Satellites with altitudes below 400 km are called super low altitude satellites (SLAS), often used to achieve responsive imaging tasks. Therefore, it is important for the manipulation of its ground track. Aiming at the problem of ground track manipulation of SLAS, a control method based on tangential impulse thrust is proposed. First, the equation of the longitude difference between SLAS and the target point on the target latitude is derived based on Gauss’s variational equations. On this basis, the influence of the tangential impulse thrust on the ground track’s longitude is derived. Finally, the method for ground track manipulation of SLAS under the tangential impulse thrust is proposed. The simulation results verify the effectiveness of the method, after manipulation, the satellite can visit the target point and revisit it for multiple days.

The warhead of a ballistic missile may precess due to lateral moments during release. The resulting micro-Doppler effect is determined by parameters such as the target’s motion state and size. A three-dimensional reconstruction method for the precession warhead via the micro-Doppler analysis and inverse Radon transform (IRT) is proposed in this paper. The precession parameters are extracted by the micro-Doppler analysis from three radars, and the IRT is used to estimate the size of targe. The scatterers of the target can be reconstructed based on the above parameters. Simulation experimental results illustrate the effectiveness of the proposed method in this paper.

Strategic management of equipment system development must attach importance to effective strategic risk management. Aiming at the identification of strategic risk of equipment system development, firstly, the source of strategic risk of equipment system development is analyzed and classified. Based on this, a causal loop diagram of strategic risk of equipment system development based on system dynamics is established. The system dynamics analysis software Vensim PLE is used to carry out the risk influencing factors analysis, risk consequences analysis, risk feedback loop identification and corresponding pre-control measures, and achieves a good risk identification effect.

This paper studies a special defense game using unmanned aerial vehicle (UAV) swarm against a fast intruder. The fast intruder applies an offensive strategy based on the artificial potential field method and Apollonius circle to scout a certain destination. As defenders, the UAVs are arranged into three layers: the forward layer, the midfield layer and the back layer. The co-defense mechanism, including the role derivation method of UAV swarm and a guidance law based on the co-defense front point, is introduced for UAV swarm to co-detect the intruder. Besides, five formations are designed for comparative analysis when ten UAVs are applied. Through Monte Carlo experiments and ablation experiment, the effectiveness of the proposed co-defense method has been verified.

Link16 data link is the communication standard of the joint tactical information distribution system (JTIDS) used by the U.S. military and North Atlantic Treaty Organization, which is applied as the opportunistic illuminator for passive radar in this paper. The time-domain expression of the Link16 signal is established, and its ambiguity function expression is derived. The time-delay dimension and Doppler dimension side peaks of which lead to the appearance of the false target during target detection. To solve the problem, the time-delay dimension and Doppler dimension side peaks suppression methods are proposed. For the problem that the conventional mismatched filter (MMF) cannot suppress the time-delay dimension side peaks, a neighborhood MMF (NMMF) is proposed. Experimental results demonstrate the effectiveness of the proposed methods.

Linear minimum mean square error (MMSE) detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output (MIMO) systems but inevitably involves complicated matrix inversion, which entails high complexity. To avoid the exact matrix inversion, a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed. By combining the advantages of both the explicit and the implicit matrix inversion, this paper introduces a new low-complexity signal detection algorithm. Firstly, the relationship between implicit and explicit techniques is analyzed. Then, an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems. The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration. However, its complexity is still high for higher iterations. Thus, it is applied only for first two iterations. For subsequent iterations, we propose a novel trace iterative method (TIM) based low-complexity algorithm, which has significantly lower complexity than higher Newton iterations. Convergence guarantees of the proposed detector are also provided. Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.

In electromagnetic countermeasures circumstances, synthetic aperture radar (SAR) imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming (MISRJ), which usually owes considerable coherence with the SAR transmission waveform together with periodical modulation patterns. This paper develops an MISRJ suppression algorithm for SAR imagery with online dictionary learning. In the algorithm, the jamming modulation temporal properties are exploited with extracting and sorting MISRJ slices using fast-time autocorrelation. Online dictionary learning is followed to separate real signals from jamming slices. Under the learned representation, time-varying MISRJs are suppressed effectively. Both simulated and real-measured SAR data are also used to confirm advantages in suppressing time-varying MISRJs over traditional methods.

In this paper, we study the accuracy of delay-Doppler parameter estimation of targets in a passive radar using orthogonal frequency division multiplexing (OFDM) signal. A coarse-fine joint estimation method is proposed to achieve better estimation accuracy of target parameters without excessive computational burden. Firstly, the modulation symbol domain (MSD) method is used to roughly estimate the delay and Doppler of targets. Then, to obtain high-precision Doppler estimation, the atomic norm (AN) based on the multiple measurement vectors (MMV) model (MMV-AN) is used to manifest the signal sparsity in the continuous Doppler domain. At the same time, a reference signal compensation (RSC) method is presented to obtain high-precision delay estimation. Simulation results based on the OFDM signal show that the coarse-fine joint estimation method based on AN-RSC can obtain a more accurate estimation of target parameters compared with other algorithms. In addition, the proposed method also possesses computational advantages compared with the joint parameter estimation.

The rapid development of unmanned aerial vehicle (UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for correlation. In this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm targets. Precisely, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM) Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation, while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets.

Beam-hopping technology has become one of the major research hotspots for satellite communication in order to enhance their communication capacity and flexibility. However, beam hopping causes the traditional continuous time-division multiplexing signal in the forward downlink to become a burst signal, satellite terminal receivers need to solve multiple key issues such as burst signal rapid synchronization and high-performance reception. Firstly, this paper analyzes the key issues of burst communication for traffic signals in beam hopping systems, and then compares and studies typical carrier synchronization algorithms for burst signals. Secondly, combining the requirements of beam-hopping communication systems for efficient burst and low signal-to-noise ratio reception of downlink signals in forward links, a decoding assisted bidirectional variable parameter iterative carrier synchronization technique is proposed, which introduces the idea of iterative processing into carrier synchronization. Aiming at the technical characteristics of communication signal carrier synchronization, a new technical approach of bidirectional variable parameter iteration is adopted, breaking through the traditional understanding that loop structures cannot adapt to low signal-to-noise ratio burst demodulation. Finally, combining the DVB-S2X standard physical layer frame format used in high throughput satellite communication systems, the research and performance simulation are conducted. The results show that the new technology proposed in this paper can significantly shorten the carrier synchronization time of burst signals, achieve fast synchronization of low signal-to-noise ratio burst signals, and have the unique advantage of flexible and adjustable parameters.

How to mine valuable information from massive multi-source heterogeneous data and identify the intention of aerial targets is a major research focus at present. Aiming at the long-term dependence of air target intention recognition, this paper deeply explores the potential attribute features from the spatiotemporal sequence data of the target. First, we build an intelligent dynamic intention recognition framework, including a series of specific processes such as data source, data preprocessing, target space-time, convolutional neural networks-bidirectional gated recurrent unit-atteneion (CBA) model and intention recognition. Then, we analyze and reason the designed CBA model in detail. Finally, through comparison and analysis with other recognition model experiments, our proposed method can effectively improve the accuracy of air target intention recognition, and is of significance to the commanders’ operational command and situation prediction.

As one of the most important part of weapon system of systems (WSoS), quantitative evaluation of reconnaissance satellite system (RSS) is indispensable during its construction and application. Aiming at the problem of nonlinear effectiveness evaluation under small sample conditions, we propose an evaluation method based on support vector regression (SVR) to effectively address the defects of traditional methods. Considering the performance of SVR is influenced by the penalty factor, kernel type, and other parameters deeply, the improved grey wolf optimizer (IGWO) is employed for parameter optimization. In the proposed IGWO algorithm, the opposition-based learning strategy is adopted to increase the probability of avoiding the local optima, the mutation operator is used to escape from premature convergence and differential convergence factors are applied to increase the rate of convergence. Numerical experiments of 14 test functions validate the applicability of IGWO algorithm dealing with global optimization. The index system and evaluation method are constructed based on the characteristics of RSS. To validate the proposed IGWO-SVR evaluation method, eight benchmark data sets and combat simulation are employed to estimate the evaluation accuracy, convergence performance and computational complexity. According to the experimental results, the proposed method outperforms several prediction based evaluation methods, verifies the superiority and effectiveness in RSS operational effectiveness evaluation.

Acoustic source localization (ASL) and sound event detection (SED) are two widely pursued independent research fields. In recent years, in order to achieve a more complete spatial and temporal representation of sound field, sound event localization and detection (SELD) has become a very active research topic. This paper presents a deep learning-based multi-overlapping sound event localization and detection algorithm in three-dimensional space. Log-Mel spectrum and generalized cross-correlation spectrum are joined together in channel dimension as input features. These features are classified and regressed in parallel after training by a neural network to obtain sound recognition and localization results respectively. The channel attention mechanism is also introduced in the network to selectively enhance the features containing essential information and suppress the useless features. Finally, a thourough comparison confirms the efficiency and effectiveness of the proposed SELD algorithm. Field experiments show that the proposed algorithm is robust to reverberation and environment and can achieve higher recognition and localization accuracy compared with the baseline method.

Classical localization methods use Cartesian or Polar coordinates, which require a priori range information to determine whether to estimate position or to only find bearings. The modified polar representation (MPR) unifies near-field and far-field models, alleviating the thresholding effect. Current localization methods in MPR based on the angle of arrival (AOA) and time difference of arrival (TDOA) measurements resort to semidefinite relaxation (SDR) and Gauss-Newton iteration, which are computationally complex and face the possible diverge problem. This paper formulates a pseudo linear equation between the measurements and the unknown MPR position, which leads to a closed-form solution for the hybrid TDOA-AOA localization problem, namely hybrid constrained optimization (HCO). HCO attains Cramér-Rao bound (CRB)-level accuracy for mild Gaussian noise. Compared with the existing closed-form solutions for the hybrid TDOA-AOA case, HCO provides comparable performance to the hybrid generalized trust region subproblem (HGTRS) solution and is better than the hybrid successive unconstrained minimization (HSUM) solution in large noise region. Its computational complexity is lower than that of HGTRS. Simulations validate the performance of HCO achieves the CRB that the maximum likelihood estimator (MLE) attains if the noise is small, but the MLE deviates from CRB earlier.

An adaptive control approach is presented in this paper for tracking desired trajectories in interactive manipulators. The controller design incorporates prescribed performance functions (PPFs) to improve dynamic performance. Notably, the performance of the output error is confined in an envelope characterized by exponential convergence, leading to convergence to zero. This feature ensures a prompt response from admittance control and establishes a reliable safety framework for interactions. Simulation results provide practical insights, demonstrating the viability of the control scheme proposed in this paper.

The conformal array can make full use of the aperture, save space, meet the requirements of aerodynamics, and is sensitive to polarization information. It has broad application prospects in military, aerospace, and communication fields. The joint polarization and direction-of-arrival (DOA) estimation based on the conformal array and the theoretical analysis of its parameter estimation performance are the key factors to promote the engineering application of the conformal array. To solve these problems, this paper establishes the wave field signal model of the conformal array. Then, for the case of a single target, the cost function of the maximum likelihood (ML) estimator is rewritten with Rayleigh quotient from a problem of maximizing the ratio of quadratic forms into those of minimizing quadratic forms. On this basis, rapid parameter estimation is achieved with the idea of manifold separation technology (MST). Compared with the modified variable projection (MVP) algorithm, it reduces the computational complexity and improves the parameter estimation performance. Meanwhile, the MST is used to solve the partial derivative of the steering vector. Then, the theoretical performance of ML, the multiple signal classification (MUSIC) estimator and Cramer-Rao bound (CRB) based on the conformal array are derived respectively, which provides theoretical foundation for the engineering application of the conformal array. Finally, the simulation experiment verifies the effectiveness of the proposed method.

Time synchronization is one of the base techniques in wireless sensor networks (WSNs). This paper proposes a novel time synchronization protocol which is a robust consensus-based algorithm in the existence of transmission delay and packet loss. It compensates for transmission delay and packet loss firstly, and then, estimates clock skew and clock offset in two steps. Simulation and experiment results show that the proposed protocol can keep synchronization error below 2 μs in the grid network of 10 nodes or the random network of 90 nodes. Moreover, the synchronization accuracy in the proposed protocol can keep constant when the WSN works up to a month.

An algorithm to track multiple sharply maneuvering targets without prior knowledge about new target birth is proposed. These targets are capable of achieving sharp maneuvers within a short period of time, such as drones and agile missiles. The probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment of the full target posterior, has been shown to be a computationally efficient solution to multitarget tracking problems. However, the standard PHD filter operates on the single dynamic model and requires prior information about target birth distribution, which leads to many limitations in terms of practical applications. In this paper, we introduce a nonzero mean, white noise turn rate dynamic model and generalize jump Markov systems to multitarget case to accommodate sharply maneuvering dynamics. Moreover, to adaptively estimate newborn targets’ information, a measurement-driven method based on the recursive random sampling consensus (RANSAC) algorithm is proposed. Simulation results demonstrate that the proposed method achieves significant improvement in tracking multiple sharply maneuvering targets with adaptive birth estimation.

Aviation accidents are currently one of the leading causes of significant injuries and deaths worldwide. This entices researchers to investigate aircraft safety using data analysis approaches based on an advanced machine learning algorithm. To assess aviation safety and identify the causes of incidents, a classification model with light gradient boosting machine (LGBM) based on the aviation safety reporting system (ASRS) has been developed. It is improved by k-fold cross-validation with hybrid sampling model (HSCV), which may boost classification performance and maintain data balance. The results show that employing the LGBM-HSCV model can significantly improve accuracy while alleviating data imbalance. Vertical comparison with other cross-validation (CV) methods and lateral comparison with different fold times comprise the comparative approach. Aside from the comparison, two further CV approaches based on the improved method in this study are discussed: one with a different sampling and folding order, and the other with more CV. According to the assessment indices with different methods, the LGBM-HSCV model proposed here is effective at detecting incident causes. The improved model for imbalanced data categorization proposed may serve as a point of reference for similar data processing, and the model’s accurate identification of civil aviation incident causes can assist to improve civil aviation safety.

In this paper, we propose an effective full array and sparse array adaptive beamforming scheme that can be applied for multiple desired signals based on the branch-and-bound algorithm. Adaptive beamforming for the multiple desired signals is realized by the improved Capon method. At the same time, the sidelobe constraint is added to reduce the sidelobe level. To reduce the pointing errors of multiple desired signals, the array response phase of the desired signal is firstly optimized by using auxilary variables while keeping the response amplitude unchanged. The whole design is formulated as a convex optimization problem solved by the branch-and-bound algorithm. In addition, the beamformer weight vector is penalized with the modified reweighted ${l_1}$-norm to achieve sparsity. Theoretical analysis and simulation results show that the proposed algorithm has lower sidelobe level, higher SINR, and less pointing error than the state-of-the-art methods in the case of a single expected signal and multiple desired signals.

With the new development trend of multi-resource coordinated Earth observation and the new goal of Earth observation application of “short response time, high observation accuracy, and wide coverage”, space-aeronautics cooperative complex task planning problem has become an urgent problem to be solved. The focus of this problem is to use multiple resources to perform collaborative observations on complex tasks. By analyzing the process from task assignment to receiving task observation results, we propose a multi-layer interactive task planning framework which is composed of a preprocessing method for complex tasks, a task allocation layer, a task planning layer, and a task coordination layer. According to the characteristics of the framework, a hybrid genetic parallel tabu (HGPT) algorithm is proposed on this basis. The algorithm uses genetic annealing algorithm (GAA), parallel tabu (PT) algorithm, and heuristic rules to achieve task allocation, task planning, and task coordination. At the same time, coding improvements, operator design, annealing operations, and parallel calculations are added to the algorithm. In order to verify the effectiveness of the algorithm, simulation experiments under complex task scenarios of different scales are carried out. Experimental results show that this method can effectively solve the problems of observing complex tasks. Meanwhile, the optimization effect and convergence speed of the HGPT is better than that of the related algorithms.

The rapid growth of mobile applications, the popularity of the Android system and its openness have attracted many hackers and even criminals, who are creating lots of Android malware. However, the current methods of Android malware detection need a lot of time in the feature engineering phase. Furthermore, these models have the defects of low detection rate, high complexity, and poor practicability, etc. We analyze the Android malware samples, and the distribution of malware and benign software in application programming interface (API) calls, permissions, and other attributes. We classify the software’s threat levels based on the correlation of features. Then, we propose deep neural networks and convolutional neural networks with ensemble learning (DCEL), a new classifier fusion model for Android malware detection. First, DCEL preprocesses the malware data to remove redundant data, and converts the one-dimensional data into a two-dimensional gray image. Then, the ensemble learning approach is used to combine the deep neural network with the convolutional neural network, and the final classification results are obtained by voting on the prediction of each single classifier. Experiments based on the Drebin and Malgenome datasets show that compared with current state-of-art models, the proposed DCEL has a higher detection rate, higher recall rate, and lower computational cost.

The joint resource block (RB) allocation and power optimization problem is studied to maximize the sum-rate of the vehicle-to-vehicle (V2V) links in the device-to-device (D2D)-enabled V2V communication system, where one feasible cellular user (FCU) can share its RB with multiple V2V pairs. The problem is first formulated as a nonconvex mixed-integer nonlinear programming (MINLP) problem with constraint of the maximum interference power in the FCU links. Using the game theory, two coalition formation algorithms are proposed to accomplish V2V link partitioning and FCU selection, where the transferable utility functions are introduced to minimize the interference among the V2V links and the FCU links for the optimal RB allocation. The successive convex approximation (SCA) is used to transform the original problem into a convex one and the Lagrangian dual method is further applied to obtain the optimal transmit power of the V2V links. Finally, numerical results demonstrate the efficiency of the proposed resource allocation algorithm in terms of the system sum-rate.

In this paper, an efficient unequal error protection (UEP) scheme for online fountain codes is proposed. In the build-up phase, the traversing-selection strategy is proposed to select the most important symbols (MIS). Then, in the completion phase, the weighted-selection strategy is applied to provide low overhead. The performance of the proposed scheme is analyzed and compared with the existing UEP online fountain scheme. Simulation results show that in terms of MIS and the least important symbols (LIS), when the bit error ratio is $ {10^{ - 4}} $, the proposed scheme can achieve $ 85{\text{% }} $ and $ 31.58{\text{% }} $ overhead reduction, respectively.

The robotic airship can provide a promising aerostatic platform for many potential applications. These applications require a precise autonomous trajectory tracking control for airship. Airship has a nonlinear and uncertain dynamics. It is prone to wind disturbances that offer a challenge for a trajectory tracking control design. This paper addresses the airship trajectory tracking problem having time varying reference path. A lumped parameter estimation approach under model uncertainties and wind disturbances is opted against distributed parameters. It uses extended Kalman filter (EKF) for uncertainty and disturbance estimation. The estimated parameters are used by sliding mode controller (SMC) for ultimate control of airship trajectory tracking. This comprehensive algorithm, EKF based SMC (ESMC), is used as a robust solution to track airship trajectory. The proposed estimator provides the estimates of wind disturbances as well as model uncertainty due to the mass matrix variations and aerodynamic model inaccuracies. The stability and convergence of the proposed method are investigated using the Lyapunov stability analysis. The simulation results show that the proposed method efficiently tracks the desired trajectory. The method solves the stability, convergence, and chattering problem of SMC under model uncertainties and wind disturbances.

A dynamic multi-beam resource allocation algorithm for large low Earth orbit (LEO) constellation based on on-board distributed computing is proposed in this paper. The allocation is a combinatorial optimization process under a series of complex constraints, which is important for enhancing the matching between resources and requirements. A complex algorithm is not available because that the LEO on-board resources is limited. The proposed genetic algorithm (GA) based on two-dimensional individual model and uncorrelated single paternal inheritance method is designed to support distributed computation to enhance the feasibility of on-board application. A distributed system composed of eight embedded devices is built to verify the algorithm. A typical scenario is built in the system to evaluate the resource allocation process, algorithm mathematical model, trigger strategy, and distributed computation architecture. According to the simulation and measurement results, the proposed algorithm can provide an allocation result for more than 1500 tasks in 14 s and the success rate is more than 91% in a typical scene. The response time is decreased by 40% compared with the conditional GA.

With the rapid development of low-orbit satellite communication networks both domestically and internationally, space-terrestrial integrated networks will become the future development trend. For space and terrestrial networks with limited resources, the utilization efficiency of the entire space-terrestrial integrated networks resources can be affected by the core network indirectly. In order to improve the response efficiency of core networks expansion construction, early warning of the core network elements capacity is necessary. Based on the integrated architecture of space and terrestrial network, multidimensional factors are considered in this paper, including the number of terminals, login users, and the rules of users’ migration during holidays. Using artifical intelligence (AI) technologies, the registered users of the access and mobility management function (AMF), authorization users of the unified data management (UDM), protocol data unit (PDU) sessions of session management function (SMF) are predicted in combination with the number of login users, the number of terminals. Therefore, the core network elements capacity can be predicted in advance. The proposed method is proven to be effective based on the data from real network.

Some attributes are uncertain for evaluation work because of incomplete or limited information and knowledge. It leads to uncertainty in evaluation results. To that end, an evaluation method, uncertainty entropy-based exploratory evaluation (UEEE), is proposed to guide the evaluation activities, which can iteratively and gradually reduce uncertainty in evaluation results. Uncertainty entropy (UE) is proposed to measure the extent of uncertainty. First, the belief degree distributions are assumed to characterize the uncertainty in attributes. Then the belief degree distribution of the evaluation result can be calculated by using uncertainty theory. The obtained result is then checked based on UE to see if it could meet the requirements of decision-making. If its uncertainty level is high, more information needs to be introduced to reduce uncertainty. An algorithm based on the UE is proposed to find which attribute can mostly affect the uncertainty in results. Thus, efforts can be invested in key attribute(s), and the evaluation results can be updated accordingly. This update should be repeated until the evaluation result meets the requirements. Finally, as a case study, the effectiveness of ballistic missiles with uncertain attributes is evaluated by UEEE. The evaluation results show that the target is believed to be destroyed.

The electric-controlled metasurface antenna array (ECMSAA) with ultra-wideband frequency reconfigurable reflection suppression is proposed and realized. Firstly, an electric- controlled metasurface with ultra-wideband frequency reconfigurable in-phase reflection characteristics is designed. The element of the ECMSAA is constructed by loading the single electric-controlled metasurface unit on the conventional patch antenna element. The radiation properties of the conventional patch antenna and the reflection performance of electric-controlled metasurface are maintained when the antenna and the metasurface are integrated. Thus, the ECMSAA elements have excellent radiation properties and ultra-wideband frequency reconfigurable in-phase reflection characteristics simultaneously. To take a further step, a 6×10 ECMSAA is realized based on the designed metasurface antenna element. Simulated and measured results prove that the reflection of the ECMSAA is dynamically suppressed in the P and L bands. Meanwhile, high-gain and multi-polarization radiation properties of the ECMSAA are achieved. This design method not only realizes the frequency reconfigurable reflection suppression of the antenna array in the ultra-wide frequency band but also provides a way to develop an intelligent low-scattering antenna.

Beamspace super-resolution methods for elevation estimation in multipath environment has attracted significant attention, especially the beamspace maximum likelihood (BML) algorithm. However, the difference beam is rarely used in super-resolution methods, especially in low elevation estimation. The target airspace information in the difference beam is different from the target airspace information in the sum beam. And the use of difference beams does not significantly increase the complexity of the system and algorithms. Thus, this paper applies the difference beam to the beamformer to improve the elevation estimation performance of BML algorithm. And the direction and number of beams can be adjusted according to the actual needs. The theoretical target elevation angle root means square error (RMSE) and the computational complexity of the proposed algorithms are analyzed. Finally, computer simulations and real data processing results demonstrate the effectiveness of the proposed algorithms.

The weapon and equipment operational requirement analysis (WEORA) is a necessary condition to win a future war, among which the acquisition of knowledge about weapons and equipment is a great challenge. The main challenge is that the existing weapons and equipment data fails to carry out structured knowledge representation, and knowledge navigation based on natural language cannot efficiently support the WEORA. To solve above problem, this research proposes a method based on question answering (QA) of weapons and equipment knowledge graph (WEKG) to construct and navigate the knowledge related to weapons and equipment in the WEORA. This method firstly constructs the WEKG, and builds a neutral network-based QA system over the WEKG by means of semantic parsing for knowledge navigation. Finally, the method is evaluated and a chatbot on the QA system is developed for the WEORA. Our proposed method has good performance in the accuracy and efficiency of searching target knowledge, and can well assist the WEORA.

A terahertz (THz) wave transmitted through vegetation experiences both absorption and scattering caused by the air molecules and leaves. This paper presents the scattering attenuation characteristics of vegetation in a THz range. The theoretical path loss model near the vegetation yields the average attenuation of THz waves in a mixed channel composed of air and vegetation leaves. Furthermore, a simplified model of the vegetation structure is obtained for generic vegetation types based on a variety of parameters, such as leaf size, distribution, and moisture content. Finally, based on specific vegetation species and different levels of air humidity, the attenuation characteristics under different conditions are calculated, and the influence of different model parameters on the attenuation characteristics is obtained.

Aimed at a multiple traveling salesman problem (MTSP) with multiple depots and closed paths, this paper proposes a k-means clustering donkey and a smuggler algorithm (K-DSA). The algorithm first uses the k-means clustering method to divide all cities into several categories based on the center of various samples; the large-scale MTSP is divided into multiple separate traveling salesman problems (TSPs), and the TSP is solved through the DSA. The proposed algorithm adopts a solution strategy of clustering first and then carrying out, which can not only greatly reduce the search space of the algorithm but also make the search space more fully explored so that the optimal solution of the problem can be more quickly obtained. The experimental results from solving several test cases in the TSPLIB database show that compared with other related intelligent algorithms, the K-DSA has good solving performance and computational efficiency in MTSPs of different scales, especially with large-scale MTSP and when the convergence speed is faster; thus, the advantages of this algorithm are more obvious compared to other algorithms.

Collaborative coverage path planning (CCPP) refers to obtaining the shortest paths passing over all places except obstacles in a certain area or space. A multi-unmanned aerial vehicle (UAV) collaborative CCPP algorithm is proposed for the urban rescue search or military search in outdoor environment. Due to flexible control of small UAVs, it can be considered that all UAVs fly at the same altitude, that is, they perform search tasks on a two-dimensional plane. Based on the agents’ motion characteristics and environmental information, a mathematical model of CCPP problem is established. The minimum time for UAVs to complete the CCPP is the objective function, and complete coverage constraint, no-fly constraint, collision avoidance constraint, and communication constraint are considered. Four motion strategies and two communication strategies are designed. Then a distributed CCPP algorithm is designed based on hybrid strategies. Simulation results compared with pattern-based genetic algorithm (PBGA) and random search method show that the proposed method has stronger real-time performance and better scalability and can complete the complete CCPP task more efficiently and stably.

Passive jamming is believed to have very good potential in countermeasure community. In this paper, a passive angular blinking jamming method based on electronically controlled corner reflectors is proposed. The amplitude of the incident wave can be modulated by switching the corner reflector between the penetration state and the reflection state, and the ensemble of multiple corner reflectors with towing rope can result in complex angle decoying effects. Dependency of the decoying effect on corner reflectors ’ radar cross section and positions are analyzed and simulated. Results show that the angle measured by a monopulse radar can be significantly interfered by this method while the automatic tracking is employed.

Three-dimensional (3D) synthetic aperture radar (SAR) extends the conventional 2D images into 3D features by several acquisitions in different aspects. Compared with 3D techniques via multiple observations in elevation, e.g. SAR interferometry (InSAR) and SAR tomography (TomoSAR), holographic SAR can retrieve 3D structure by observations in azimuth. This paper focuses on designing a novel type of orbit to achieve SAR regional all-azimuth observation (AAO) for embedded targets detection and holographic 3D reconstruction. The ground tracks of the AAO orbit separate the earth surface into grids. Target in these grids can be accessed with an azimuth angle span of 360°, which is similar to the flight path of airborne circular SAR (CSAR). Inspired from the successive coverage orbits of optical sensors, several optimizations are made in the proposed method to ensure favorable grazing angles, the performance of 3D reconstruction, and long-term supervision for SAR sensors. Simulation experiments show the regional AAO can be completed within five hours. In addition, a second AAO of the same area can be duplicated in two days. Finally, an airborne SAR data process result is presented to illustrate the significance of AAO in 3D reconstruction.

Autonomous umanned aerial vehicle (UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devoted to improving the autonomous decision-making ability of UAV in an interactive environment, where finding the optimal maneuvering decision-making policy became one of the key issues for enabling the intelligence of UAV. In this paper, we propose a maneuvering decision-making algorithm for autonomous air-delivery based on deep reinforcement learning under the guidance of expert experience. Specifically, we refine the guidance towards area and guidance towards specific point tasks for the air-delivery process based on the traditional air-to-surface fire control methods. Moreover, we construct the UAV maneuvering decision-making model based on Markov decision processes (MDPs). Specifically, we present a reward shaping method for the guidance towards area and guidance towards specific point tasks using potential-based function and expert-guided advice. The proposed algorithm could accelerate the convergence of the maneuvering decision-making policy and increase the stability of the policy in terms of the output during the later stage of training process. The effectiveness of the proposed maneuvering decision-making policy is illustrated by the curves of training parameters and extensive experimental results for testing the trained policy.

The design of mini-missiles (MMs) presents several novel challenges. The stringent mission requirement to reach a target with a certain precision imposes a high guidance precision. The miniaturization of the size of MMs makes the design of the guidance, navigation, and control (GNC) have a larger-than-before impact on the main-body design (shape, motor, and layout design) and its design objective, i.e., flight performance. Pursuing a trade-off between flight performance and guidance precision, all the relevant interactions have to be accounted for in the design of the main body and the GNC system. Herein, a multi-objective and multidisciplinary design optimization (MDO) is proposed. Disciplines pertinent to motor, aerodynamics, layout, trajectory, flight dynamics, control, and guidance are included in the proposed MDO framework. The optimization problem seeks to maximize the range and minimize the guidance error. The problem is solved by using the nondominated sorting genetic algorithm II. An optimum design that balances a longer range with a smaller guidance error is obtained. Finally, lessons learned about the design of the MM and insights into the trade-off between flight performance and guidance precision are given by comparing the optimum design to a design provided by the traditional approach.