Path-Following Control With Obstacle Avoidance of Autonomous Surface Vehicles Subject to Actuator Faults

This paper investigates the path-following control problem with obstacle avoidance of autonomous surface vehicles in the presence of actuator faults,uncertainty and external disturbances.Autonomous surface vehicles inevitably suffer from actuator faults in complex sea environments,which may cause existing obstacle avoidance strategies to fail.To reduce the influence of actuator faults,an improved artificial potential function is constructed by introducing the lower bound of actuator efficiency factors.The nonlinear state observer,which only depends on measurable position information of the autonomous surface vehicle,is used to address uncertainties and external disturbances.By using a backstepping technique and adaptive mechanism,a path-following control strategy with obstacle avoidance and fault tolerance is designed which can ensure that the tracking errors converge to a small neighborhood of zero.Compared with existing results,the proposed control strategy has the capability of obstacle avoidance and fault tolerance simultaneously.Finally,the comparison results through simulations are given to verify the effectiveness of the proposed method.

Structural features in the mid-southern section of the Kyushu–Palau Ridge based on satellite altimetry gravity anomaly

The Kyushu–Palau Ridge(KPR),an anti-S-shaped submarine highland at the center of the Philippine Sea Plate(PSP),is considered the residual arc of the Izu–Bonin–Mariana Island Arc,which retains key information about the cessation of the Western Philippine Basin(WPB)expansion and the Parece Vela Basin(PVB)breakup.Herein,using the new generation of satellite altimetry gravity data,high-precision seafloor topography data,and newly acquired ship-borne gravity data,the topographic and gravity characteristics of the KPR mid-southern section and adjacent region are depicted.The distribution characteristics of the faults were delineated using the normalized vertical derivative–total horizontal derivative method(NVDR-THDR)and the minimum curvature potential field separation method.The Moho depth and crustal thickness were inverted using the rapid inversion method for a double-interface model with depth constraints.Based on these results,the crust structure features in the KPR mid-southern section,and the“triangular”structure geological significance where the KPR and Central Basin Rift(CBR)of the WPB intersect are interpreted.The KPR crustal thickness is approximately 6–16 km,with a distinct discontinuity that is slightly thicker than the normal oceanic crust.The KPR mid-southern section crust structure was divided into four segments(S1–S4)from north to south,formed by the CBR eastward extension joint action and clockwise rotation of the PVB expansion axis and the Mindanao fault zone blocking effect.

An Intelligent Framework for Resilience Recovery of FANETs with Spatio-Temporal Aggregation and Multi-Head Attention Mechanism

Due to the time-varying topology and possible disturbances in a conflict environment,it is still challenging to maintain the mission performance of flying Ad hoc networks(FANET),which limits the application of Unmanned Aerial Vehicle(UAV)swarms in harsh environments.This paper proposes an intelligent framework to quickly recover the cooperative coveragemission by aggregating the historical spatio-temporal network with the attention mechanism.The mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model.A spatio-temporal node pooling method is proposed to ensure all node location features can be updated after destruction by capturing the temporal network structure.Combined with the corresponding Laplacian matrix as the hyperparameter,a recovery algorithm based on the multi-head attention graph network is designed to achieve rapid recovery.Simulation results showed that the proposed framework can facilitate rapid recovery of the connectivity and coverage more effectively compared to the existing studies.The results demonstrate that the average connectivity and coverage results is improved by 17.92%and 16.96%,respectively compared with the state-of-the-art model.Furthermore,by the ablation study,the contributions of each different improvement are compared.The proposed model can be used to support resilient network design for real-time mission execution.

A Parameterization Scheme for Wind Wave Modules that Includes the Sea Ice Thickness in the Marginal Ice Zone

The global wave model WAVEWATCH III®works well in open water.To simulate the propagation and attenuation of waves through ice-covered water,existing simulations have considered the influence of sea ice by adding the sea ice concentration in the wind wave module;however,they simply suppose that the wind cannot penetrate the ice layer and ignore the possibility of wind forcing waves below the ice cover.To improve the simulation performance of wind wave modules in the marginal ice zone(MIZ),this study proposes a parameterization scheme by directly including the sea ice thickness.Instead of scaling the wind input with the fraction of open water,this new scheme allows partial wind input in ice-covered areas based on the ice thickness.Compared with observations in the Barents Sea in 2016,the new scheme appears to improve the modeled waves in the high-frequency band.Sensitivity experiments with and without wind wave modules show that wind waves can play an important role in areas with low sea ice concentration in the MIZ.

K-DSA for the multiple traveling salesman problem

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

Target tracking method of Siamese networks based on the broad learning system

Target tracking has a wide range of applications in intelligent transportation,real‐time monitoring,human‐computer interaction and other aspects.However,in the tracking process,the target is prone to deformation,occlusion,loss,scale variation,background clutter,illumination variation,etc.,which bring great challenges to realize accurate and real‐time tracking.Tracking based on Siamese networks promotes the application of deep learning in the field of target tracking,ensuring both accuracy and real‐time performance.However,due to its offline training,it is difficult to deal with the fast motion,serious occlusion,loss and deformation of the target during tracking.Therefore,it is very helpful to improve the performance of the Siamese networks by learning new features of the target quickly and updating the target position in time online.The broad learning system(BLS)has a simple network structure,high learning efficiency,and strong feature learning ability.Aiming at the problems of Siamese networks and the characteristics of BLS,a target tracking method based on BLS is proposed.The method combines offline training with fast online learning of new features,which not only adopts the powerful feature representation ability of deep learning,but also skillfully uses the BLS for re‐learning and re‐detection.The broad re‐learning information is used for re‐detection when the target tracking appears serious occlusion and so on,so as to change the selection of the Siamese networks search area,solve the problem that the search range cannot meet the fast motion of the target,and improve the adaptability.Experimental results show that the proposed method achieves good results on three challenging datasets and improves the performance of the basic algorithm in difficult scenarios.

Formation and adjustment of manned/unmanned combat aerial vehicle cooperative engagement system

Manned combat aerial vehicles （MCAVs）, and un-manned combat aerial vehicles （UCAVs） together form a cooper-ative engagement system to carry out operational mission, whichwill be a new air engagement style in the near future. On the basisof analyzing the structure of the MCAV/UCAV cooperative engage-ment system, this paper divides the unique system into three hi-erarchical levels, respectively, i.e., mission level, task-cluster leveland task level. To solve the formation and adjustment problem ofthe latter two levels, three corresponding mathematical modelsare established. To solve these models, three algorithms calledquantum artificial bee colony （QABC） algorithm, greedy strategy（GS） and two-stage greedy strategy （TSGS） are proposed. Finally,a series of simulation experiments are designed to verify the effec-tiveness and superiority of the proposed algorithms.

Robust range-parameterized cubature Kalman filter for bearings-only tracking

In order to improve tracking accuracy when initial estimate is inaccurate or outliers exist,a bearings-only tracking approach called the robust range-parameterized cubature Kalman filter(RRPCKF)was proposed.Firstly,the robust extremal rule based on the pollution distribution was introduced to the cubature Kalman filter(CKF)framework.The improved Turkey weight function was subsequently constructed to identify the outliers whose weights were reduced by establishing equivalent innovation covariance matrix in the CKF.Furthermore,the improved range-parameterize(RP)strategy which divides the filter into some weighted robust CKFs each with a different initial estimate was utilized to solve the fuzzy initial estimation problem efficiently.Simulations show that the result of the RRPCKF is more accurate and more robust whether outliers exist or not,whereas that of the conventional algorithms becomes distorted seriously when outliers appear.

MAV/UAV task coalition phased-formation method

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.

Intervention decision-making in MAV/UAV cooperative engagement based on human factors engineering

Aiming at the intervention decision-making problem in manned/unmanned aerial vehicle（MAV/UAV） cooperative engagement, this paper carries out a research on allocation strategy of emergency discretion based on human factors engineering（HFE）.Firstly, based on the brief review of research status of HFE, it gives structural description to emergency in the process of cooperative engagement and analyzes intervention of commanders. After that,constraint conditions of intervention decision-making of commanders based on HFE（IDMCBHFE） are given, and the mathematical model, which takes the overall efficiency value of handling emergencies as the objective function, is established. Then, through combining K-best and variable neighborhood search（VNS） algorithm, a K-best optimization variable neighborhood search mixed algorithm（KBOVNSMA） is designed to solve the model. Finally,through three groups of simulation experiments, effectiveness and superiority of the proposed algorithm are verified.

WFRFT modulation recognition based on HOC and optimal order searching algorithm

A hybrid carrier（HC） scheme based on weighted-type fractional Fourier transform（WFRFT） has been proposed recently.While most of the works focus on HC scheme＇s inherent characteristics, little attention is paid to the WFRFT modulation recognition.In this paper, a new theory is provided to recognize the WFRFT modulation based on higher order cumulants（HOC）. First, it is deduced that the optimal WFRFT received order can be obtained through the minimization of 4 th-order cumulants, C_（42）. Then, a combinatorial searching algorithm is designed to minimize C_（42）.Finally, simulation results show that the designed scheme has a high recognition rate and the combinatorial searching algorithm is effective and reliable.

Distributed tasks-platforms scheduling method to holonic-C2 organization

To solve the problem of distributed tasks-platforms scheduling in holonic command and control(C2) organization,the basic elements of the organization are analyzed firstly and the formal description of organizational elements and structure is provided. Based on the improvement of task execution quality,a single task resource scheduling model is established and the solving method based on the m-best algorithm is proposed. For the problem of tactical decision-holon cannot handle tasks with low priority effectively, a distributed resource scheduling collaboration mechanism based on platform pricing and a platform exchange mechanism based on resource capacities are designed. Finally,a series of experiments are designed to prove the effectiveness of these methods. The results show that the proposed distributed scheduling methods can realize the effective balance of platform resources.

Self-adapting radiation control method for RFS in tracking

The aim of this paper is to achieve the radio frequency stealth(RFS) during the course of tracking by controlling the radiation energy and the interval of a radar. Firstly, we build the model of probability of interception with the once radiation during the course of tracking. Secondly, we establish the model of the cumulative probability of interception to describe the effect of RFS throughout the tracking process and obtain two solutions that are minimizing the probability of interception and the radiation times to reduce the cumulative probability of interception. Thirdly, we propose a self-adapting radiation energy control method(SARE)to minimize the probability of interception. Fourthly, we propose a self-adapting radiation interval control method(SARI) to minimize radiation times. Fifthly, combining SARE with SARI, we propose a SARE-SARI control method(SAEI) during the course of tracking.Finally, we compare SAEI with two others by simulation, and the results show the effect of RFS of SAEI is better than the other two,but we have to make a trade-off between the ability of RFS and the effect of tracking.

A precise tidal prediction mechanism based on the combination of harmonic analysis and adaptive network-based fuzzy inference system model

An efficient and accurate prediction of a precise tidal level in estuaries and coastal areas is indispensable for the management and decision-making of human activity in the field wok of marine engineering. The variation of the tidal level is a time-varying process. The time-varying factors including interference from the external environment that cause the change of tides are fairly complicated. Furthermore, tidal variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. Consequently, for the efficient and precise tidal level prediction, a neuro-fuzzy hybrid technology based on the combination of harmonic analysis and adaptive network-based fuzzy inference system（ANFIS）model is utilized to construct a precise tidal level prediction system, which takes both advantages of the harmonic analysis method and the ANFIS network. The proposed prediction model is composed of two modules： the astronomical tide module caused by celestial bodies’ movement and the non-astronomical tide module caused by various meteorological and other environmental factors. To generate a fuzzy inference system（FIS） structure,three approaches which include grid partition（GP）, fuzzy c-means（FCM） and sub-clustering（SC） are used in the ANFIS network constructing process. Furthermore, to obtain the optimal ANFIS based prediction model, large numbers of simulation experiments are implemented for each FIS generating approach. In this tidal prediction study, the optimal ANFIS model is used to predict the non-astronomical tide module, while the conventional harmonic analysis model is used to predict the astronomical tide module. The final prediction result is performed by combining the estimation outputs of the harmonious analysis model and the optimal ANFIS model. To demonstrate the applicability and capability of the proposed novel prediction model, measured tidal level samples of Fort Pulaski tidal station are selected as the testing database. Simulation and experimental results confirm that the proposed prediction approach can achieve precise predictions for the tidal level with high accuracy, satisfactory convergence and stability.

Robust TDOA/FDOA estimation from emitter signals for hybrid localization using UAVs

This paper considers the time difference of arrival(TDOA)and frequency difference of arrival(FDOA)estimation problem for joint localization using unmanned aerial vehicles(UAVs),involving range migration(RM)and Doppler ambiguity within observation interval.A robust estimation method based on interpolation and resampling is proposed.Specifically,the interpolation artificially increases the pulse repetition frequency(PRF).After that,the resampling eliminates the coupling between range frequency and slow time.Finally,a coherent integration step based on inverse discrete Fourier transform(IDFT)is used to achieve parameter estimation and suppress the grating lobes caused by interpolation.The proposed method could be efficiently implemented by fast Fourier transform(FFT),inverse FFT(IFFT)and non-uniform FFT(NUFFT)without parameter searching procedures.Numerical experiments indicate that the proposed method has nearly optimal anti-noise performance but much lower computational complexity than the maximum likelihood estimator,which makes it more competitive in practical applications.

Spatial-temporal variations in net primary productivity in the Arctic from 2003 to 2016

The area of Arctic sea ice has dramatically decreased, and the length of the open water season has increased;these patterns have been observed by satellite remote sensing since the 1970 s. In this paper, we calculate the net primary productivity(NPP, calculated by carbon) from 2003 to 2016 based on sea ice concentration products,chlorophyll a(Chl a) concentration, photosynthetically active radiation(PAR), sea surface temperature(SST), and sunshine duration data. We then analyse the spatiotemporal changes in the Chl a concentration and NPP and further investigate the relations among NPP, the open water area, and the length of the open water season. The results indicate that(1) the Chl a concentration increased by 0.025 mg/m^3 per year;(2) the NPP increased by 4.29 mg/(m^2·d) per year, reaching a maximum of 525.74 mg/(m^2·d) in 2016;and(3) the Arctic open water area increased by 57.23×10^3 km^2/a, with a growth rate of 1.53 d/a for the length of the open water season. The annual NPP was significantly positively related to the open water area, the length of the open water season and the SST.The daily NPP was also found to have a lag correlation with the open water area, with a lag time of two months.With global warming, NPP has maintained an increasing trend, with the most significant increase occurring in the Kara Sea. In summary, this study provides a macroscopic understanding of the distribution of phytoplankton in the Arctic, which is valuable information for the evaluation and management of marine ecological environments.

Ultra-wideband reflective polarization converter based on anisotropic metasurface

In this paper, we propose an ultra-wideband reflective linear cross-polarization converter based on anisotropic metasurface. Its unit cell is composed of a square-shaped resonator with intersectant diagonal and metallic ground sheet separated by dielectric substrate. Simulated results show that the converter can generate resonances at four frequencies under normal incident electromagnetic（EM） wave, leading to the bandwidth expansion of cross-polarization reflection. For verification,the designed polarization converter is fabricated and measured. The measured and simulated results agree well with each other, showing that the fabricated converter can convert x- or y-polarized incident wave into its cross polarized wave in a frequency range from 7.57 GHz to 20.46 GHz with a relative bandwidth of 91.2%, and the polarization conversion efficiency is greater than 90%. The proposed polarization converter has a simple geometry but an ultra wideband compared with the published designs, and hence possesses potential applications in novel polarization-control devices.

Multi-Innovation Gradient Iterative Locally Weighted Learning Identification for A Nonlinear Ship Maneuvering System

This paper explores a highly accurate identification modeling approach for the ship maneuvering motion with fullscale trial. A multi-innovation gradient iterative(MIGI) approach is proposed to optimize the distance metric of locally weighted learning(LWL), and a novel non-parametric modeling technique is developed for a nonlinear ship maneuvering system. This proposed method’s advantages are as follows: first, it can avoid the unmodeled dynamics and multicollinearity inherent to the conventional parametric model; second, it eliminates the over-learning or underlearning and obtains the optimal distance metric; and third, the MIGI is not sensitive to the initial parameter value and requires less time during the training phase. These advantages result in a highly accurate mathematical modeling technique that can be conveniently implemented in applications. To verify the characteristics of this mathematical model, two examples are used as the model platforms to study the ship maneuvering.

Waveform design for radar and extended target in the environment of electronic warfare

Transmit waveform optimization is critical to radar system performance. There have been a fruit of achievements about waveform design in recent years. However, most of the existing methods are based on the assumption that radar is smart and the target is dumb, which is not always reasonable in the modern electronic warfare. This paper focuses on the waveform design for radar and the extended target in the environment of electronic warfare. Three different countermeasure models between smart radar and dumb target, smart target and dumb radar, smart radar and smart target are proposed. Taking the signal-to-interferenceplus-noise ratio(SINR) as the metric, optimized waveforms for the first two scenarios are achieved by the general water-filling method in the presence of clutter. For the last case, the equilibrium between smart radar and smart target in the presence of clutter is given mathematically and the optimized solution is achieved through a novel two-step water-filling method on the basis of minmax theory. Simulation results under different power constraints show the power allocation strategies of radar and target and the output SINRs are analyzed.

System structural analysis of communication networks based on DEMATEL-ISM and entropy

A method of system structural analysis based on decision making trial and evaluation laboratory together with interpretative structural modeling(DEMATEL-ISM) and entropy is proposed to clarify system structure of communication networks and analyze mutual influencing degree between different networks.Mutual influencing degree and importance degree of elements are both considered to determine weights of elements,and the entropy of expert judgment results is used to omit unimportant influence relation and simplify system structure.Structural analysis on communication networks system shows that the proposed method can quantificationally present weights and mutual influencing degree of elements,and reasonably simplify system structure.The results indicate the rationality and feasibility of the method.