Fabry-Perot (FP) antennas have characteristics of planar structures combined with high gain, and they have been widely used in wireless communications. With the progress of ongoing research, FP antennas have achieved various capabilities, but many of them are still under development, such as low-profile, wideband, circular polarization, multi-band, low-radar cross section (RCS) and reconfigurable features. This paper discusses the theoretical analysis methods and research progress of FP antennas, and explains the realization methods of different features of FP antennas. In order to indicate different technologies for realizing various capabilities, the key technologies and features of some of the latest designs are described. Finally, the research situation and prospects of FP antennas are summarized to guide their research directions in the future.

Nonuniform linear arrays, such as coprime array and nested array, have received great attentions because of the increased degrees of freedom (DOFs) and weakened mutual coupling. In this paper, inspired by the existing coprime array, we propose a high-order extended coprime array (HoECA) for improved direction of arrival (DOA) estimation. We first derive the closed-form expressions for the range of consecutive lags. Then, by changing the inter-element spacing of a uniform linear array (ULA), three cases are proposed and discussed. It is indicated that the HoECA can obtain the largest number of consecutive lags when the spacing takes the maximum value. Finally, by comparing it with the other sparse arrays, the optimized HoECA enjoys a larger number of consecutive lags with mitigating mutual coupling. Simulation results are shown to evaluate the superiority of HoECA over the others in terms of DOF, mutual coupling leakage and estimation accuracy.

Most of the near-field source localization methods are developed with the approximated signal model, because the phases of the received near-field signal are highly non-linear. Nevertheless, the approximated signal model based methods suffer from model mismatch and performance degradation while the exact signal model based estimation methods usually involve parameter searching or multiple decomposition procedures. In this paper, a search-free near-field source localization method is proposed with the exact signal model. Firstly, the approximative estimates of the direction of arrival (DOA) and range are obtained by using the approximated signal model based method through parameter separation and polynomial rooting operations. Then, the approximative estimates are corrected with the exact signal model according to the exact expressions of phase difference in near-field observations. The proposed method avoids spectral searching and parameter pairing and has enhanced estimation performance. Numerical simulations are provided to demonstrate the effectiveness of the proposed method.

This paper examines the direction of arrival (DOA) estimation for polarized signals impinging on a sparse vector sensor array which is based on the maximum interelement spacing constraint (MISC). The vector array effectively utilizes the polarization domain information of incident signals, and the quaternion model is adopted for signals polarization characteristic maintenance and computational burden reduction. The features of MISC arrays are crucial to the mutual coupling effects reduction and higher degrees of freedom (DOFs). The quaternion data model based on vector MISC arrays is established, which extends the scalar MISC array into the vector MISC array. Based on the model, a quaternion multiple signal classification (MUSIC) algorithm based on vector MISC arrays is proposed for DOA estimation. The algorithm combines the advantages of the quaternion model and the vector MISC array to enhance the DOA estimation performance. Analytical simulations are performed to certify the capability of the algorithm.

Orbital angular momentum (OAM) at radio frequency (RF) has attracted more and more attention as a novel approach of multiplexing a set of orthogonal OAM modes on the same frequency channel to achieve high spectral efficiency (SE). However, the precondition for maintaining the orthogonality among different OAM modes is perfect alignment of the transmit and receive uniform circular arrays (UCAs), which is difficult to be satisfied in practical wireless communication scenarios. Therefore, to achieve available multi-mode OAM broadband wireless communication, we first investigate the effect of oblique angles on the transmission performance of the multi-mode OAM broadband system in the non-parallel misalignment case. Then, we compare the UCA-based RF analog and baseband digital transceiver structures and corresponding beam steering schemes. Mathematical analysis and numerical simulations validate that the SE of the misaligned multi-mode OAM broadband system is quite low, while analog and digital beam steering (DBS) both can significantly improve the SE of the system. However, DBS can obtain higher SE than analog beam steering especially when the bandwidth and the number of array elements are large, which validates that the baseband digital transceiver with DBS is more suitable for multi-mode OAM broadband wireless communication systems in practice.

A multi-objective optimization based robust beamforming (BF) scheme is proposed to realize secure transmission in a cognitive satellite and unmanned aerial vehicle (UAV) network. Since the satellite network coexists with the UAV network, we first consider both achievable secrecy rate maximization and total transmit power minimization, and formulate a multi-objective optimization problem (MOOP) using the weighted Tchebycheff approach. Then, by supposing that only imperfect channel state information based on the angular information is available, we propose a method combining angular discretization with Taylor approximation to transform the non-convex objective function and constraints to the convex ones. Next, we adopt semi-definite programming together with randomization technology to solve the original MOOP and obtain the BF weight vector. Finally, simulation results illustrate that the Pareto optimal trade-off can be achieved, and the superiority of our proposed scheme is confirmed by comparing with the existing BF schemes.

An automatic method for classifying frequency shift keying (FSK), minimum shift keying (MSK), phase shift keying (PSK), quadrature amplitude modulation (QAM), and orthogonal frequency division multiplexing (OFDM) is proposed by simultaneously using normality test, spectral analysis, and geometrical characteristics of in-phase-quadrature (I-Q) constellation diagram. Since the extracted features are unique for each modulation, they can be considered as a fingerprint of each modulation. We show that the proposed algorithm outperforms the previously published methods in terms of signal-to-noise ratio (SNR) and success rate. For example, the success rate of the proposed method for 64-QAM modulation at SNR=11 dB is 99%. Another advantage of the proposed method is its wide SNR range; such that the probability of classification for 16-QAM at SNR=3 dB is almost 1. The proposed method also provides a database for geometrical features of I-Q constellation diagram. By comparing and correlating the data of the provided database with the estimated I-Q diagram of the received signal, the processing gain of 4 dB is obtained. Whatever can be mentioned about the preference of the proposed algorithm are low complexity, low SNR, wide range of modulation set, and enhanced recognition at higher-order modulations.

Radar cross section (RCS) is an important attribute of radar targets and has been widely used in automatic target recognition (ATR). In a passive radar, only the RCS multiplied by a coefficient is available due to the unknown transmitting parameters. For different transmitter-receiver (bistatic) pairs, the coefficients are different. Thus, the recovered RCS in different transmitter-receiver (bistatic) pairs cannot be fused for further use. In this paper, we propose a quantity named quasi-echo-power (QEP) as well as a method for eliminating differences of this quantity among different transmitter-receiver (bistatic) pairs. The QEP is defined as the target echo power after being compensated for distance and pattern propagation factor. The proposed method estimates the station difference coefficients (SDCs) of transmitter-receiver (bistatic) pairs relative to the reference transmitter-receiver (bistatic) pair first. Then, it compensates the QEP and gets the compensated QEP. The compensated QEP possesses a linear relationship with the target RCS. Statistical analyses on the simulated and real-life QEP data show that the proposed method can effectively estimate the SDC between different stations, and the compensated QEP from different receiving stations has the same distribution characteristics for the same target.

The dual-axis rotational inertial navigation system (INS) with dithered ring laser gyro (DRLG) is widely used in high precision navigation. The major inertial sensor errors such as drift errors of gyro and accelerometer can be averaged out, but the G-sensitive drifts of laser gyro cannot be averaged out by indexing. A 16-position rotational simulation experiment proves the G-sensitive drift will affect the long-term navigation error for the rotational INS quantitatively. The vibration coupling and asymmetric structure of the DRLG are the main errors. A new dithered mechanism and optimized DRLG is designed. The validity and efficiency of the optimized design are conformed by 1 g sinusoidal vibration experiments. An optimized inertial measurement unit (IMU) is formulated and measured experimentally. Laboratory and vehicle experimental results show that the divergence speed of longitude errors can be effectively slowed down in the optimized IMU. In long term independent navigation, the position accuracy of dual-axis rotational INS is improved close to 50%, and the G-sensitive drifts of laser gyro in the optimized IMU are less than 0.000 2 °/h. These results have important theoretical significance and practical value for improving the structural dynamic characteristics of DRLG INS, especially the high-precision inertial system.

To acquire global navigation satellite system (GNSS) signals means four-dimension acquisition of bit transition, Doppler frequency, Doppler rate, and code phase in high-dynamic and weak signal environments, which needs a high computational cost. To reduce the computations, this paper proposes a two-step compressed acquisition method (TCAM) for the post-correlation signal parameters estimation. Compared with the fast Fourier transform (FFT) based methods, TCAM uses fewer frequency search points. In this way, the proposed method reduces complex multiplications, and uses real multiplications instead of improving the accuracy of the Doppler frequency and the Doppler rate. Furthermore, the differential process between two adjacent milliseconds is used for avoiding the impact of bit transition and the Doppler frequency on the integration peak. The results demonstrate that due to the reduction of complex multiplications, the computational cost of TCAM is lower than that of the FFT based method under the same signal to noise ratio (SNR).

Unauthorized operations referred to as “black flights” of unmanned aerial vehicles (UAVs) pose a significant danger to public safety, and existing low-attitude object detection algorithms encounter difficulties in balancing detection precision and speed. Additionally, their accuracy is insufficient, particularly for small objects in complex environments. To solve these problems, we propose a lightweight feature-enhanced convolutional neural network able to perform detection with high precision detection for low-attitude flying objects in real time to provide guidance information to suppress black-flying UAVs. The proposed network consists of three modules. A lightweight and stable feature extraction module is used to reduce the computational load and stably extract more low-level feature, an enhanced feature processing module significantly improves the feature extraction ability of the model, and an accurate detection module integrates low-level and advanced features to improve the multiscale detection accuracy in complex environments, particularly for small objects. The proposed method achieves a detection speed of 147 frames per second (FPS) and a mean average precision (mAP) of 90.97% for a dataset composed of flying objects, indicating its potential for low-altitude object detection. Furthermore, evaluation results based on microsoft common objects in context (MS COCO) indicate that the proposed method is also applicable to object detection in general.

How to improve the efficiency of exact learning of the Bayesian network structure is a challenging issue. In this paper, four different causal constraints algorithms are added into score calculations to prune possible parent sets, improving state-of-the-art learning algorithms’ efficiency. Experimental results indicate that exact learning algorithms can significantly improve the efficiency with only a slight loss of accuracy. Under causal constraints, these exact learning algorithms can prune about 70% possible parent sets and reduce about 60% running time while only losing no more than 2% accuracy on average. Additionally, with sufficient samples, exact learning algorithms with causal constraints can also obtain the optimal network. In general, adding max-min parents and children constraints has better results in terms of efficiency and accuracy among these four causal constraints algorithms.

A range-spread target (RST) detector is proposed for wideband radar. The detector, referred to as a conjugate multiplication and block thresholding (CMBT) detector, is simple for implementation in existing radar systems and has the advantage of minor calculation. First, the target energy of adjacent stretched echoes is coherently accumulated via conjugate multiplication and Fourier transform operations. It is noted that conjugate multiplication of two complex Gaussian distributed noise is complex double Gaussian distributed, leading to a signal to noise ratio (SNR) loss. Subsequently, considering the sparsity and clustering characteristics of the conjugate multiplication amplitude spectrum (CMAS), the block thresholding method is adopted for denoising, where the noise and cross-terms are adaptively smoothed, and the signal terms can be basically preserved. Finally, numerical simulation results for both synthetic and real radar data validate the effectiveness of the proposed detector, comparing with the conventional integration detector (ID), the spatial scattering density (SSD) detector, and waveform entropy (WE) and waveform contrast (WC) based detectors.

The complexity of business and information systems (IS) alignment is a growing concern for researchers and practitioners alike. The extant research on alignment architecture fails to consider the human viewpoint, which makes it difficult to embrace emergent complexity. This paper contributes to the extant literature in the following ways. First, we combine an enterprise architecture (EA) framework with a human viewpoint to address alignment issues in the architecture design phase; second, we describe a dynamic alignment model by developing a human-centered meta-model that explains first- and second-order changes and their effects on alignment evolution. This paper provides better support for the theoretical research and the practical application of dynamic alignment.

As the scale of current systems become larger and larger and their complexity is increasing gradually, research on executable models in the design phase becomes significantly important as it is helpful to simulate the execution process and capture defects of a system in advance. Meanwhile, the capability of a system becomes so important that stakeholders tend to emphasize their capability requirements when developing a system. To deal with the lack of official specifications and the fundamental theory basis for capability requirement, we propose a cooperative capability requirements (CCR) meta-model as a theory basis for researchers to refer to in this research domain, in which we provide detailed definition of the CCR concepts, associations and rules. Moreover, we also propose an executable framework, which may enable modelers to simulate the execution process of a system in advance and do well in filling the inconsistency and semantic gaps between stakeholders’ requirements and their models. The primary working mechanism of the framework is to transform the Alf activity meta-model into the communicating sequential process (CSP) process meta-model based on some mapping rules, after which the internal communication mechanism between process nodes is designed to smooth the execution of behaviors in a CSP system. Moreover, a validation method is utilized to check the correctness and consistency of the models, and a self-fixing mechanism is used to fix the errors and warnings captured during the validation process automatically. Finally, a validation report is generated and fed back to the modelers for system optimization.

The meteorological satellite service range is extensive, and science and technology and related industries have become beneficiaries of it. The complex meteorological satellite stakeholder relationship warrants quantitative evaluation. This study investigates the meteorological satellite stakeholder relationship network to provide a new research perspective for me-teorological satellites in the field of management. For literature analysis, 16 meteorological satellite stakeholders are identified through keyword screening, classified, and coded. A meteorological satellite stakeholder relationship network model is then constructed through social network analysis (SNA). Ego, local, and overall networks are analyzed from three perspectives to measure the network principle and to form a relationship network coordination degree evaluation system. The improved analytic hierarchy process (AHP)-fuzzy comprehensive evaluation method is then used to determine index weights and evaluate the relationship network coordination process design comprehensively. In empirical analysis, data for the meteorological satellite Fengyun-4 are obtained through questionnaire survey and literature analysis. Ucinet6 is used to generate relationship networks and analyze various stakeholder roles and status, stakeholder relationship network coordination degree, and eva-luation results. The results demonstrate that the competent me-teorological satellite department, the meteorological administration, the National Meteorological Centre, and the government are in the center of the Fengyun-4 stakeholder relationship network, with coordination degree in an “average” state. Thus, establishing a stakeholder coordination mechanism may strengthen connection and promote the development of meteorological undertakings.

This paper investigates the guidance method based on reinforcement learning (RL) for the coplanar orbital interception in a continuous low-thrust scenario. The problem is formulated into a Markov decision process (MDP) model, then a well-designed RL algorithm, experience based deep deterministic policy gradient (EBDDPG), is proposed to solve it. By taking the advantage of prior information generated through the optimal control model, the proposed algorithm not only resolves the convergence problem of the common RL algorithm, but also successfully trains an efficient deep neural network (DNN) controller for the chaser spacecraft to generate the control sequence. Numerical simulation results show that the proposed algorithm is feasible and the trained DNN controller significantly improves the efficiency over traditional optimization methods by roughly two orders of magnitude.

The existing theories for closed loop identification with the linear feedback controller are very mature. To apply the existed theories directly in the control field, we propose a new idea about replacing the original unknown and nonlinear feedback controller with one approximated linear controller, while guaranteeing the equivalent property for the obtained closed loop system. Based on some statistical correlation functions, one condition is derived to show the equivalent property between the approximated linear controller and the original nonlinear controller. The detailed explicit form, corresponding to the approximated linear controller, is also constructed. Furthermore, to give a complete analysis for closed loop identification, the cost function is rewritten as one extended expression, being convenient to understand. Then spectral estimation is introduced to identify the unknown plant in the closed loop system. Finally, the proposed theories are verified by one simulation example.

In recent years, with the continuous development of multi-agent technology represented by unmanned aerial vehicle (UAV) swarm, consensus control has become a hot spot in academic research. In this paper, we put forward a discrete-time consensus protocol and obtain the necessary and sufficient conditions for the second-order consensus of the second-order multi-agent system with a fixed structure under the condition of no saturation input. The theoretical derivation verifies that the two eigenvalues of the Laplacian of the communication network matrix and the sampling period have an important effect on achieving consensus. Then we construct and verify sufficient conditions to achieve consensus under the condition of input saturation constraints. The results show that consensus can be achieved if velocity, position gain, and sampling period satisfy a set of inequalities related to the eigenvalues of the Laplacian matrix. Finally, the accuracy and validity of the theoretical results are proved by numerical simulations.

In this paper, a linear/nonlinear switching active disturbance rejection control (SADRC) based decoupling control approach is proposed to deal with some difficult control problems in a class of multi-input multi-output (MIMO) systems such as multi-variables, disturbances, and coupling, etc. Firstly, the structure and parameter tuning method of SADRC is introduced into this paper. Followed on this, virtual control variables are adopted into the MIMO systems, making the systems decoupled. Then the SADRC controller is designed for every subsystem. After this, a stability analyzed method via the Lyapunov function is proposed for the whole system. Finally, some simulations are presented to demonstrate the anti-disturbance and robustness of SADRC, and results show SADRC has a potential applications in engineering practice.

Fiber optical gyroscope (FOG) is a highly reliable navigation element, and the degradation trajectories of its two accuracy indexes are monotonic and non-monotonic respectively. In this paper, a flexible accelerated degradation testing (ADT) model is used for analyzing the bivariate dependent degradation process of FOG. The time-varying copulas are employed to consider the dynamic dependency structure between two marginal degradation processes as the Wiener process and the inverse Gaussian process. The statistical inference is implemented by utilizing an inference function for the margins (IFM) approach. It is demonstrated that the proposed method is powerful in modeling the joint distribution with various margins.

Test selection is to select the test set with the least total cost or the least total number from the alternative test set on the premise of meeting the required testability indicators. The existing models and methods are not suitable for system level test selection. The first problem is the lack of detailed data of the units’ fault set and the test set, which makes it impossible to establish a traditional dependency matrix for the system level. The second problem is that the system level fault detection rate and the fault isolation rate (referred to as "two rates") are not enough to describe the fault diagnostic ability of the system level tests. An innovative dependency matrix (called combinatorial dependency matrix) composed of three submatrices is presented. The first problem is solved by simplifying the submatrix between the units’ fault and the test, and the second problem is solved by establishing the system level fault detection rate, the fault isolation rate and the integrated fault detection rate (referred to as "three rates") based on the new matrix. The mathematical model of the system level test selection problem is constructed, and the binary genetic algorithm is applied to solve the problem, which achieves the goal of system level test selection.