The electromagnetic surface antenna array (EMSAA) has been proposed for obtaining reflection suppression and excellent radiation simultaneously. The antenna with rectangular radiation patch is used to design anisotropic electromagnetic surface. Preternatural reflection characteristics of the element antenna can be tailored depending on the incident polarizations. EMSAA can be constructed by using single structured element antenna with 90° rotation and orthometric arrangement. This orthometric arrangement of EMSAA is helpful to achieve reflection suppression and excellent radiation. The simulated results show that the reflection of EMSAA is suppressed from 5.0 GHz to 8.0 GHz with peak reduction of 12.3 dB. The linear- and circular-polarized radiation properties of EMSAA are obtained and the maximum gain is 14.3 dBi. The measured results are consistent with the simulation results. The results demonstrate that the reflection suppression and excellent radiation are achieved simultaneously. Such design of EMSAA will open the path for integrating antenna fields and electromagnetic surface (EMS) fields.

In order to resolve direction finding problems in the impulse noise, a direction of arrival (DOA) estimation method is proposed. The proposed DOA estimation method can restrain the impulse noise by using infinite norm exponential kernel covariance matrix and obtain excellent performance via the maximum-likelihood (ML) algorithm. In order to obtain the global optimal solutions of this method, a quantum electromagnetic field optimization (QEFO) algorithm is designed. In view of the QEFO algorithm, the proposed method can resolve the difficulties of DOA estimation in the impulse noise. Comparing with some traditional DOA estimation methods, the proposed DOA estimation method shows high superiority and robustness for determining the DOA of independent and coherent sources, which has been verified via the Monte-Carlo experiments of different schemes, especially in the case of snapshot deficiency, low generalized signal to noise ratio (GSNR) and strong impulse noise. Beyond that, the Cramér-Rao bound (CRB) of angle estimation in the impulse noise and the proof of the convergence of the QEFO algorithm are provided in this paper.

Low elevation estimation, which has attracted wide attention due to the presence of specular multipath, is essential for tracking radars. Frequency agility not only has the advantage of strong anti-interference ability, but also can enhance the performance of tracking radars. A frequency-agile refined maximum likelihood (RML) algorithm based on optimal fusion is proposed. The algorithm constructs an optimization problem, which minimizes the mean square error (MSE) of angle estimation. Thereby, the optimal weight at different frequency points is obtained for fusing the angle estimation. Through theoretical analysis and simulation, the frequency-agile RML algorithm based on optimal fusion can improve the accuracy of angle estimation effectively.

Strong spatial variance of the imaging parameters and serious geometric distortion of the image are induced by the acceleration and vertical velocity in a high-squint synthetic aperture radar (SAR) mounted on maneuvering platforms. In this paper, a frequency-domain imaging algorithm is proposed based on a novel slant range model and azimuth perturbation resampling. First, a novel slant range model is presented for mitigating the geometric distortion according to the equal squint angle curve on the ground surface. Second, the correction of azimuth-dependent range cell migration (RCM) is achieved by introducing a high-order time-domain perturbation function. Third, an azimuth perturbation resampling method is proposed for azimuth compression. The azimuth resampling and the time-domain perturbation are used for correcting first-order and high-order azimuthal spatial-variant components, respectively. Experimental results illustrate that the proposed algorithm can improve the focusing quality and the geometric distortion correction accuracy of the imaging scene effectively.

A miniaturized periodic element for constructing bandpass frequency selective surface (FSS) independent of incident angles and polarizations is presented. An interdigital resonator (IR) with one extending finger to connect the two separate parts of the interdigital capacitor is explored to achieve parallel resonance. The equivalent circuit model (ECM) and electric field distributions are introduced to explain frequency performance of FSS. The whole structure has only one layer and possesses a low profile (a thickness of 0.001 5 $\lambda $ , where $\lambda $ represents the resonant wavelength in free space) as well as a small size (0.03 $\lambda $ ×0.03 $\lambda $ ). This FSS performs as a spatial bandpass filter which exhibits a great angular stability with incident angles ranging from 0° to 80° for both transverse electric (TE) and transverse magnetic (TM) polarizations. As an example, a prototype of one proposed FSS is fabricated and tested. The measured results show a good angular stability.

Modern radar signals mostly use low probability of intercept (LPI) waveforms, which have short pulses in the time domain, multicomponent properties, frequency hopping, combined modulation waveforms and other characteristics, making the detection and estimation of LPI radar signals extremely difficult, and leading to highly required significant research on perception technology in the battlefield environment. This paper proposes a visibility graphs (VG)-based multicomponent signals detection method and a modulation waveforms parameter estimation algorithm based on the time-frequency representation (TFR). On the one hand, the frequency domain VG is used to set the dynamic threshold for detecting the multicomponent LPI radar waveforms. On the other hand, the signal is projected into the time and frequency domains by the TFR method for estimating its symbol width and instantaneous frequency (IF). Simulation performance shows that, compared with the most advanced methods, the algorithm proposed in this paper has a valuable advantage. Meanwhile, the calculation cost of the algorithm is quite low, and it is achievable in the future battlefield.

Adaptive digital self-interference cancellation (ADSIC) is a significant method to suppress self-interference and improve the performance of the linear frequency modulated continuous wave (LFMCW) radar. Due to efficient implementation structure, the conventional method based on least mean square (LMS) is widely used, but its performance is not sufficient for LFMCW radar. To achieve a better self-interference cancellation (SIC) result and more optimal radar performance, we present an ADSIC method based on fractional order LMS (FOLMS), which utilizes the multi-path cancellation structure and adaptively updates the weight coefficients of the cancellation system. First, we derive the iterative expression of the weight coefficients by using the fractional order derivative and short-term memory principle. Then, to solve the problem that it is difficult to select the parameters of the proposed method due to the non-stationary characteristics of radar transmitted signals, we construct the performance evaluation model of LFMCW radar, and analyze the relationship between the mean square deviation and the parameters of FOLMS. Finally, the theoretical analysis and simulation results show that the proposed method has a better SIC performance than the conventional methods.

Equipment selection is an essential work in the research and development planning of equipment. The scientific and rational development of weapons equipment portfolios is of considerable significance to the optimization of equipment architecture design, the adequate resources allocation, and the joint combat performance. From the system view, this paper proposes a method of weapons equipment portfolios selection (WEPS) based on the contribution rate of weapon systems, providing a new idea for weapon equipment portfolio selection. Firstly, we analyze the WEPS problem and the concept of the contribution rate under the systems background. Secondly, we propose a combat network modeling method for weapon equipment systems based on the function chain. Thirdly, we propose a WEPS method based on the contribution rate, fully considering the correlation relationships between potential weapons and the old weapon systems by the combat network model, under the limitation of capability demands and budget resources, with the objective to maximally increasing the combat ability of weapon systems. Finally, we make a case study with a specific WEPS problem where the whole calculation processes and results are analyzed and exhibited to verify the feasibility and effectiveness of the proposed method model.

The advancement of small satellites is promoting the development of distributed satellite systems, and for the latter, it is essential to coordinate the spatial and temporal relations between mutually visible satellites. By now, dual one-way ranging (DOWR) and two-way time transfer (TWTT) are generally integrated in the same software and hardware system to meet the limitations of small satellites in terms of size, weight and power (SWaP) consumption. However, studies show that pseudo-noise regenerative ranging (PNRR) performs better than DOWR if some advanced implementation technologies are employed. Besides, PNRR has no requirement on time synchronization. To apply PNRR to small satellites, and meanwhile, meet the demand for time difference measurement, we propose the round-way time difference measurement, which can be combined with PNRR to form a new integrated system without exceeding the limits of SWaP. The new integrated system can provide distributed small satellite systems with on-orbit high-accuracy and high-precision distance measurement and time difference measurement in real time. Experimental results show that the precision of ranging is about 1.94 cm, and that of time difference measurement is about 78.4 ps, at the signal to noise ratio of 80 dBHz.

Because of the low convergence efficiency of the typical Vicsek model, a Vicsek with static summoning points (VSSP) algorithm based on the Vicsek model considering static summoning points is proposed. Firstly, the mathematical model of the individual movement total cost on each summoning point is established. Then the individual classification rule is designed according to the initial state of the cluster to obtain the subclusters guided by each summoning point. Finally, the summoning factor is introduced to modify the course angle updating formula of the Vicsek model. To verify the effectiveness of the proposed algorithm and study the effect of the cluster summoning factor on the convergence rate, three groups of simulation experiments under different summoning factors are designed in this paper. To verify the superiority of the VSSP algorithm, the performance of the VSSP algorithm is compared with the classic algorithm by designing the algorithm performance comparison verification experiment. The results show that the algorithm proposed in this paper has good convergence and course angle consistency. The summoning factor is the sensitive factor of cluster convergence. This algorithm can provide a reference for efficient cluster segmentation movement.

To overcome the defects that the traditional approach for multi-objective programming under uncertain random environment (URMOP) neglects the randomness and uncertainty of the problem and the volatility of the results, a new approach is proposed based on expected value-standard deviation value criterion (C_ESD criterion). Firstly, the effective solution to the URMOP problem is defined; then, by applying sequence relationship between the uncertain random variables, the URMOP problem is transformed into a single-objective programming (SOP) under uncertain random environment (URSOP), which are transformed into a deterministic counterpart based on the C_ESD criterion. Then the validity of the new approach is proved that the optimal solution to the SOP problem is also efficient for the URMOP problem; finally, a numerical example and a case application are presented to show the effectiveness of the new approach.

The maintenance model of simple repairable system is studied. We assume that there are two types of failure, namely type I failure (repairable failure) and type II failure (irrepairable failure). As long as the type I failure occurs, the system will be repaired immediately, which is failure repair (FR). Between the $(n - 1)$ th and the $n$ th FR, the system is supposed to be preventively repaired (PR) as the consecutive working time of the system reaches ${\lambda ^{n - 1}}T$ , where $\lambda $ and $T$ are specified values. Further, we assume that the system will go on working when the repair is finished and will be replaced at the occurrence of the $N$ th type I failure or the occurrence of the first type II failure, whichever occurs first. In practice, the system will degrade with the increasing number of repairs. That is, the consecutive working time of the system forms a decreasing generalized geometric process (GGP) whereas the successive repair time forms an increasing GGP. A simple bivariate policy $(T, N)$ repairable model is introduced based on GGP. The alternative searching method is used to minimize the cost rate function $C(N, T)$ , and the optimal ${(T, N)^*}$ is obtained. Finally, numerical cases are applied to demonstrate the reasonability of this model.

In the evolutionary game of the same task for groups, the changes in game rules, personal interests, the crowd size, and external supervision cause uncertain effects on individual decision-making and game results. In the Markov decision framework, a single-task multi-decision evolutionary game model based on multi-agent reinforcement learning is proposed to explore the evolutionary rules in the process of a game. The model can improve the result of a evolutionary game and facilitate the completion of the task. First, based on the multi-agent theory, to solve the existing problems in the original model, a negative feedback tax penalty mechanism is proposed to guide the strategy selection of individuals in the group. In addition, in order to evaluate the evolutionary game results of the group in the model, a calculation method of the group intelligence level is defined. Secondly, the Q-learning algorithm is used to improve the guiding effect of the negative feedback tax penalty mechanism. In the model, the selection strategy of the Q-learning algorithm is improved and a bounded rationality evolutionary game strategy is proposed based on the rule of evolutionary games and the consideration of the bounded rationality of individuals. Finally, simulation results show that the proposed model can effectively guide individuals to choose cooperation strategies which are beneficial to task completion and stability under different negative feedback factor values and different group sizes, so as to improve the group intelligence level.

To analyze and optimize the weapon system of systems (WSOS) scheduling process, a new method based on robust capabilities for WSOS scheduling optimization is proposed. First, we present an activity network to represent the military mission. The member systems need to be reasonably assigned to perform different activities in the mission. Then we express the problem as a set partitioning formulation with novel columns (activity flows). A heuristic branch-and-price algorithm is designed based on the model of the WSOS scheduling problem (WSOSSP). The algorithm uses the shortest resource-constrained path planning to generate robust activity flows that meet the capability requirements. Finally, we discuss this method in several test cases. The results show that the solution can reduce the makespan of the mission remarkably.

The trajectory optimization of an unpowered reentry vehicle via artificial emotion memory optimization (AEMO) is discussed. Firstly, reentry dynamics are established based on multiple constraints and parameterized control variables with finite dimensions are designed. If the constraint is not satisfied, a distance measure and an adaptive penalty function are used to address this scenario. Secondly, AEMO is introduced to solve the trajectory optimization problem. Based on the theories of biology and cognition, the trial solutions based on emotional memory are established. Three search strategies are designed for realizing the random search of trial solutions and for avoiding becoming trapped in a local minimum. The states of the trial solutions are determined according to the rules of memory enhancement and forgetting. As the iterations proceed, the trial solutions with poor quality will gradually be forgotten. Therefore, the number of trial solutions is decreased, and the convergence of the algorithm is accelerated. Finally, a numerical simulation is conducted, and the results demonstrate that the path and terminal constraints are satisfied and the method can realize satisfactory performance.

To deal with stabilizing of nonlinear affine fractional order systems subject to time varying delays, two methods for finding an appropriate pseudo state feedback controller are discussed. In the first method, using the Mittag-Lefler function, Laplace transform and Gronwall inequality, a linear stabilizing controller is derived, which uses the fractional order of the delayed system and the upper bound of system nonlinear functions. In the second method, at first a sufficient stability condition for the delayed system is given in the form of a simple linear matrix inequality (LMI) which can easily be solved. Then, on the basis of this result, a stabilizing pseudo-state feedback controller is designed in which the controller gain matrix is easily computed by solving an LMI in terms of delay bounds. Simulation results show the effectiveness of the proposed methods.

A fast self-adapting high-order sliding mode (FSHOSM) controller is designed for a class of nonlinear systems with unknown uncertainties. As for uncertainty-free nonlinear system, a new switching condition is introduced into the standard geometric homogeneity. Different from the existing geometric homogeneity method, both state variables and their derivatives are considered to bring a reasonable effective switching condition. As a result, a faster convergence rate of state variables is achieved. Furthermore, based on the integral sliding mode (ISM) and above geometric homogeneity, a self-adapting high-order sliding mode (HOSM) control law is proposed for a class of nonlinear systems with uncertainties. The resulting controller allows the closed-loop system to conduct with the expected properties of strong robustness and fast convergence. Stable analysis of the nonlinear system is also proved based on the Lyapunov approach. The effectiveness of the resulting controller is verified by several simulation results.

This paper proposes a time-varying sliding mode control method to address nonlinear missile body kinematics based on the suboptimal control theory. The analytical solution of suboptimal time-varying sliding surface and the corresponding suboptimal control law are obtained by solving the state-dependent Riccati equation analytically. Then, the Lyapunov method is used to analyze the motion trend in sliding surface and the asymptotic stability of the closed-loop system is validated. The suboptimal control law is transformed to the form of pseudo-angle-of-attack feedback. The simulation results indicate that the satisfactory performance can be obtained and the control law can overcome the influence of parameter errors.

This paper proposes reliability and maintenance models for systems suffering random shocks arriving according to a non-homogeneous Poisson process. The system degradation process include two stages: from the installation of a new system to an initial point of a defect (normal stage), and then from that point to failure (defective stage), following the delay time concept. By employing the virtual age method, the impact of external shocks on the system degradation process is characterized by random virtual age increment in the two stages, resulting in the corresponding two-stage virtual age process. When operating in the defective state, the system becomes more susceptible to fatigue and suffers from a greater aging rate. Replacement is carried out either on failure or on the detection of a defective state at periodic or opportunistic inspections. This paper evaluates system reliability performance and investigates the optimal opportunistic maintenance policy. A case study on a cooling system is given to verify the obtained results.

The availability of a periodic inspection system under mixed maintenance policies is studied in this paper. To accommodate the characteristic of multiple failure modes for complex systems, the system failures can be divided into two failure modes: hard failure and soft failure. When hard failure occurs, the corresponding corrective maintenance will be performed, taking a random time under the perfect maintenance policy; in contrast, if the soft failure is found, the corresponding preventive maintenance will be performed, taking a random time under the imperfect maintenance policy. The dynamic age setback model is adopted for imperfect maintenance, which can accurately reflect the fault characteristics of the degraded system. Then an analytical model for system steady state availability and instantaneous availability are derived. Moreover, the optimal method to maximize the system steady-state availability through adjusting the inspection interval is researched. According to the above research, the optimization of system unit time cost, preventive maintenance intervals and availability is researched. Finally, the developed approach is demonstrated by a numerical example.