While moving ahead with the object detection technology, especially deep neural networks, many related tasks, such as medical application and industrial automation, have achieved great success. However, the detection of objects with multiple aspect ratios and scales is still a key problem. This paper proposes a top-down and bottom-up feature pyramid network (TDBU-FPN), which combines multi-scale feature representation and anchor generation at multiple aspect ratios. First, in order to build the multi-scale feature map, this paper puts a number of fully convolutional layers after the backbone. Second, to link neighboring feature maps, top-down and bottom-up flows are adopted to introduce context information via top-down flow and supplement suboriginal information via bottom-up flow. The top-down flow refers to the deconvolution procedure, and the bottom-up flow refers to the pooling procedure. Third, the problem of adapting different object aspect ratios is tackled via many anchor shapes with different aspect ratios on each multi-scale feature map. The proposed method is evaluated on the pattern analysis, statistical modeling and computational learning visual object classes (PASCAL VOC) dataset and reaches an accuracy of 79%, which exhibits a 1.8% improvement with a detection speed of 23 fps.

Device to device (D2D) multi-hop communication in multicast networks solves the contradiction between high speed requirements and limited bandwidth in regional data sharing communication services. However, most networking models demand a large control overhead in eNodeB. Moreover, the topology should be calculated again due to the mobility of terminals, which causes the long delay. In this work, we model multicast network construction in D2D communication through a fuzzy mathematics and game theory based algorithm. In resource allocation, we assume that user equipment (UE) can detect the available frequency and the fuzzy mathematics is introduced to describe an uncertain relationship between the resource and UE distributedly, which diminishes the time delay. For forming structure, a distributed myopic best response dynamics formation algorithm derived from a novel concept from the coalitional game theory is proposed, in which every UE can self-organize into stable structure without the control from eNodeB to improve its utilities in terms of rate and bit error rate (BER) while accounting for a link maintenance cost, and adapt this topology to environmental changes such as mobility while converging to a Nash equilibrium fast. Simulation results show that the proposed architecture converges to a tree network quickly and presents significant gains in terms of average rate utility reaching up to 50% compared to the star topology where all of the UE is directly connected to eNodeB.

A gradient-based optimization method for producing a contoured beam by using a single-fed reflector antenna is presented. First, a quick and accurate pattern approximation formula based on physical optics (PO) is adopted to calculate the gradients of the directivity with respect to reflectoros nodal displacements. Because the approximation formula is a linear function of nodal displacements, the gradient can be easily derived. Then, the method of the steepest descent is adopted, and an optimization iteration procedure is proposed. The iteration procedure includes two loops: an inner loop and an outer loop. In the inner loop, the gradient and pattern are calculated by matrix operation, which is very fast by using the pre-calculated data in the outer loop. In the outer loop, the ideal terms used in the inner loop to calculate the gradient and pattern are updated, and the real pattern is calculated by the PO method. Due to the high approximation accuracy, when the outer loop is performed once, the inner loop can be performed many times, which will save much time because the integration is replaced by matrix operation. In the end, a contoured beam covering the continental United States (CONUS) is designed, and simulation results show the effectiveness of the proposed algorithm.

The delay tolerant network (DTN) is an emerging concept, which is used to describe the network, where the communication link may disrupt frequently. To cope with this problem, the DTN uses the store-carry-forward (SCF) transmission mode. With this policy, the messages in the DTN are transmitted based on the nodeso cooperation. However, the nodes may be selfish, so the source has to pay certain rewards for others to get their help. This paper studies the optimal incentive policy to maximize the sourceos final utility. First, it models the message spreading process as several ordinary differential equations (ODEs) based on the meanfield approximation. Then, it mathematically gets the optimal policy and explores the structure of the policy. Finally, it checks the accuracy of the ODEs model and shows the advantages of the optimal policy through simulation and theoretical results respectively.

This paper presents the design of an experimental battlefield dynamic scanning and staring imaging system based on a fast steering mirror (FSM), which is capable of real-time monitoring of hot targets and wide-area reconnaissance of hot regions. First, the working principle and working sequence of the FSM are briefly analyzed. The mathematical model of the FSM system is built by modeling its dynamic and electrical properties, and the rationality of the model is validated by means of model identification. Second, the influence of external sources of disturbance such as the carrier and moment on the control precision of the FSM is effectively suppressed by the jointly controlling of proportional integral (PI) and disturbance observer (DOB), thus realizing a high precision and strong robustness control of the FSM system. Then, this paper designs an experimental prototype and introduces a special optical structure to enable the infrared camera to share the FSM with the visible light camera. Finally, the influence of the velocity difference between the mirror of the FSM and the rotating platform on the imaging quality of the system is experimentally analyzed by using the image sharpness evaluation method based on point sharpness. A good dynamic scanning and staring imaging result is achieved when the velocity of these two components correspond.

In multiple extended targets tracking, replacing traditional multiple measurements with a rectangular region of the nonzero volume in the state space inspired by the box-particle idea is exactly suitable to deal with extended targets, without distinguishing the measurements originating from the true targets or clutter. Based on our recent work on extended box-particle probability hypothesis density (ET-BP-PHD) filter, we propose the extended labeled box-particle cardinalized probability hypothesis density (ET-LBP-CPHD) filter, which relaxes the Poisson assumptions of the extended target probability hypothesis density (PHD) filter in target numbers, and propagates not only the intensity function but also cardinality distribution. Moreover, it provides the identity of individual target by adding labels to box-particles. The proposed filter can improve the precision of estimating target number meanwhile achieve targetso tracks. The effectiveness and reliability of the proposed algorithm are verified by the simulation results.

The theoretical statistical model of sea clutter is a key issue for maritime surveillance. In recent years, the statistical model of sea clutter has attracted much attention to design the detector well. Meanwhile, the circular scanning synthetic aperture radar (SAR) has been applied to wide surveillance of the sea surface. Therefore, the paper analyzes the validity of available sea clutter models for range Doppler images from different scan angles for moderate sea state in the medium grazing angle. The parameter estimation method for different distribution models employs the method of logarithmic cumulant (MoLC) based on Mellin transform uniformly. By the analysis of the fitting performance between the histogram of real data and the amplitude probability density function (PDF) of empirical distribution models and the goodness-of-fit (GoF) test for real data from different scan angles, it is indicated the generalized K (GK) distribution with generalized Gamma texture distribution can fit the sea clutter well for different scan angles in the medium grazing angle.

This paper proposes a new multi-baseline extended particle filtering phase unwrapping algorithm which combines an extended particle filter with an amended matrix pencil model and a quantized path-following strategy. The contributions to multibaseline synthetic aperture radar (SAR) interferometry are as follows: a new recursive multi-baseline phase unwrapping model based on an extended particle filter is built, and the amended matrix pencil model is used to acquire phase gradient information with a higher precision and lower computational cost, and the quantized path-following strategy is introduced to guide the proposed phase unwrapping procedure to efficiently unwrap wrapped phase image along the paths routed by a phase derivative variance map.

Weapon project planning (WPP) plays a critical role in the process of national defense development and establishment of the future national defense force. WPP faces the backgrounds of various uncertainties, intense inter-influence of weapon systems and involves modelling, assessment, and optimization procedures. The contents of this paper are mainly divided into three parts: first, the WPP processes are analyzed, and related elements are formulated to transform the qualitative problem to mathematics form; second, the value evaluation model of WPP solutions is proposed based on two criteria of total capability gap and total capability dispersion; third, two robustness optimization models are constructed based on the absolute robustness criterion and the robustness deviation criterion to support the robustness optimization process under multi-scenario. Finally, a case is studied to examine the feasibility and effectiveness of the proposed models and approaches.

Model combat information conditions are always uncertain and varied, the uncertain interval theory is therefore introduced into the research of multiple fighters suppressing the integrated air defense system (IADS) problem. Considering that the combat information conditions are uncertain intervals, the payoff function of the game for multiple fighters suppressing the IADS is modeled. Using the operation rules for interval numbers and the possibility degree, an improved chaotic particle swarm optimization (CPSO) is designed to solve the proposed model so as to obtain the optimal game solution. Comparison simulations are performed to analyze the influence of the weapons consumption and the distances of non-escaped zone and jamming on air combat result. Simulation results suggest that Nash equilibrium is achieved and verify the effectiveness of the proposed method.

To solve the problem of distributed tasks-platforms scheduling in holonic command and control (C2) organization, the basic elements of the organization are analyzed firstly and the formal description of organizational elements and structure is provided. Based on the improvement of task execution quality, a single task resource scheduling model is established and the solving method based on the m-best algorithm is proposed. For the problem of tactical decision-holon cannot handle tasks with low priority effectively, a distributed resource scheduling collaboration mechanism based on platform pricing and a platform exchange mechanism based on resource capacities are designed. Finally, a series of experiments are designed to prove the effectiveness of these methods. The results show that the proposed distributed scheduling methods can realize the effective balance of platform resources.

The clustering evaluation can be used to scientifically classify the objects to be evaluated according to the information aggregation of various evaluation rules. In grey weighted clustering evaluation, the index clustering rule relies on the construction of the whitenization weight function, while the existing construction method of the linear function lacks the construction mechanism analysis and validity explanation. A normative construction principle is put forward by analyzing the construction mechanism of the function. Through proving the normative principle of the function, the basic modal function (BMF) is proposed and characterized by different function forms. Then, a new type of the whitenization weight function and its grey clustering evaluation model algorithm are given by studying the mechanism and nature of the construction of different forms of the function. Finally, the comparative study for self-innovation capability of defense science and technology industry (DSTI) is taken as an example. The results show that the different construction ways of the function have an effect on the clustering result. The proposed construction mechanism can better explain the index clustering rules and evaluation effectiveness, which will perfect the theoretical system of grey clustering evaluation and be applied to practice effectively.

Since most parameter control methods are based on prior knowledge, it is difficult for them to solve various problems. In this paper, an adaptive selection method used for operators and parameters is proposed and named double adaptive selection (DAS) strategy. Firstly, some experiments about the operator search ability are given and the performance of operators with different donate vectors is analyzed. Then, DAS is presented by inducing the upper confidence bound strategy, which chooses suitable combination of operators and donates sets to optimize solutions without prior knowledge. Finally, the DAS is used under the framework of the multi-objective evolutionary algorithm based on decomposition, and the multi-objective evolutionary algorithm based on DAS (MOEA/D-DAS) is compared to state-of-the-art MOEAs. Simulation results validate that the MOEA/D-DAS could select the suitable combination of operators and donate sets to optimize problems and the proposed algorithm has better convergence and distribution.

Monitoring high-dimensional multistage processes becomes crucial to ensure the quality of the final product in modern industry environments. Few statistical process monitoring (SPC) approaches for monitoring and controlling quality in highdimensional multistage processes are studied. We propose a deviance residual-based multivariate exponentially weighted moving average (MEWMA) control chart with a variable selection procedure. We demonstrate that it outperforms the existing multivariate SPC charts in terms of out-of-control average run length (ARL) for the detection of process mean shift.

A useful life prediction method based on the integration of the stochastic hybrid automata (SHA) model and the frame of the dynamic fault tree (DFT) is proposed. The SHA model can incorporate the orbit environment, work modes, system configuration, dynamic probabilities and degeneration of components, as well as spacecraft dynamics and kinematics. By introducing the frame of DFT, the system is classified into several layers, and the problem of state combination explosion is artfully overcome. An improved dynamic reliability model (DRM) based on the Nelson hypothesis is investigated to improve the defect of cumulative failure probability (CFP), which is used to address the failure probability of components in the SHA model. The simulation using the Monte-Carlo method is finally conducted on two satellites, which are deployed with the same multi-gyro subsystem but run on different orbits. The results show that the predicted useful life of the attitude control system (ACS) with consideration of abrupt failure, degradation, and running environment is quite different between the two satellites.

A switching disturbance rejection attitude control law is proposed for a near space vehicle (NSV) with variable structure. The multiple flight modes, system uncertainties and disturbances of the NSV are taken into account based on switched nonlinear systems. Compared with traditional backstepping design methods, the proposed method utilizes the added integrals of attitude angle and angular rate tracking errors to further decrease the tracking errors. Moreover, to reduce the computation complexity, a rapid convergent differentiator is employed to obtain the derivative of the virtual control command. Finally, for disturbance rejection, based on the idea from the extended state observer (ESO), two disturbance observers are designed by using non-smooth functions to estimate the disturbances in the switched nonlinear systems. All signals of the closed-loop system are proven to be uniformly ultimately bounded under the Lyapunov function framework. Simulation results demonstrate the effectiveness of the proposed control scheme.

In conventional technical trajectory correction schemes, continuous attitude adjusting mechanisms, such as canards, are inferior in terms of response time and efficiency of executing instructions. Discontinuous attitude adjusting mechanisms, such as the lateral pulse jet, have complex impact on the airflow layer of the projectile surface caused by the thrust vector jet flow. An improved two-dimensional trajectory correction mechanism is designed based on the principle of firing mass blocks by a tailor-made propellant. The mechanical properties of the thrust force (namely the correction force) is analyzed. The trajectory correction model is established to analyze the effects of correction starting moment and correction phase angle of a thrust force on the projectile's trajectory. According to the trajectory correction scheme, an improved genetic algorithm is employed to this work. The scheme is tested in the simulation. The results show that the correction scheme is effective to reduce target dispersion and increase the precision of the impact point.

For the problem of attitude control of a quad tilt rotor aircraft with unknown external disturbances, a class of control methods based on a new exponential fast nonsingular terminal sliding surface, a new fast reaching law, and a super twisting sliding mode disturbance observer is investigated. First, the new exponential nonsingular terminal sliding surface is designed by using the advantages of nonsingular terminal sliding mode finite time convergence and strong robustness. Second, to solve the problem of a long convergence time and the serious shaking of the traditional reaching law, a new fast reaching law model with characteristics of the second-order sliding mode is put forward. Third, considering the existence of complex disturbances, the super twisting sliding mode disturbance observer is used to estimate and compensate the composite disturbances online. Finally, compared with the traditional nonsingular fast sliding mode control, simulation results show that the proposed control scheme achieves a good control performance.

Inference are considered for the dependence competing risks model by using the Marshal-Olkin bivariate exponential distribution. Under generalized progressively hybrid censoring with partially observed failure causes, the maximum likelihood estimators are established, and the approximate confidence intervals are also constructed via the observed Fisher information matrix. Moreover, Bayes estimates and highest probability density credible intervals are presented and the importance sampling technique is used to compute corresponding results. Finally, the numerical analysis is proposed for illustration.

Middle censoring is an important censoring scheme, in which the actual failure data of an observation becomes unobservable if it falls into a random interval. This paper considers the statistical analysis of the dependent competing risks model by using the Marshall-Olkin bivariate Weibull (MOBW) distribution. The maximum likelihood estimations (MLEs), midpoint approximation (MPA) estimations and approximate confidence intervals (ACIs) of the unknown parameters are obtained. In addition, the Bayes approach is also considered based on the Gamma-Dirichlet prior of the scale parameters, with the given shape parameter. The acceptance-rejection sampling method is used to obtain the Bayes estimations and construct credible intervals (CIs). Finally, two numerical examples are used to show the performance of the proposed methods.