and general model of actuator continuous gain failures, taking the transient property, robust behaviour on H∞ performance and quadratic cost performance requirements into consideration, sufficient conditions for the existence of satisfactory fault-tolerant controller are given and the effective design steps with constraints of multiple performance indices are provided. Meanwhile, the consistency of the regional pole index, H∞ norm-bound constraint and cost performance indices is set up for fault-tolerant control. A simulation example shows the effectiveness of the proposed method.

Heuristic optimization methods provide a robust and efficient approach to solving complex optimization problems. This paper presents a hybrid optimization technique combining two heuristic optimization methods, artificial immune system (AIS) and particle swarm optimization (PSO), together in searching for the global optima of nonlinear functions. The proposed algorithm, namely hybrid anti-prematuration optimization method, contains four significant operators, i.e. swarm operator, cloning operator, suppression operator, and receptor editing operator. The swarm operator is inspired by the particle swarm intelligence, and the clone operator, suppression operator, and receptor editing operator are gleaned by the artificial immune system. The simulation results of three representative nonlinear test functions demonstrate the superiority of the hybrid optimization algorithm over the conventional methods with regard to both the solution quality and convergence rate. It is also employed to cope with a real-world optimization problem.

An improved particle swarm optimization (PSO) algorithm is proposed to train the fuzzy support vector machine (FSVM) for pattern multi-classification. In the improved algorithm, the particles studies not only from itself and the best one but also from the mean value of some other particles. In addition, adaptive mutation was introduced to reduce the rate of premature convergence. The experimental results on the synthetic aperture radar (SAR) target recognition of moving and stationary target acquisition and recognition (MSTAR) dataset and character recognition of MNIST database show that the improved algorithm is feasible and effective for fuzzy multi-class SVM training.

In order to calculate the cross-correlation of two color images treated as vector in a holistic manner, a rapid vertical/parallel decomposition algorithm for quaternion is  resented. The calculation for decomposition is reduced from 21 times to 4 times real number multiplications with the same results. An algorithm for cross-correlation of color images based on decomposition in time domain is put forward, in which some properties pointed out in this paper can be utilized to reduce the computational complexity. Simulation results show the effectiveness and superiority of the proposed method.

Local invariant algorithm applied in downward-looking image registration, usually computes the camera’s pose relative to visual landmarks. Generally, there are three requirements in the process of image registration when using these approaches. First, the algorithm is apt to be influenced by illumination. Second, algorithm should have less computational complexity. Third, the depth information of images needs to be estimated without other sensors. This paper investigates a famous local invariant feature named speeded up robust feature (SURF), and proposes a highspeed and robust image registration and localization algorithm based on it. With supports from feature tracking and pose estimation methods, the proposed algorithm can compute camera poses under different conditions of scale, viewpoint and rotation so as to precisely localize object’s position. At last, the study makes registration experiment by scale invariant feature transform (SIFT), SURF and the proposed algorithm, and designs a method to evaluate their performances. Furthermore, this study makes object retrieval test on remote sensing video. For there is big deformation on remote sensing frames, the registration algorithm absorbs the Kanade-Lucas-Tomasi (KLT) 3-D coplanar calibration feature tracker methods, which can localize interesting targets precisely and efficiently. The experimental results prove that the proposed method has a higher localization speed and lower localization error rate than traditional visual simultaneous localization and mapping (vSLAM) in a period of time.

An ε-subgradient projection algorithm for solving a convex feasibility problem is presented. Based on the iterative projection methods and the notion of ε-subgradient, a series of special projection hyperplanes is established. Moreover, compared with the existing projection hyperplanes methods with subgradient, the proposed hyperplanes are interactive with ε, and their ranges are more larger. The convergence of the proposed algorithm is given under some mild conditions, and the validity of the algorithm is proved by the numerical test.