Dual-frequency multi-constellation (DFMC) satellitebased augmentation system (SBAS) does not broadcast fast correction, which is important in reducing range error in L1-only SBAS. Meanwhile, the integrity bound of a satellite at low elevation is so loose that the service availability is decreased near the boundary of the service area. Therefore, the computation of satellite clockephemeris (SCE) augmentation parameters needs improvement. We propose a method introducing SCE prediction to eliminate most of the SCE error resulting from global navigation satellite system GNSS broadcast message. Compared with the signal-inspace (SIS) after applying augmentation parameters broadcast by the wide area augmentation system (WAAS), SIS accuracy after applying augmentation parameters computed by the proposed algorithm is improved and SIS integrity is ensured. With global positioning system (GPS) only, the availability of category-I (CAT-I) with a vertical alert level of 15 m in continental United States is about 90%, while the availability in the other part of the WAAS service area is markedly improved. With measurements made by the stations from the crustal movement observation network of China, users in some part of China can obtain CAT-I (vertical alert limit is 15 m) service with GPS and global navigation satellite system (GLONASS).
This paper tackles the formation-containment control problem of fixed-wing unmanned aerial vehicle (UAV) swarm with model uncertainties for dynamic target tracking in three-dimensional space in the faulty case of UAVs ’ actuator and sensor. The fixed-wing UAV swarm under consideration is organized as a “multi-leader-multi-follower” structure, in which only several leaders can obtain the dynamic target information while others only receive the neighbors’ information through the communication network. To simultaneously realize the formation, containment, and dynamic target tracking, a two-layer control framework is adopted to decouple the problem into two subproblems: reference trajectory generation and trajectory tracking. In the upper layer, a distributed finite-time estimator (DFTE) is proposed to generate each UAV ’s reference trajectory in accordance with the control objective. Subsequently, a distributed composite robust fault-tolerant trajectory tracking controller is developed in the lower layer, where a novel adaptive extended super-twisting (AESTW) algorithm with a finite-time extended state observer (FTESO) is involved in solving the robust trajectory tracking control problem under model uncertainties, actuator, and sensor faults. The proposed controller simultaneously guarantees rapidness and enhances the system ’s robustness with fewer chattering effects. Finally, corresponding simulations are carried out to demonstrate the effectiveness and competitiveness of the proposed two-layer fault-tolerant cooperative control scheme.
Complex systems widely exist in nature and human society. There are complex interactions between system elements in a complex system, and systems show complex features at the macro level, such as emergence, self-organization, uncertainty, and dynamics. These complex features make it difficult to understand the internal operation mechanism of complex systems. Networked modeling of complex systems is a favorable means of understanding complex systems. It not only represents complex interactions but also reflects essential attributes of complex systems. This paper summarizes the research progress of complex systems modeling and analysis from the perspective of network science, including networked modeling, vital node analysis, network invulnerability analysis, network disintegration analysis, resilience analysis, complex network link prediction, and the attacker-defender game in complex networks. In addition, this paper presents some points of view on the trend and focus of future research on network analysis of complex systems.
As high-dynamics and weak-signal are of two primary concerns of navigation using Global Navigation Satellite System (GNSS) signals, an acquisition algorithm based on three-time fractional Fourier transform (FRFT) is presented to simplify the calculation effectively. Firstly, the correlation results similar to linear frequency modulated (LFM) signals are derived on the basis of the high dynamic GNSS signal model. Then, the principle of obtaining the optimum rotation angle is analyzed, which is measured by FRFT projection lengths with two selected rotation angles. Finally, Doppler shift, Doppler rate, and code phase are accurately estimated in a real-time and low signal to noise ratio (SNR) wireless communication system. The theoretical analysis and simulation results show that the fast FRFT algorithm can accurately estimate the high dynamic parameters by converting the traditional two-dimensional search process to only three times FRFT. While the acquisition performance is basically the same, the computational complexity and running time are greatly reduced, which is more conductive to practical application.
With the rapid development of informatization, autonomy and intelligence, unmanned swarm formation intelligent operations will become the main combat mode of future wars. Typical unmanned swarm formations such as ground-based directed energy weapon formations, space-based kinetic energy weapon formations, and sea-based carrier-based formations have become the trump card for winning future wars. In a complex confrontation environment, these sophisticated weapon formation systems can precisely strike mobile threat group targets, making them extreme deterrents in joint combat applications. Based on this, first, this paper provides a comprehensive summary of the outstanding advantages, strategic position and combat style of unmanned clusters in joint warfare to highlight their important position in future warfare. Second, a detailed analysis of the technological breakthroughs in four key areas, situational awareness, heterogeneous coordination, mixed combat, and intelligent assessment of typical unmanned aerial vehicle (UAV) swarms in joint warfare, is presented. An in-depth analysis of the UAV swarm communication networking operating mechanism during joint warfare is provided to lay the theoretical foundation for subsequent cooperative tracking and control. Then, an in-depth analysis of the shut-in technology requirements of UAV clusters in joint warfare is provided to lay a theoretical foundation for subsequent cooperative tracking control. Finally, the technical requirements of UAV clusters in joint warfare are analysed in depth so the key technologies can form a closed-loop kill chain system and provide theoretical references for the study of intelligent command operations.
In this paper, a comprehensive overview of radar detection methods for low-altitude targets in maritime environments is presented, focusing on the challenges posed by sea clutter and multipath scattering. The performance of the radar detection methods under sea clutter, multipath, and combined conditions is categorized and summarized, and future research directions are outlined to enhance radar detection performance for low–altitude targets in maritime environments.
Global Navigation Satellite System (GNSS)-based passive radar (GBPR) has been widely used in remote sensing applications. However, for moving target detection (MTD), the quadratic phase error (QPE) introduced by the non-cooperative target motion is usually difficult to be compensated, as the low power level of the GBPR echo signal renders the estimation of the Doppler rate less effective. Consequently, the moving target in GBPR image is usually defocused, which aggravates the difficulty of target detection even further. In this paper, a spawning particle filter (SPF) is proposed for defocused MTD. Firstly, the measurement model and the likelihood ratio function (LRF) of the defocused point-like target image are deduced. Then, a spawning particle set is generated for subsequent target detection, with reference to traditional particles in particle filter (PF) as their parent. After that, based on the PF estimator, the SPF algorithm and its sequential Monte Carlo (SMC) implementation are proposed with a novel amplitude estimation method to decrease the target state dimension. Finally, the effectiveness of the proposed SPF is demonstrated by numerical simulations and preliminary experimental results, showing that the target range and Doppler can be estimated accurately.
The unmanned aerial vehicle (UAV) swarm technology is one of the research hotspots in recent years. With the continuous improvement of autonomous intelligence of UAV, the swarm technology of UAV will become one of the main trends of UAV development in the future. This paper studies the behavior decision-making process of UAV swarm rendezvous task based on the double deep Q network (DDQN) algorithm. We design a guided reward function to effectively solve the problem of algorithm convergence caused by the sparse return problem in deep reinforcement learning (DRL) for the long period task. We also propose the concept of temporary storage area, optimizing the memory playback unit of the traditional DDQN algorithm, improving the convergence speed of the algorithm, and speeding up the training process of the algorithm. Different from traditional task environment, this paper establishes a continuous state-space task environment model to improve the authentication process of UAV task environment. Based on the DDQN algorithm, the collaborative tasks of UAV swarm in different task scenarios are trained. The experimental results validate that the DDQN algorithm is efficient in terms of training UAV swarm to complete the given collaborative tasks while meeting the requirements of UAV swarm for centralization and autonomy, and improving the intelligence of UAV swarm collaborative task execution. The simulation results show that after training, the proposed UAV swarm can carry out the rendezvous task well, and the success rate of the mission reaches 90%.
In real-time strategy (RTS) games, the ability of recognizing other players’ goals is important for creating artifical intelligence (AI) players. However, most current goal recognition methods do not take the player ’s deceptive behavior into account which often occurs in RTS game scenarios, resulting in poor recognition results. In order to solve this problem, this paper proposes goal recognition for deceptive agent, which is an extended goal recognition method applying the deductive reason method (from general to special) to model the deceptive agent’s behavioral strategy. First of all, the general deceptive behavior model is proposed to abstract features of deception, and then these features are applied to construct a behavior strategy that best matches the deceiver’s historical behavior data by the inverse reinforcement learning (IRL) method. Final, to interfere with the deceptive behavior implementation, we construct a game model to describe the confrontation scenario and the most effective interference measures.
The concept of unmanned weapon system-of-systems (UWSoS) involves a collection of various unmanned systems to achieve or accomplish a specific goal or mission. The mission reliability of UWSoS is represented by its ability to finish a required mission above the baselines of a given mission. However, issues with heterogeneity, cooperation between systems, and the emergence of UWSoS cannot be effectively solved by traditional system reliability methods. This study proposes an effective operation-loop-based mission reliability evaluation method for UWSoS by analyzing dynamic reconfiguration. First, we present a new connotation of an effective operation loop by considering the allocation of operational entities and physical resource constraints. Then, we propose an effective operation-loop-based mission reliability model for a heterogeneous UWSoS according to the mission baseline. Moreover, a mission reliability evaluation algorithm is proposed under random external shocks and topology reconfiguration, revealing the evolution law of the effective operation loop and mission reliability. Finally, a typical 60-unmanned-aerial-vehicle-swarm is taken as an example to demonstrate the proposed models and methods. The mission reliability is achieved by considering external shocks, which can serve as a reference for evaluating and improving the effectiveness of UWSoS.
Air-to-air combat tactical decisions for multiple unmanned aerial vehicles (ACTDMU) are a key decision-making step in beyond visual range combat. Complex influencing factors, strong antagonism and real-time requirements need to be considered in the ACTDMU problem. In this paper, we propose a multicriteria game approach to ACTDMU. This approach consists of a multicriteria game model and a Pareto Nash equilibrium algorithm. In this model, we form the strategy profiles for the integration of air-to-air combat tactics and weapon target assignment strategies by considering the correlation between them, and we design the vector payoff functions based on predominance factors. We propose a algorithm of Pareto Nash equilibrium based on preference relations using threshold constraints (PNE-PRTC), and we prove that the solutions obtained by this algorithm are refinements of Pareto Nash equilibrium solutions. The numerical experiments indicate that PNE-PRTC algorithm is considerably faster than the baseline algorithms and the performance is better. Especially on large-scale instances, the Pareto Nash equilibrium solutions can be calculated by PNE-PRTC algorithm at the second level. The simulation experiments show that the multicriteria game approach is more effective than one-side decision approaches such as multiple-attribute decision-making and randomly chosen decisions.
It is essential to maximize capacity while satisfying the transmission time delay of unmanned aerial vehicle (UAV) swarm communication system. In order to address this challenge, a dynamic decentralized optimization mechanism is presented for the realization of joint spectrum and power (JSAP) resource allocation based on deep Q-learning networks (DQNs). Each UAV to UAV (U2U) link is regarded as an agent that is capable of identifying the optimal spectrum and power to communicate with one another. The convolutional neural network, target network, and experience replay are adopted while training. The findings of the simulation indicate that the proposed method has the potential to improve both communication capacity and probability of successful data transmission when compared with random centralized assignment and multichannel access methods.
Recognition of pulse repetition interval (PRI) modulation is a fundamental task in the interpretation of radar intentions. However, the existing PRI modulation recognition methods mainly focus on single-label classification of PRI sequences. The prerequisite for the effectiveness of these methods is that the PRI sequences are perfectly divided according to different modulation types before identification, while the actual situation is that radar pulses reach the receiver continuously, and there is no completely reliable method to achieve this division in the case of non-cooperative reception. Based on the above actual needs, this paper implements an algorithm based on the recurrence plot technique and the multi-target detection model, which does not need to divide the PRI sequence in advance. Compared with the sliding window method, it can more effectively realize the recognition of the dynamically varying PRI modulation.
To meet the requirements of safety, concealment, and timeliness of trajectory planning during the unmanned aerial vehicle (UAV) penetration process, a three-dimensional path planning algorithm is proposed based on improved holonic particle swarm optimization (IHPSO). Firstly, the requirements of terrain threat, radar detection, and penetration time in the process of UAV penetration are quantified. Regarding radar threats, a radar echo analysis method based on radar cross section (RCS) and the spatial situation is proposed to quantify the concealment of UAV penetration. Then the structure-particle swarm optimization (PSO) algorithm is improved from three aspects. First, the conversion ability of the search strategy is enhanced by using the system clustering method and the information entropy grouping strategy instead of random grouping and constructing the state switching conditions based on the fitness function. Second, the unclear setting of iteration numbers is addressed by using particle spacing to create the termination condition of the algorithm. Finally, the trajectory is optimized to meet the intended requirements by building a predictive control model and using the IHPSO for simulation verification. Numerical examples show the superiority of the proposed method over the existing PSO methods.
Rotating machinery is widely used in the industry. They are vulnerable to many kinds of damages especially for those working under tough and time-varying operation conditions. Early detection of these damages is important, otherwise, they may lead to large economic loss even a catastrophe. Many signal processing methods have been developed for fault diagnosis of the rotating machinery. Local mean decomposition (LMD) is an adaptive mode decomposition method that can decompose a complicated signal into a series of mono-components, namely product functions (PFs). In recent years, many researchers have adopted LMD in fault detection and diagnosis of rotating machines. We give a comprehensive review of LMD in fault detection and diagnosis of rotating machines. First, the LMD is described. The advantages, disadvantages and some improved LMD methods are presented. Then, a comprehensive review on applications of LMD in fault diagnosis of the rotating machinery is given. The review is divided into four parts:fault diagnosis of gears, fault diagnosis of rotors, fault diagnosis of bearings, and other LMD applications. In each of these four parts, a review is given to applications applying the LMD, improved LMD, and LMD-based combination methods, respectively. We give a summary of this review and some future potential topics at the end.
According to the oversampling imaging characteristics, an infrared small target detection method based on deep learning is proposed. A 7-layer deep convolutional neural network (CNN) is designed to automatically extract small target features and suppress clutters in an end-to-end manner. The input of CNN is an original oversampling image while the output is a cluttersuppressed feature map. The CNN contains only convolution and non-linear operations, and the resolution of the output feature map is the same as that of the input image. The L1-norm loss function is used, and a mass of training data is generated to train the network effectively. Results show that compared with several baseline methods, the proposed method improves the signal clutter ratio gain and background suppression factor by 3 – 4 orders of magnitude, and has more powerful target detection performance.
For imbalanced datasets, the focus of classification is to identify samples of the minority class. The performance of current data mining algorithms is not good enough for processing imbalanced datasets. The synthetic minority over-sampling technique (SMOTE) is specifically designed for learning from imbalanced datasets, generating synthetic minority class examples by interpolating between minority class examples nearby. However, the SMOTE encounters the overgeneralization problem. The density-based spatial clustering of applications with noise (DBSCAN) is not rigorous when dealing with the samples near the borderline. We optimize the DBSCAN algorithm for this problem to make clustering more reasonable. This paper integrates the optimized DBSCAN and SMOTE, and proposes a density-based synthetic minority over-sampling technique (DSMOTE). First, the optimized DBSCAN is used to divide the samples of the minority class into three groups, including core samples, borderline samples and noise samples, and then the noise samples of minority class is removed to synthesize more effective samples. In order to make full use of the information of core samples and borderline samples, different strategies are used to over-sample core samples and borderline samples. Experiments show that DSMOTE can achieve better results compared with SMOTE and Borderline-SMOTE in terms of precision, recall and F-value.
The measurement of the rolling angle of the projectile is one of the key technologies for the terminal correction projectile. To improve the resolution accuracy of the rolling angle in the laser seeker weapon system, the imaging model of the detector, calculation model of the position and the signal-to-noise ratio (SNR) model of the circuit are built to derive both the correlation between the resolution error of the rolling angle and the spot position, and the relation between the position resolution error and the SNR. Then the influence of each parameter on the SNR is analyzed at large, and the parameters of the circuit are determined. Meanwhile, the SNR and noise voltage of the circuit are calculated according to the SNR model and the decay model of the laser energy. Finally, the actual photoelectric detection circuit is built, whose SNR is measured to be up to 53 dB. It can fully meet the requirement of 0.5° for the resolution error of the rolling angle, thereby realizing the analysis of critical technology for photoelectric detection of laser seeker signals.
Long-time coherent integration (LTCI) is an effective way for radar maneuvering target detection, but it faces the problem of a large number of search parameters and large amount of calculation. Realizing the simultaneous compensation of the range and Doppler migrations in complex clutter background, and at the same time improving the calculation efficiency has become an urgent problem to be solved. The sparse transformation theory is introduced to LTCI in this paper, and a non-parametric searching sparse LTCI (SLTCI) based maneuvering target detection method is proposed. This method performs time reversal (TR) and second-order Keystone transform (SKT) in the range frequency & slow-time data to complete high-order range walk compensation, and achieves the coherent integration of maneuvering target across range and Doppler units via the robust sparse fractional Fourier transform (RSFRFT). It can compensate for the nonlinear range migration caused by high-order motion. S-band and X-band radar data measured in sea clutter background are used to verify the detection performance of the proposed method, which can achieve better detection performance of maneuvering targets with less computational burden compared with several popular integration methods.
This paper proposes the nonlinear direct data-driven control from theoretical analysis and practical engineering, i.e., unmanned aerial vehicle (UAV) formation flight system. Firstly, from the theoretical point of view, consider one nonlinear closed-loop system with a nonlinear plant and nonlinear feed-forward controller simultaneously. To avoid the complex identification process for that nonlinear plant, a nonlinear direct data-driven control strategy is proposed to design that nonlinear feed-forward controller only through the input-output measured data sequence directly, whose detailed explicit forms are model inverse method and approximated analysis method. Secondly, from the practical point of view, after reviewing the UAV formation flight system, nonlinear direct data-driven control is applied in designing the formation controller, so that the followers can track the leader’s desired trajectory during one small time instant only through solving one data fitting problem. Since most natural phenomena have nonlinear properties, the direct method must be the better one. Corresponding system identification and control algorithms are required to be proposed for those nonlinear systems, and the direct nonlinear controller design is the purpose of this paper.
Large-scale and diverse businesses based on the cloud computing platform bring the heavy network traffic to cloud data centers. However, the unbalanced workload of cloud data center network easily leads to the network congestion, the low resource utilization rate, the long delay, the low reliability, and the low throughput. In order to improve the utilization efficiency and the quality of services (QoS) of cloud system, especially to solve the problem of network congestion, we propose MTSS, a multi-path traffic scheduling mechanism based on software defined networking (SDN). MTSS utilizes the data flow scheduling flexibility of SDN and the multi-path feature of the fat-tree structure to improve the traffic balance of the cloud data center network. A heuristic traffic balancing algorithm is presented for MTSS, which periodically monitors the network link and dynamically adjusts the traffic on the heavy link to achieve programmable data forwarding and load balancing. The experimental results show that MTSS outperforms equal-cost multi-path protocol (ECMP), by effectively reducing the packet loss rate and delay. In addition, MTSS improves the utilization efficiency, the reliability and the throughput rate of the cloud data center network.
In this paper, the formation control problem of second-order nonholonomic mobile robot systems is investigated in a dynamic event-triggered scheme. Event-triggered control protocols combined with persistent excitation (PE) conditions are presented. In event-detecting processes, an inactive time is introduced after each sampling instant, which can ensure a positive minimum sampling interval. To increase the flexibility of the event-triggered scheme, internal dynamic variables are included in event-triggering conditions. Moreover, the dynamic event-triggered scheme plays an important role in increasing the lengths of time intervals between any two consecutive events. In addition, event-triggered control protocols without forward and angular velocities are also presented based on approximate-differentiation (low-pass) filters. The asymptotic convergence results are given based on a nested Matrosov theorem and artificial sampling methods.
This paper presents a path planning approach for rotary unmanned aerial vehicles (R-UAVs) in a known static rough terrain environment. This approach aims to find collision-free and feasible paths with minimum altitude, length and angle variable rate. First, a three-dimensional (3D) modeling method is proposed to reduce the computation burden of the dynamic models of R-UAVs. Considering the length, height and tuning angle of a path, the path planning of R-UAVs is described as a tri-objective optimization problem. Then, an improved multi-objective particle swarm optimization algorithm is developed. To render the algorithm more effective in dealing with this problem, a vibration function is introduced into the collided solutions to improve the algorithm efficiency. Meanwhile, the selection of the global best position is taken into account by the reference point method. Finally, the experimental environment is built with the help of the Google map and the 3D terrain generator World Machine. Experimental results under two different rough terrains from Guilin and Lanzhou of China demonstrate the capabilities of the proposed algorithm in finding Pareto optimal paths.
The rapid development of data communication in modern era demands secure exchange of information. Steganography is an established method for hiding secret data from an unauthorized access into a cover object in such a way that it is invisible to human eyes. The cover object can be image, text, audio, or video. This paper proposes a secure steganography algorithm that hides a bitstream of the secret text into the least significant bits (LSBs) of the approximation coefficients of the integer wavelet transform (IWT) of grayscale images as well as each component of color images to form stego-images. The embedding and extracting phases of the proposed steganography algorithms are performed using the MATLAB software. Invisibility, payload capacity, and security in terms of peak signal to noise ratio (PSNR) and robustness are the key challenges to steganography. The statistical distortion between the cover images and the stego-images is measured by using the mean square error (MSE) and the PSNR, while the degree of closeness between them is evaluated using the normalized cross correlation (NCC). The experimental results show that, the proposed algorithms can hide the secret text with a large payload capacity with a high level of security and a higher invisibility. Furthermore, the proposed technique is computationally efficient and better results for both PSNR and NCC are achieved compared with the previous algorithms.
The formation of the manned aerial vehicle/unmanned aerial vehicle (MAV/UAV) task coalition is considered. To reduce the scale of the problem, the formation progress is divided into three phases. For the task clustering phase, the geographical position of tasks is taken into consideration and a cluster method is proposed. For the UAV allocation phase, the UAV requirement for both constrained and unconstrained resources is introduced, and a multi-objective optimal algorithm is proposed to solve the allocation problem. For the MAV allocation phase, the optimal model is firstly constructed and it is decomposed according to the ideal of greed to reduce the time complexity of the algorithm. Based on the above phases, the MAV/UAV task coalition formation method is proposed and the effectiveness and practicability are demonstrated by simulation examples.
Motion planning is critical to realize the autonomous operation of mobile robots. As the complexity and randomness of robot application scenarios increase, the planning capability of the classical hierarchical motion planners is challenged. With the development of machine learning, the deep reinforcement learning (DRL)-based motion planner has gradually become a research hotspot due to its several advantageous feature. The DRL-based motion planner is model-free and does not rely on the prior structured map. Most importantly, the DRL-based motion planner achieves the unification of the global planner and the local planner. In this paper, we provide a systematic review of various motion planning methods. Firstly, we summarize the representative and state-of-the-art works for each submodule of the classical motion planning architecture and analyze their performance features. Then, we concentrate on summarizing reinforcement learning (RL)-based motion planning approaches, including motion planners combined with RL improvements, map-free RL-based motion planners, and multi-robot cooperative planning methods. Finally, we analyze the urgent challenges faced by these mainstream RL-based motion planners in detail, review some state-of-the-art works for these issues, and propose suggestions for future research.
This paper proposes a fast integral terminal sliding mode (ITSM) control method for a cascaded nonlinear dynamical system with mismatched uncertainties. Firstly, an integral terminal sliding mode surface is presented, which not only avoids the singularity in the traditional terminal sliding mode, but also addresses the mismatched problems in the nonlinear control system. Secondly, a new ITSM controller with finite convergence time based on the backstepping technique is derived for a cascaded nonlinear dynamical system with mismatched uncertainties. Thirdly, the convergence time of ITSM is analyzed, whose convergence speed is faster than those of two nonsingular terminal sliding modes. Finally, simulation results are presented in order to evaluate the effectiveness of ITSM control strategies for mismatched uncertainties.
The contribution rate of equipment system-of-systems architecture (ESoSA) is an important index to evaluate the equipment update, development, and architecture optimization. Since the traditional ESoSA contribution rate evaluation method does not make full use of the fuzzy information and uncertain information in the equipment system-of-systems (ESoS), and the Bayesian network is an effective tool to solve the uncertain information, a new ESoSA contribution rate evaluation method based on the fuzzy Bayesian network (FBN) is proposed. Firstly, based on the operation loop theory, an ESoSA is constructed considering three aspects: reconnaissance equipment, decision equipment, and strike equipment. Next, the fuzzy set theory is introduced to construct the FBN of ESoSA to deal with fuzzy information and uncertain information. Furthermore, the fuzzy importance index of the root node of the FBN is used to calculate the contribution rate of the ESoSA, and the ESoSA contribution rate evaluation model based on the root node fuzzy importance is established. Finally, the feasibility and rationality of this method are validated via an empirical case study of aviation ESoSA. Compared with traditional methods, the evaluation method based on FBN takes various failure states of equipment into consideration, is free of acquiring accurate probability of traditional equipment failure, and models the uncertainty of the relationship between equipment. The proposed method not only supplements and improves the ESoSA contribution rate assessment method, but also broadens the application scope of the Bayesian network.
This paper concerns minimum-energy leader-following formation design and analysis problems of distributed multi-agent systems (DMASs) subjected to randomly switching topologies and aperiodic communication pauses. The critical feature of this paper is that the energy consumption during the formation control process is restricted by the minimum-energy constraint in the sense of the linear matrix inequality. Firstly, the leader-following formation control protocol is proposed based on the relative state information of neighboring agents, where the total energy consumption is considered. Then, minimum-energy leader-following formation design and analysis criteria are presented in the form of the linear matrix inequality, which can be checked by the generalized eigenvalue method. Especially, the value of the minimum-energy constraint is determined. An illustrative simulation is provided to show the effectiveness of the main results.
The time difference of arrival (TDOA) estimation plays a crucial role in the accurate localization of the satellite interference source. In the dual-satellites interference source localization system, the target signal from the adjacent satellite is likely to be interfered by the normal communication signal with the same frequency. Therefore, the signal to noise ratio (SNR) of the target signal would become too low, and the TDOA estimation through cross-correlation processing would be unreliable or even unattainable. This paper proposes a technique based on blind separation to solve the co-channel interference problem, where separation of the mixed signal can be carried out by the particle filter (PF) algorithm. The experimental results show that the proposed method could achieve more accurate TDOA estimation. The measured data obtained by using the software radio platform at 915 MHz and 2 GHz respectively verify the effectiveness of the proposed method.
To reach a higher level of autonomy for unmanned combat aerial vehicle (UCAV) in air combat games, this paper builds an autonomous maneuver decision system. In this system, the air combat game is regarded as a Markov process, so that the air combat situation can be effectively calculated via Bayesian inference theory. According to the situation assessment result, adaptively adjusts the weights of maneuver decision factors, which makes the objective function more reasonable and ensures the superiority situation for UCAV. As the air combat game is characterized by highly dynamic and a significant amount of uncertainty, to enhance the robustness and effectiveness of maneuver decision results, fuzzy logic is used to build the functions of four maneuver decision factors. Accuracy prediction of opponent aircraft is also essential to ensure making a good decision; therefore, a prediction model of opponent aircraft is designed based on the elementary maneuver method. Finally, the moving horizon optimization strategy is used to effectively model the whole air combat maneuver decision process. Various simulations are performed on typical scenario test and close-in dogfight, the results sufficiently demonstrate the superiority of the designed maneuver decision method.
Prior research on the resilience of critical infrastructure usually utilizes the network model to characterize the structure of the components so that a quantitative representation of resilience can be obtained. Particularly, network component importance is addressed to express its significance in shaping the resilience performance of the whole system. Due to the intrinsic complexity of the problem, some idealized assumptions are exerted on the resilience-optimization problem to find partial solutions. This paper seeks to exploit the dynamic aspect of system resilience, i.e., the scheduling problem of link recovery in the post-disruption phase. The aim is to analyze the recovery strategy of the system with more practical assumptions, especially inhomogeneous time cost among links. In view of this, the presented work translates the resilience-maximization recovery plan into the dynamic decisionmaking of runtime recovery option. A heuristic scheme is devised to treat the core problem of link selection in an ongoing style. Through Monte Carlo simulation, the link recovery order rendered by the proposed scheme demonstrates excellent resilience performance as well as accommodation with uncertainty caused by epistemic knowledge.
Unmanned aerial vehicles (UAVs) may play an important role in data collection and offloading in vast areas deploying wireless sensor networks, and the UAV's action strategy has a vital influence on achieving applicability and computational complexity. Dynamic programming (DP) has a good application in the path planning of UAV, but there are problems in the applicability of special terrain environment and the complexity of the algorithm. Based on the analysis of DP, this paper proposes a hierarchical directional DP (DDP) algorithm based on direction determination and hierarchical model. We compare our methods with Q-learning and DP algorithm by experiments, and the results show that our method can improve the terrain applicability, meanwhile greatly reduce the computational complexity.
Anti-jamming solutions based on antenna arrays enhance the anti-jamming ability and the robustness of global navigation satellite system (GNSS) receiver remarkably. However, the performance of the receiver will deteriorate significantly in the overloaded interferences scenario. We define the overloaded interferences scenario as where the number of interferences is more than or equal to the number of antenna arrays elements. In this paper, the effect mechanism of interferences with different incident directions is found by studying the anti-jamming performance of the adaptive space filter. The theoretical analysis and conclusions, which are first validated through numerical examples, reveal the relationships between the optimal weight vector and the eigenvectors of the input signal autocorrelation matrix, the relationships between the interference cancellation ratio (ICR), the signal to interference and noise power ratio (SINR) of the adaptive space filter output and the number of interferences, the eigenvalues of the input signal autocorrelation matrix. In addition, two simulation experiments are utilized to further corroborate the theoretical findings through soft anti-jamming receiver. The simulation results match well with the theoretical analysis results, thus validating the effect mechanism of overloaded interferences. The simulation results show that, for a four elements circular array, the performance difference is up to 19 dB with different incident directions of interferences. Anti-jamming performance evaluation and jamming deployment optimization can obtain more accurate and efficient results by using the conclusions.
An efficient and real-time simulation method is proposed for the dynamic electromagnetic characteristics of cluster targets to meet the requirements of engineering practical applications. First, the coordinate transformation method is used to establish a geometric model of the observation scene, which is described by the azimuth angles and elevation angles of the radar in the target reference frame and the attitude angles of the target in the radar reference frame. Then, an approach for dynamic electromagnetic scattering simulation is proposed. Finally, a fast-computing method based on sparsity in the time domain, space domain, and frequency domain is proposed. The method analyzes the sparsity-based dynamic scattering characteristic of the typical cluster targets. The error between the sparsity-based method and the benchmark is small, proving the effectiveness of the proposed method.
The anti-aircraft system plays an irreplaceable role in modern combat. An anti-aircraft system consists of various types of functional entities interacting to destroy the hostile aircraft moving in high speed. The connecting structure of combat entities in it is of great importance for supporting the normal process of the system. In this paper, we explore the optimizing strategy of the structure of the anti-aircraft network by establishing extra communication channels between the combat entities. Firstly, the thought of combat network model (CNM) is borrowed to model the anti-aircraft system as a heterogeneous network. Secondly, the optimization objectives are determined as the survivability and the accuracy of the system. To specify these objectives, the information chain and accuracy chain are constructed based on CNM. The causal strength (CAST) logic and influence network (IN) are introduced to illustrate the establishment of the accuracy chain. Thirdly, the optimization constraints are discussed and set in three aspects: time, connection feasibility and budget. The time constraint network (TCN) is introduced to construct the timing chain and help to detect the timing consistency. Then, the process of the multi-objective optimization of the structure of the anti-aircraft system is designed. Finally, a simulation is conducted to prove the effectiveness and feasibility of the proposed method. Non-dominated sorting based genetic algorithm-II (NSGA2) is used to solve the multi-objective optimization problem and two other algorithms including non-dominated sorting based genetic algorithm-III (NSGA3) and strength Pareto evolutionary algorithm-II (SPEA2) are employed as comparisons. The deciders and system builders can make the anti-aircraft system improved in the survivability and accuracy in the combat reality.
The time dependent vehicle routing problem with time windows (TDVRPTW) is considered. A multi-type ant system (MTAS) algorithm hybridized with the ant colony system (ACS) and the max-min ant system (MMAS) algorithms is proposed. This combination absorbs the merits of the two algorithms in solutions construction and optimization separately. In order to improve the efficiency of the insertion procedure, a nearest neighbor selection (NNS) mechanism, an insertion local search procedure and a local optimization procedure are specified in detail. And in order to find a balance between good scouting performance and fast convergence rate, an adaptive pheromone updating strategy is proposed in the MTAS. Computational results confirm the MTAS algorithm's good performance with all these strategies on classic vehicle routing problem with time windows (VRPTW) benchmark instances and the TDVRPTW instances, and some better results especially for the number of vehicles and travel times of the best solutions are obtained in comparison with the previous research.
