In a cloud-native era, the Kubernetes-based workflow engine enables workflow containerized execution through the inherent abilities of Kubernetes. However, when encountering continuous workflow requests and unexpected resource request spikes, the engine is limited to the current workflow load information for resource allocation, which lacks the agility and predictability of resource allocation, resulting in over and under-provisioning resources. This mechanism seriously hinders workflow execution efficiency and leads to high resource waste. To overcome these drawbacks, we propose an adaptive resource allocation scheme named adaptive resource allocation scheme (ARAS) for the Kubernetes-based workflow engines. Considering potential future workflow task requests within the current task pod’s lifecycle, the ARAS uses a resource scaling strategy to allocate resources in response to high-concurrency workflow scenarios. The ARAS offers resource discovery, resource evaluation, and allocation functionalities and serves as a key component for our tailored workflow engine (KubeAdaptor). By integrating the ARAS into KubeAdaptor for workflow containerized execution, we demonstrate the practical abilities of KubeAdaptor and the advantages of our ARAS. Compared with the baseline algorithm, experimental evaluation under three distinct workflow arrival patterns shows that ARAS gains time-saving of 9.8% to 40.92% in the average total duration of all workflows, time-saving of 26.4% to 79.86% in the average duration of individual workflow, and an increase of 1% to 16% in centrol processing unit (CPU) and memory resource usage rate.

Intelligent wars can take place not only in the physical domain and information domain but also in the cognitive domain. The cognitive domain will become the key domain to win in the future intelligent war. A Lanchester equation considering cognitive domain is proposed to fit the development tendency intelligent wars in this paper. One party is considered to obtain the exponential enhancement advantage on combat forces in combat if it can gain an advantage in the cognitive domain over the other party according to the systemic advantage function. The operational effectiveness of the cognitive domain in war is considered to consist of a series of indicators. Hesitant fuzzy sets and linguistic term sets are powerful tools when evaluating indicators, hence the indicators are scored by experts using hesitant fuzzy linguistic terms sets here. A unique hesitant fuzzy hybrid arithmetical averaging operator is used to aggregate the evaluation.

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.

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.

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.

This review article aims to give a comprehensive review of periodic orbits in the circular restricted three-body problem (CRTBP), which is a standard ideal model for the Earth-Moon system and is closest to the practical mechanical model. It focuses the attention on periodic orbits in the Earth-Moon system. This work is primarily motivated by a series of missions and plans that take advantages of the three-body periodic orbits near the libration points or around two gravitational celestial bodies. Firstly, simple periodic orbits and their classi?cation that is usually considered to be early work before 1970 are summarized, and periodic orbits around Lagrange points, either planar or three-dimensional, are intensively studied during past decades. Subsequently, stability index of a periodic orbit and bifurcation analysis are presented, which demonstrate a guideline to ?nd more periodic orbits inspired by bifurcation signals. Then, the practical techniques for computing a wide range of periodic orbits and associated quasi-periodic orbits, as well as constructing database of periodic orbits by numerical searching techniques are also presented. For those unstable periodic orbits, the station keeping maneuvers are reviewed. Finally, the applications of periodic orbits are presented, including those in practical missions, under consideration, and still in conceptual design stage. This review article has the function of bridging between engineers and researchers, so as to make it more convenient and faster for engineers to understand the complex restricted three-body problem (RTBP). At the same time, it can also provide some technical thinking for general researchers.

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.

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%.

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.

In this paper, a velocity filtering based track-before-detect algorithm in mixed coordinates is presented to address the problem of integration loss caused by inaccurate motion model in polar coordinate sensors. Since the motion of a constant velocity (CV) target is better modeled in Cartesian coordinates, the search of measurements for integration in polar sensor coordinates is carried out according to the CV model in Cartesian coordinates instead of an approximate model in polar sensor coordinates. The position of each cell is converted into Cartesian coordinates and predicted according to an assumed velocity. Then, the predicted Cartesian position is converted back to polar sensor coordinates for multiframe accumulation. The use of the correct model improves integration effectiveness and consequently improves algorithm performance. To handle the weak target with unknown velocity, a velocity filter bank in mixed coordinates is presented. The influence of velocity mismatch on the performance of filter bank is analyzed, and an efficient strategy for filter bank design is proposed. Numerical results are presented to demonstrate the effectiveness of the proposed algorithm.

In some tracking applications, due to the sensor characteristic, only range measurements are available. If this is the case, due to the lack of full position measurements, the observability of Cartesian states (e.g., position and velocity) are limited to particular cases. For general cases, the range measurements can be utilized by developing a state estimation algorithm in range-Doppler (R-D) plane to obtain accurate range and Doppler estimates. In this paper, a state estimation method based on the proper dynamic model in the R-D plane is proposed. The unscented Kalman filter is employed to handle the strong nonlinearity in the dynamic model. Two filtering initialization methods are derived to extract the initial state estimate and the initial covariance in the R-D plane from the first several range measurements. One is derived based on the well-known two-point differencing method. The other incorporates the correct dynamic model information and uses the unscented transformation method to obtain the initial state estimates and covariance, resulting in a model-based method, which capitalizes the model information to yield better performance. Monte Carlo simulation results are provided to illustrate the effectiveness and superior performance of the proposed state estimation and filter initialization methods.

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.

By deploying the ubiquitous and reliable coverage of low Earth orbit (LEO) satellite networks using optical inter satellite link (OISL), computation offloading services can be provided for any users without proximal servers, while the resource limitation of both computation and storage on satellites is the important factor affecting the maximum task completion time. In this paper, we study a delay-optimal multi-satellite collaborative computation offloading scheme that allows satellites to actively migrate tasks among themselves by employing the high-speed OISLs, such that tasks with long queuing delay will be served as quickly as possible by utilizing idle computation resources in the neighborhood. To satisfy the delay requirement of delay-sensitive task, we first propose a deadline-aware task scheduling scheme in which a priority model is constructed to sort the order of tasks being served based on its deadline, and then a delay-optimal collaborative offloading scheme is derived such that the tasks which cannot be completed locally can be migrated to other idle satellites. Simulation results demonstrate the effectiveness of our multi-satellite collaborative computation offloading strategy in reducing task complement time and improving resource utilization of the LEO satellite network.

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.

In this paper, we propose a beam space coversion (BSC)-based approach to achieve a single near-field signal localization under uniform circular array (UCA). By employing the centro-symmetric geometry of UCA, we apply BSC to extract the two-dimensional (2-D) angles of near-field signal in the Vandermonde form, which allows for azimuth and elevation angle estimation by utilizing the improved estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. By substituting the calculated 2-D angles into the direction vector of near-field signal, the range parameter can be consequently obtained by the 1-D multiple signal classification (MUSIC) method. Simulations demonstrate that the proposed algorithm can achieve a single near-field signal localization, which can provide satisfactory performance and reduce computational complexity.

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.

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.

In this paper, we first propose a memristive chaotic system and implement it by circuit simulation. The chaotic dynamics and various attractors are analysed by using phase portrait, bifurcation diagram, and Lyapunov exponents. In particular, the system has robust chaos in a wide parameter range and the initial value space, which is favourable to the security communication application. Consequently, we further explore its application in image encryption and present a new scheme. Before image processing, the external key is protected by the Grain-128a algorithm and the initial values of the memristive system are updated with the plain image. We not only perform random pixel extraction and masking with the chaotic cipher, but also use them as control parameters for Brownian motion to obtain the permutation matrix. In addition, multiplication on the finite field GF(2⁸) is added to further enhance the cryptography. Finally, the simulation results verify that the proposed image encryption scheme has better performance and higher security, which can effectively resist various attacks.

In the process of performing a task, autonomous unmanned systems face the problem of scene changing, which requires the ability of real-time decision-making under dynamically changing scenes. Therefore, taking the unmanned system coordinative region control operation as an example, this paper combines knowledge representation with probabilistic decision-making and proposes a role-based Bayesian decision model for autonomous unmanned systems that integrates scene cognition and individual preferences. Firstly, according to utility value decision theory, the role-based utility value decision model is proposed to realize task coordination according to the preference of the role that individual is assigned. Then, multi-entity Bayesian network is introduced for situation assessment, by which scenes and their uncertainty related to the operation are semantically described, so that the unmanned systems can conduct situation awareness in a set of scenes with uncertainty. Finally, the effectiveness of the proposed method is verified in a virtual task scenario. This research has important reference value for realizing scene cognition, improving cooperative decision-making ability under dynamic scenes, and achieving swarm level autonomy of unmanned systems.

Coherent change detection (CCD) is an effective method to detect subtle scene changes that occur between temporal synthetic aperture radar (SAR) observations. Most coherence estimators are obtained from a Hermitian product based on local statistics. Increasing the number of samples in the local window can improve the estimation bias, but cause the loss of the estimated images spatial resolution. The limitations of these estimators lead to unclear contour of the disturbed region, and even the omission of fine change targets. In this paper, a CCD approach is proposed to detect fine scene changes from multi-temporal and multi-angle SAR image pairs. Multi-angle CCD estimator can improve the contrast between the change target and the background clutter by jointly accumulating single-angle alternative estimator results without further loss of image resolution. The sensitivity of detection performance to image quantity and angle interval is analyzed. Theoretical analysis and experimental results verify the performance of the proposed algorithm.

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.

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.

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.

Aerial image sequence mosaicking is one of the challenging research fields in computer vision. To obtain large-scale orthophoto maps with object detection information, we propose a vision-based image mosaicking algorithm without any extra location data. According to object detection results, we define a complexity factor to describe the importance of each input image and dynamically optimize the feature extraction process. The feature points extraction and matching processes are mainly guided by the speeded-up robust features (SURF) and the grid motion statistic (GMS) algorithm respectively. A robust reference frame selection method is proposed to eliminate the transformation distortion by searching for the center area based on overlaps. Besides, the sparse Levenberg-Marquardt (LM) algorithm and the heavy occluded frames removal method are applied to reduce accumulated errors and further improve the mosaicking performance. The proposed algorithm is performed by using multithreading and graphics processing unit (GPU) acceleration on several aerial image datasets. Extensive experiment results demonstrate that our algorithm outperforms most of the existing aerial image mosaicking methods in visual quality while guaranteeing a high calculation speed.

In the field of satellite imagery, remote sensing image captioning (RSIC) is a hot topic with the challenge of overfitting and difficulty of image and text alignment. To address these issues, this paper proposes a vision-language aligning paradigm for RSIC to jointly represent vision and language. First, a new RSIC dataset DIOR-Captions is built for augmenting object detection in optical remote (DIOR) sensing images dataset with manually annotated Chinese and English contents. Second, a Vision-Language aligning model with Cross-modal Attention (VLCA) is presented to generate accurate and abundant bilingual descriptions for remote sensing images. Third, a cross-modal learning network is introduced to address the problem of visual-lingual alignment. Notably, VLCA is also applied to end-to-end Chinese captions generation by using the pre-training language model of Chinese. The experiments are carried out with various baselines to validate VLCA on the proposed dataset. The results demonstrate that the proposed algorithm is more descriptive and informative than existing algorithms in producing captions.

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.

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.

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.

Code acquisition is the kernel operation for signal synchronization in the spread-spectrum receiver. To reduce the computational complexity and latency of code acquisition, this paper proposes an efficient scheme employing sparse Fourier transform (SFT) and the relevant hardware architecture for field programmable gate array (FPGA) and application-specific integrated circuit (ASIC) implementation. Efforts are made at both the algorithmic level and the implementation level to enable merged searching of code phase and Doppler frequency without incurring massive hardware expenditure. Compared with the existing code acquisition approaches, it is shown from theoretical analysis and experimental results that the proposed design can shorten processing latency and reduce hardware complexity without degrading the acquisition probability.

In this paper, we introduce an incident angle based fusion method for radar and infrared sensors to improve the recognition rate of complex targets under half space scenarios, e.g., vehicles on the ground in this paper. For radar sensors, convolutional operation is introduced into the autoencoder, a “winner-take-all (WTA)” convolutional autoencoder (CAE) is used to improve the recognition rate of the radar high resolution range pro?le (HRRP). Moreover, different from the free space, the HRRP in half space is more complex. In order to get closer to the real situation, the half space HRRP is simulated as the dataset. The recognition rate has a growth more than 7% compared with the traditional CAE or denoised sparse autoencoder (DSAE). For infrared sensor, a convolutional neural network (CNN) is used for infrared image recognition. Finally, we combine the two results with the Dempster-Shafer (D-S) evidence theory, and the discounting operation is introduced in the fusion to improve the recognition rate. The recognition rate after fusion has a growth more than 7% compared with a single sensor. After the discounting operation, the accuracy rate has been improved by 1.5%, which validates the effectiveness of the proposed method.

The perception module of advanced driver assistance systems plays a vital role. Perception schemes often use a single sensor for data processing and environmental perception or adopt the information processing results of various sensors for the fusion of the detection layer. This paper proposes a multi-scale and multi-sensor data fusion strategy in the front end of perception and accomplishes a multi-sensor function disparity map generation scheme. A binocular stereo vision sensor composed of two cameras and a light deterction and ranging (LiDAR) sensor is used to jointly perceive the environment, and a multi-scale fusion scheme is employed to improve the accuracy of the disparity map. This solution not only has the advantages of dense perception of binocular stereo vision sensors but also considers the perception accuracy of LiDAR sensors. Experiments demonstrate that the multi-scale multi-sensor scheme proposed in this paper significantly improves disparity map estimation.

In this paper, an optimization model is proposed to simulate and predict the current situation of smog. The model takes the interval grey number sequence with the known possibility function as the original data, and constructs a time-delay nonlinear multivariable grey model MGM $(1,m|\tau ,\gamma )$ based on the new kernel and degree of greyness sequences considering its time-delay and nonlinearity. The time-delay parameter is determined by the maximum value of the grey time-delay absolute correlation degree, and the nonlinear parameter is determined by the minimum value of average relative error. In order to verify the feasibility of the model, this paper uses the smog related data of Nanjing city for simulation and prediction. Compared with the other four models, the new model has higher simulation and prediction accuracy.

A system of systems (SoS) composes a set of independent constituent systems (CSs), where the degree of authority to control the independence of CSs varies, depending on different SoS types. Key researchers describe four SoS types with descending levels of central authority: directed, acknowledged, collaborative and virtual. Although the definitions have been recognized in SoS engineering, what is challenging is the difficulty of translating these definitions into models and simulation environments. Thus, we provide a goal-based method including a mathematical baseline to translate these definitions into more effective agent-based modeling and simulations. First, we construct the theoretical models of CS and SoS. Based on the theoretical models, we analyze the degree of authority influenced by SoS characteristics. Next, we propose a definition of SoS types by quantitatively explaining the degree of authority. Finally, we recognize the differences between acknowledged SoS and collaborative SoS using a migrating waterfowl flock by an agent-based model (ABM) simulation. This paper contributes to the SoS body of knowledge by increasing our understanding of the degree of authority in an SoS, so we may identify suitable SoS types to achieve SoS goals by modeling and simulation.

The existing recognition algorithms of space-time block code (STBC) for multi-antenna (MA) orthogonal frequency-division multiplexing (OFDM) systems use feature extraction and hypothesis testing to identify the signal types in a complex communication environment. However, owing to the restrictions on the prior information and channel conditions, these existing algorithms cannot perform well under strong interference and non-cooperative communication conditions. To overcome these defects, this study introduces deep learning into the STBC-OFDM signal recognition field and proposes a recognition method based on the fourth-order lag moment spectrum (FOLMS) and attention-guided multi-scale dilated convolution network (AMDCNet). The fourth-order lag moment vectors of the received signals are calculated, and vectors are stitched to form two-dimensional FOLMS, which is used as the input of the deep learning-based model. Then, the multi-scale dilated convolution is used to extract the details of images at different scales, and a convolutional block attention module (CBAM) is introduced to construct the attention-guided multi-scale dilated convolution module (AMDCM) to make the network be more focused on the target area and obtian the multi-scale guided features. Finally, the concatenate fusion, residual block and fully-connected layers are applied to acquire the STBC-OFDM signal types. Simulation experiments show that the average recognition probability of the proposed method at ?12 dB is higher than 98%. Compared with the existing algorithms, the recognition performance of the proposed method is significantly improved and has good adaptability to environments with strong disturbances. In addition, the proposed deep learning-based model can directly identify the pre-processed FOLMS samples without a priori information on channel and noise, which is more suitable for non-cooperative communication systems than the existing algorithms.

Deep neural networks (DNNs) have achieved great success in many data processing applications. However, high computational complexity and storage cost make deep learning difficult to be used on resource-constrained devices, and it is not environmental-friendly with much power cost. In this paper, we focus on low-rank optimization for efficient deep learning techniques. In the space domain, DNNs are compressed by low rank approximation of the network parameters, which directly reduces the storage requirement with a smaller number of network parameters. In the time domain, the network parameters can be trained in a few subspaces, which enables efficient training for fast convergence. The model compression in the spatial domain is summarized into three categories as pre-train, pre-set, and compression-aware methods, respectively. With a series of integrable techniques discussed, such as sparse pruning, quantization, and entropy coding, we can ensemble them in an integration framework with lower computational complexity and storage. In addition to summary of recent technical advances, we have two findings for motivating future works. One is that the effective rank, derived from the Shannon entropy of the normalized singular values, outperforms other conventional sparse measures such as the $ \ell_1 $ norm for network compression. The other is a spatial and temporal balance for tensorized neural networks. For accelerating the training of tensorized neural networks, it is crucial to leverage redundancy for both model compression and subspace training.

Rich semantic information in natural language increases team efficiency in human collaboration, reduces dependence on high precision data information, and improves adaptability to dynamic environment. We propose a semantic centered cloud control framework for cooperative multi-unmanned ground vehicle (UGV) system. Firstly, semantic modeling of task and environment is implemented by ontology to build a unified conceptual architecture, and secondly, a scene semantic information extraction method combining deep learning and semantic web rule language (SWRL) rules is used to realize the scene understanding and task-level cloud task cooperation. Finally, simulation results show that the framework is a feasible way to enable autonomous unmanned systems to conduct cooperative tasks.

High-precision filtering estimation is one of the key techniques for strapdown inertial navigation system/global navigation satellite system (SINS/GNSS) integrated navigation system, and its estimation plays an important role in the performance evaluation of the navigation system. Traditional filter estimation methods usually assume that the measurement noise conforms to the Gaussian distribution, without considering the influence of the pollution introduced by the GNSS signal, which is susceptible to external interference. To address this problem, a high-precision filter estimation method using Gaussian process regression (GPR) is proposed to enhance the prediction and estimation capability of the unscented quaternion estimator (USQUE) to improve the navigation accuracy. Based on the advantage of the GPR machine learning function, the estimation performance of the sliding window for model training is measured. This method estimates the output of the observation information source through the measurement window and realizes the robust measurement update of the filter. The combination of GPR and the USQUE algorithm establishes a robust mechanism framework, which enhances the robustness and stability of traditional methods. The results of the trajectory simulation experiment and SINS/GNSS car-mounted tests indicate that the strategy has strong robustness and high estimation accuracy, which demonstrates the effectiveness of the proposed method.

Aiming at the suppression of enemy air defense (SEAD) task under the complex and complicated combat scenario, the spatiotemporal cooperative path planning methods are studied in this paper. The major research contents include optimal path points generation, path smoothing and cooperative rendezvous. In the path points generation part, the path points availability testing algorithm and the path segments availability testing algorithm are designed, on this foundation, the swarm intelligence-based path point generation algorithm is utilized to generate the optimal path. In the path smoothing part, taking terminal attack angle constraint and maneuverability constraint into consideration, the Dubins curve is introduced to smooth the path segments. In cooperative rendezvous part, we take estimated time of arrival requirement constraint and flight speed range constraint into consideration, the speed control strategy and flight path control strategy are introduced, further, the decoupling scheme of the circling maneuver and detouring maneuver is designed, in this case, the maneuver ways, maneuver point, maneuver times, maneuver path and flight speed are determined. Finally, the simulation experiments are conducted and the acquired results reveal that the time-space cooperation of multiple unmanned aeriel vehicles (UAVs) is effectively realized, in this way, the combat situation suppression against the enemy can be realized in SEAD scenarios.