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.

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.

A low-Earth-orbit (LEO) satellite network can provide full-coverage access services worldwide and is an essential candidate for future 6G networking. However, the large variability of the geographic distribution of the Earth’s population leads to an uneven service volume distribution of access service. Moreover, the limitations on the resources of satellites are far from being able to serve the traffic in hotspot areas. To enhance the forwarding capability of satellite networks, we first assess how hotspot areas under different load cases and spatial scales significantly affect the network throughput of an LEO satellite network overall. Then, we propose a multi-region cooperative traffic scheduling algorithm. The algorithm migrates low-grade traffic from hotspot areas to coldspot areas for forwarding, significantly increasing the overall throughput of the satellite network while sacrificing some latency of end-to-end forwarding. This algorithm can utilize all the global satellite resources and improve the utilization of network resources. We model the cooperative multi-region scheduling of large-scale LEO satellites. Based on the model, we build a system testbed using OMNET++ to compare the proposed method with existing techniques. The simulations show that our proposed method can reduce the packet loss probability by 30% and improve the resource utilization ratio by 3.69%.

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.

The application scope of the forward scatter radar (FSR) based on the Global Navigation Satellite System (GNSS) can be expanded by improving the detection capability. Firstly, the forward-scatter signal model when the target crosses the baseline is constructed. Then, the detection method of the forward-scatter signal based on the Rényi entropy of time-frequency distribution is proposed and the detection performance with different time-frequency distributions is compared. Simulation results show that the method based on the smooth pseudo Wigner-Ville distribution (SPWVD) can achieve the best performance. Next, combined with the geometry of FSR, the influence on detection performance of the relative distance between the target and the baseline is analyzed. Finally, the proposed method is validated by the anechoic chamber measurements and the results show that the detection ability has a 10 dB improvement compared with the common constant false alarm rate (CFAR) detection.

The quality of synthetic aperture radar (SAR) image degrades in the case of multiple imaging projection planes (IPPs) and multiple overlapping ship targets, and then the performance of target classification and recognition can be influenced. For addressing this issue, a method for extracting ship targets with overlaps via the expectation maximization (EM) algorithm is proposed. First, the scatterers of ship targets are obtained via the target detection technique. Then, the EM algorithm is applied to extract the scatterers of a single ship target with a single IPP. Afterwards, a novel image amplitude estimation approach is proposed, with which the radar image of a single target with a single IPP can be generated. The proposed method can accomplish IPP selection and targets separation in the image domain, which can improve the image quality and reserve the target information most possibly. Results of simulated and real measured data demonstrate the effectiveness of the proposed method.

For bistatic multiple-input multiple-output (MIMO) radar, this paper presents a robust and direction finding method in strong impulse noise environment. By means of a new lower order covariance, the method is effective in suppressing impulse noise and achieving superior direction finding performance using the maximum likelihood (ML) estimation method. A quantum equilibrium optimizer algorithm (QEOA) is devised to resolve the corresponding objective function for efficient and accurate direction finding. The results of simulation reveal the capability of the presented method in success rate and root mean square error over existing direction-finding methods in different application situations, e.g., locating coherent signal sources with very few snapshots in strong impulse noise. Other than that, the Cramér-Rao bound (CRB) under impulse noise environment has been drawn to test the capability of the presented method.

Overlooking the issue of false alarm suppression in heterogeneous change detection leads to inferior detection performance. This paper proposes a method to handle false alarms in heterogeneous change detection. A lightweight network of two channels is bulit based on the combination of convolutional neural network (CNN) and graph convolutional network (GCN). CNNs learn feature difference maps of multitemporal images, and attention modules adaptively fuse CNN-based and graph-based features for different scales. GCNs with a new kernel filter adaptively distinguish between nodes with the same and those with different labels, generating change maps. Experimental evaluation on two datasets validates the efficacy of the proposed method in addressing false alarms.

With the continuous development of network functions virtualization (NFV) and software-defined networking (SDN) technologies and the explosive growth of network traffic, the requirement for computing resources in the network has risen sharply. Due to the high cost of edge computing resources, coordinating the cloud and edge computing resources to improve the utilization efficiency of edge computing resources is still a considerable challenge. In this paper, we focus on optimizing the placement of network services in cloud-edge environments to maximize the efficiency. It is first proved that, in cloud-edge environments, placing one service function chain (SFC) integrally in the cloud or at the edge can improve the utilization efficiency of edge resources. Then a virtual network function (VNF) performance-resource (P-R) function is proposed to represent the relationship between the VNF instance computing performance and the allocated computing resource. To select the SFCs that are most suitable to deploy at the edge, a VNF placement and resource allocation model is built to configure each VNF with its particular P-R function. Moreover, a heuristic recursive algorithm is designed called the recursive algorithm for max edge throughput (RMET) to solve the model. Through simulations on two scenarios, it is verified that RMET can improve the utilization efficiency of edge computing resources.

To maintain the stability of the inter-satellite link for gravitational wave detection, an intelligent learning monitoring and fast warning method of the inter-satellite link control system failure is proposed. Different from the traditional fault diagnosis optimization algorithms, the fault intelligent learning method proposed in this paper is able to quickly identify the faults of inter-satellite link control system despite the existence of strong coupling nonlinearity. By constructing a two-layer learning network, the method enables efficient joint diagnosis of fault areas and fault parameters. The simulation results show that the average identification time of the system fault area and fault parameters is 0.27 s, and the fault diagnosis efficiency is improved by 99.8% compared with the traditional algorithm.

As the unmanned weap system-of systems (UWSoS) becomes complex, the inevitable uncertain interference gradually increases, which leads to a strong emphasis on the resilience of UWSoS. Hence, this paper presents a resilience-driven cooperative reconfiguration strategy to enhance the resilience of UWSoS. First, a unified resilience-driven cooperative reconfiguration strategy framework is designed to guide the UWSoS resilience enhancement. Subsequently, a cooperative reconfiguration strategy algorithm is proposed to identify the optimal cooperative reconfiguration sequence, combining the cooperative pair resilience contribution index (CPRCI) and cooperative pair importance index (CPII). At last, the effectiveness and superiority of the proposed algorithm are demonstrated through various attack scenario simulations that include different attack modes and intensities. The analysis results can provide a reference for decision-makers to manage UWSoS.

Today’s air combat has reached a high level of uncertainty where continuous or discrete variables with crisp values cannot be properly represented using fuzzy sets. With a set of membership functions, fuzzy logic is well-suited to tackle such complex states and actions. However, it is not necessary to fuzzify the variables that have definite discrete semantics. Hence, the aim of this study is to improve the level of model abstraction by proposing multiple levels of cascaded hierarchical structures from the perspective of function, namely, the functional decision tree. This method is developed to represent behavioral modeling of air combat systems, and its metamodel, execution mechanism, and code generation can provide a sound basis for function-based behavioral modeling. As a proof of concept, an air combat simulation is developed to validate this method and the results show that the fighter Alpha built using the proposed framework provides better performance than that using default scripts.

To solve the problem that the existing situation awareness research focuses on multi-sensor data fusion, but the expert knowledge is not fully utilized, a heterogeneous information fusion recognition method based on belief rule structure is proposed. By defining the continuous probabilistic hesitation fuzzy linguistic term sets (CPHFLTS) and establishing CPHFLTS distance measure, the belief rule base of the relationship between feature space and category space is constructed through information integration, and the evidence reasoning of the input samples is carried out. The experimental results show that the proposed method can make full use of sensor data and expert knowledge for recognition. Compared with the other methods, the proposed method has a higher correct recognition rate under different noise levels.

Evolutionary algorithms (EAs) have been used in high utility itemset mining (HUIM) to address the problem of discovering high utility itemsets (HUIs) in the exponential search space. EAs have good running and mining performance, but they still require huge computational resource and may miss many HUIs. Due to the good combination of EA and graphics processing unit (GPU), we propose a parallel genetic algorithm (GA) based on the platform of GPU for mining HUIM (PHUI-GA). The evolution steps with improvements are performed in central processing unit (CPU) and the CPU intensive steps are sent to GPU to evaluate with multi-threaded processors. Experiments show that the mining performance of PHUI-GA outperforms the existing EAs. When mining 90% HUIs, the PHUI-GA is up to 188 times better than the existing EAs and up to 36 times better than the CPU parallel approach.

Survivability is used to evaluate the ability of the satellite to complete the mission after failure, while the duration of maintaining performance is often ignored. An effective backup strategy can restore the constellation performance timely, and maintain good network communication performance in case of satellite failure. From the perspective of network utility, the low Earth orbit (LEO) satellite constellation survivable graphical evaluation and review technology (GERT) network with backup satellites is constructed. A network utility transfer function algorithm based on moment generating function and Mason formula is proposed, the network survivability evaluation models of on-orbit backup strategy and ground backup strategy are established. The survivable GERT model can deduce the expected maintenance time of LEO satellite constellation under different fault states and the network utility generated during the state maintenance period. The case analysis shows that the proposed survivable GERT model can consider the satellite failure rate, backup satellite replacement rate, maneuver control replacement ability and life requirement, and effectively determine the optimal survivable backup strategy for LEO satellite constellation with limited resources according to the expected network utility.

With the development of positioning technology, location services are constantly in demand by people. As a primary location service pedestrian navigation has two main approaches based on radio and inertial navigation. The pedestrian navigation based on radio is subject to environmental occlusion leading to the degradation of positioning accuracy. The pedestrian navigation based on micro-electro-mechanical system inertial measurement unit (MIMU) is less susceptible to environmental interference, but its errors dissipate over time. In this paper, a chest card pedestrian navigation improvement method based on complementary correction is proposed in order to suppress the error divergence of inertial navigation methods. To suppress attitude errors, optimal feedback coefficients are established by pedestrian motion characteristics. To extend navigation time and improve positioning accuracy, the step length in subsequent movements is compensated by the first step length. The experimental results show that the positioning accuracy of the proposed method is improved by more than 47% and 44% compared with the pure inertia-based method combined with step compensation and the traditional complementary filtering combined method with step compensation. The proposed method can effectively suppress the error dispersion and improve the positioning accuracy.

To investigate the real-time mean orbital elements (MOEs) estimation problem under the influence of state jumping caused by non-fatal spacecraft collision or protective orbit transfer, a modified augmented square-root unscented Kalman filter (MASUKF) is proposed. The MASUKF is composed of sigma points calculation, time update, modified state jumping detection, and measurement update. Compared with the filters used in the existing literature on MOEs estimation, it has three main characteristics. Firstly, the state vector is augmented from six to nine by the added thrust acceleration terms, which makes the filter additionally give the state-jumping-thrust-acceleration estimation. Secondly, the normalized innovation is used for state jumping detection to set detection threshold concisely and make the filter detect various state jumping with low latency. Thirdly, when sate jumping is detected, the covariance matrix inflation will be done, and then an extra time update process will be conducted at this time instance before measurement update. In this way, the relatively large estimation error at the detection moment can significantly decrease. Finally, typical simulations are performed to illustrated the effectiveness of the method.

The industrial Internet of Things (IIoT) is a new industrial idea that combines the latest information and communication technologies with the industrial economy. In this paper, a cloud control structure is designed for IIoT in cloud-edge environment with three modes of 5G. For 5G based IIoT, the time sensitive network (TSN) service is introduced in transmission network. A 5G logical TSN bridge is designed to transport TSN streams over 5G framework to achieve end-to-end configuration. For a transmission control protocol (TCP) model with nonlinear disturbance, time delay and uncertainties, a robust adaptive fuzzy sliding mode controller (AFSMC) is given with control rule parameters. IIoT workflows are made up of a series of subtasks that are linked by the dependencies between sensor datasets and task flows. IIoT workflow scheduling is a non-deterministic polynomial (NP)-hard problem in cloud-edge environment. An adaptive and non-local-convergent particle swarm optimization (ANCPSO) is designed with nonlinear inertia weight to avoid falling into local optimum, which can reduce the makespan and cost dramatically. Simulation and experiments demonstrate that ANCPSO has better performances than other classical algorithms.

The observation error model of the underwater acoustic positioning system is an important factor to influence the positioning accuracy of the underwater target. For the position inconsistency error caused by considering the underwater target as a mass point, as well as the observation system error, the traditional error model best estimation trajectory (EMBET) with little observed data and too many parameters can lead to the ill-condition of the parameter model. In this paper, a multi-station fusion system error model based on the optimal polynomial constraint is constructed, and the corresponding observation system error identification based on improved spectral clustering is designed. Firstly, the reduced parameter unified modeling for the underwater target position parameters and the system error is achieved through the polynomial optimization. Then a multi-station non-oriented graph network is established, which can address the problem of the inaccurate identification for the system errors. Moreover, the similarity matrix of the spectral clustering is improved, and the iterative identification for the system errors based on the improved spectral clustering is proposed. Finally, the comprehensive measured data of long baseline lake test and sea test show that the proposed method can accurately identify the system errors, and moreover can improve the positioning accuracy for the underwater target positioning.

Missile interception problem can be regarded as a two-person zero-sum differential games problem, which depends on the solution of Hamilton-Jacobi-Isaacs (HJI) equation. It has been proved impossible to obtain a closed-form solution due to the nonlinearity of HJI equation, and many iterative algorithms are proposed to solve the HJI equation. Simultaneous policy updating algorithm (SPUA) is an effective algorithm for solving HJI equation, but it is an on-policy integral reinforcement learning (IRL). For online implementation of SPUA, the disturbance signals need to be adjustable, which is unrealistic. In this paper, an off-policy IRL algorithm based on SPUA is proposed without making use of any knowledge of the systems dynamics. Then, a neural-network based online adaptive critic implementation scheme of the off-policy IRL algorithm is presented. Based on the online off-policy IRL method, a computational intelligence interception guidance (CIIG) law is developed for intercepting high-maneuvering target. As a model-free method, intercepting targets can be achieved through measuring system data online. The effectiveness of the CIIG is verified through two missile and target engagement scenarios.

The laser-guided bomb (LGB) is an air-to-ground precision-guided weapon that offers high hit rates, great power, and ease of use. LGBs are guided by semi-active laser ground-seeking technology, which means that atmospheric conditions can affect their accuracy. The spatial release region (SRR) of LGBs is difficult to calculate precisely, especially when there is a poor field of view. This can result in a lower real hit probability. To increase the hit probability of LGBs in tough atmospheric situations, a novel method for calculating the SRR has been proposed. This method is based on the transmittance model of the 1.06 μm laser in atmospheric species and the laser diffuse reflection model of the target surface to determine the capture target time of the laser seeker. Then, it calculates the boundary ballistic space starting position by ballistic model and gets the spatial scope of the spatial release region. This method can determine the release region of LGBs based on flight test data such as instantaneous velocity, altitude, off-axis angle, and atmospheric visibility. By more effectively employing aircraft release conditions, atmospheric visibility and other factors, the SRR calculation method can improve LGB hit probability by 9.2%.