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Multiple Model PHD filter for Tracking Sharply Maneuvering Targets Using Recursive Ransac Based Adaptive Birth Estimation
Changwen DingDi ZhouXinguang ZouRunle DuJiaqi Liu
Keywords:Target trackingAdaptation modelsFiltering algorithmsComputational modelingEstimationMatched filtersLogic gatesAdaptive EstimationRandom Sample ConsensusManeuvering TargetProbability Hypothesis DensityProbability Hypothesis Density FilterDynamic ModelWhite NoisePrior InformationNon-zero MeanTurn RateProbability Density FunctionPerformance AccuracyTransition ProbabilitiesDetection ProbabilityTracking PerformanceMotion ModelTarget StateProcess NoiseTransition Probability MatrixFilter DesignTarget IntensityAugmented StateOriginal FilterTracking ScenariosGaussian ComponentsTarget TrackingTarget MatrixLinear GaussianTransition DensityGaussian Formmultitarget trackingprobability hypothesis density (PHD) filtersharply maneuvering targetsmultiple modeladaptive birth intensity estimation
Abstracts:An algorithm to track multiple sharply maneuvering targets without prior knowledge about new target birth is proposed. These targets are capable of achieving sharp maneuvers within a short period of time, such as drones and agile missiles. The probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment of the full target posterior, has been shown to be a computationally efficient solution to multitarget tracking problems. However, the standard PHD filter operates on the single dynamic model and requires prior information about target birth distribution, which leads to many limitations in terms of practical applications. In this paper, we introduce a nonzero mean, white noise turn rate dynamic model and generalize jump Markov systems to multitarget case to accommodate sharply maneuvering dynamics. Moreover, to adaptively estimate newborn targets' information, a measurement-driven method based on the recursive random sampling consensus (RANSAC) algorithm is proposed. Simulation results demonstrate that the proposed method achieves significant improvement in tracking multiple sharply maneuvering targets with adaptive birth estimation.
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Kinematic Calibration Under the Expectation Maximization Framework for Exoskeletal Inertial Motion Capture System
Weiwei QinWenxin GuoChen HuGang LiuTainian Song
Keywords:Embedded systemsEstimationSystems engineering and theoryMotion captureCalibrationColored noiseExpectation MaximizationMotion CaptureExpectation Maximization FrameworkInertial Motion Capture SystemGeometric ParametersCalibration MethodGyroscopeEmbedded SystemLeast-squaresLog-likelihoodMaximum Likelihood MethodLikelihood FunctionTransformation MatrixObjective ValuePosition ErrorJoint AnglesInertial Measurement UnitGlobal Navigation Satellite SystemKinematic ParametersCalibration ResultsGeometric ErrorsNominal SystemGeometric CalibrationFirst-order ProcessEnd-effector PositionMaximum Likelihood SolutionCartesian SpaceRandom TrajectoriesMixing CoefficientsGeometric Modelhuman motion capturekinematic calibrationexoskeletongyroscopic driftexpectation maximization (EM)
Abstracts:This study presents a kinematic calibration method for exoskeletal inertial motion capture (EI-MoCap) system with considering the random colored noise such as gyroscopic drift. In this method, the geometric parameters are calibrated by the traditional calibration method at first. Then, in order to calibrate the parameters affected by the random colored noise, the expectation maximization (EM) algorithm is introduced. Through the use of geometric parameters calibrated by the traditional calibration method, the iterations under the EM framework are decreased and the efficiency of the proposed method on embedded system is improved. The performance of the proposed kinematic calibration method is compared to the traditional calibration method. Furthermore, the feasibility of the proposed method is verified on the EI-MoCap system. The simulation and experiment demonstrate that the motion capture precision is significantly improved by 16.79% and 7.16% respectively in comparison to the traditional calibration method.
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Contact Detumbling Toward a Nutating Target Through Deformable Effectors and Prescribed Performance Controller
Yue ZangYao ZhangQuan HuMou LiYujun Chen
Keywords:End effectorsRobotsKinematicsRobot kinematicsFingersTrajectoryTarget trackingPrescribed Performance ControlNumerical SimulationsRobotic SystemTracking ErrorComplex OperationsSliding Mode ControlSliding ModeAdaptive Sliding Mode ControlError TransformationControl MethodConvergence RateContact SurfaceAngular VelocityControl PerformanceGreater PerformanceSymmetry AxisRobotic ArmContact ForceOutput ControlMass MatrixTarget RotationContact StrengthEnd Of TipArm JointsImpedance ControlContact ConditionsError BoundsTarget DisplacementContact MethodSolar Panelsnutating targetcontact detumblingdual-arm space robotdeformable end-effectorprescribed performance controller
Abstracts:Detumbling operation toward a rotating target with nutation is meaningful for debris removal but challenging. In this study, a deformable end-effector is first designed based on the requirements for contacting the nutating target. A dual-arm robotic system installed with the deformable end-effectors is modeled and the movement of the end-tips is analyzed. The complex operation of the contact toward a nutating target places strict requirements on control accuracy and controller robustness. Thus, an improvement of the tracking error transformation is proposed and an adaptive sliding mode controller with prescribed performance is designed to guarantee the fast and precise motion of the effector during the contact detumbling. Finally, by employing the proposed effector and the controller, numerical simulations are carried out to verify the effectiveness and efficiency of the contact detumbling toward a nutating target.
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Real-Time Tracking of Fast-Moving Object in Occlusion Scene
Yuran LiYichen LiMonan ZhangWenbin YuXinping Guan
Keywords:TrainingAccuracyTrackingComputational modelingPredictive modelsFeature extractionReal-time systemsFast-moving ObjectsConvolution OperationKalman FilterObject LocationArea Under CurveMotion ModelTracking AccuracyObject TrackingFuture PositionTrack ModelCorrelation FilterConvolutional Neural NetworkGaussian NoiseFeature MapsFrame RateColor FeaturesTranslational MotionDifficulty FindingObjective ConditionsSearch AreaTracking ResultsAdvanced TrackingBackground ClutterVideo SegmentsMotion BlurMeasurement EquationNormal Probability DistributionResponse ScoresBad Situationspeed-accuracy balancedmotion modelingconstrained updater
Abstracts:Tracking the fast-moving object in occlusion situations is an important research topic in computer vision. Despite numerous notable contributions have been made in this field, few of them simultaneously incorporate both object's extrinsic features and intrinsic motion patterns into their methodologies, thereby restricting the potential for tracking accuracy improvement. In this paper, on the basis of efficient convolution operators (ECO) model, a speed-accuracy-balanced model is put forward. This model uses the simple correlation filter to track the object in real-time, and adopts the sophisticated deep-learning neural network to extract high-level features to train a more complex filter correcting the tracking mistakes, when the tracking state is judged to be poor. Furthermore, in the context of scenarios involving regular fast-moving, a motion model based on Kalman filter is designed which greatly promotes the tracking stability, because this motion model could predict the object's future location from its previous movement pattern. Additionally, instead of periodically updating our tracking model and training samples, a constrained condition for updating is proposed, which effectively mitigates contamination to the tracker from the background and undesirable samples avoiding model degradation when occlusion happens. From comprehensive experiments, our tracking model obtains better performance than ECO on object tracking benchmark 2015 (OTB100), and improves the area under curve (AUC) by about 8% and 32% compared with ECO, in the scenarios of fast-moving and occlusion on our own collected dataset.
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A Dual Adaptive Unscented Kalman Filter Algorithm for SINS-Based Integrated Navigation System
Xu LyuZiyang MengChunyu LiZhenyu CaiYi HuangXiaoyong LiXingkai Yu
Keywords:AccuracyNavigationNoiseMeasurement uncertaintyFiltering algorithmsInformation filtersPollution measurementKalman FilterNavigation SystemAdaptive FilterUnscented Kalman FilterAdaptive Kalman FilterIntegrated Navigation SystemIntegrated NavigationAdaptive Unscented Kalman FilterNonlinear ModelMeasurement NoiseInertial NavigationDifferential EquationsCovariance MatrixGlobal Positioning SystemPosition ErrorOptimal EstimationRecursive AlgorithmExtended Kalman FilterNoise CovarianceVelocity ErrorClassical FilterNoise Covariance MatrixNoise MatrixNavigation AccuracyExperimental VehicleNonlinear FilterInitial NoiseMeasurement Noise CovarianceKalman filterdual-adaptiveintegrated navigationunscented Kalman filter (UKF)robust
Abstracts:In this study, the problem of measuring noise pollution distribution by the intertial-based integrated navigation system is effectively suppressed. Based on nonlinear inertial navigation error modeling, a nested dual Kalman filter framework structure is developed. It consists of unscented Kalman filter (UKF) master filter and Kalman filter slave filter. This method uses nonlinear UKF for integrated navigation state estimation. At the same time, the exact noise measurement covariance is estimated by the Kalman filter dependency filter. The algorithm based on dual adaptive UKF (Dual-AUKF) has high accuracy and robustness, especially in the case of measurement information interference. Finally, vehicle-mounted and ship-mounted integrated navigation tests are conducted. Compared with traditional UKF and the Sage-Husa adaptive UKF (SH-AUKF), this method has comparable filtering accuracy and better filtering stability. The effectiveness of the proposed algorithm is verified.
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A Deep Multimodal Fusion and Multitasking Trajectory Prediction Model for Typhoon Trajectory Prediction to Reduce Flight Scheduling Cancellation
Jun TangWanting QinQingtao PanSongyang Lao
Keywords:Atmospheric modelingTropical cyclonesAirline industryModelingData modelsMeteorologyAircraftTrajectory PredictionFlight SchedulesTrajectory Prediction ModelDeep Multimodal FusionTyphoon TrajectoryLatitudeExtreme WeatherMultiple ModalitiesHurricaneOutput FeatureRelated TasksTime And SpaceConvolutional Neural NetworkFeature SizeLong Short-term MemoryArrival TimeConvolution OperationTraffic FlowDeep FeaturesFeature Fusion3D ConvolutionAir ControlExtreme Weather ConditionsInput Gate3D Convolutional LayersReal TrajectoryAir TravelTrajectory DataBaidu MapTime Trajectoriesflight scheduling optimizationdeep multimodal fusionmultitasking trajectory predictiontyphoon weatherflight cancellationprediction reliability
Abstracts:Natural events have had a significant impact on overall flight activity, and the aviation industry plays a vital role in helping society cope with the impact of these events. As one of the most impactful weather typhoon seasons appears and continues, airlines operating in threatened areas and passengers having travel plans during this time period will pay close attention to the development of tropical storms. This paper proposes a deep multimodal fusion and multitasking trajectory prediction model that can improve the reliability of typhoon trajectory prediction and reduce the quantity of flight scheduling cancellation. The deep multimodal fusion module is formed by deep fusion of the feature output by multiple submodal fusion modules, and the multitask generation module uses longitude and latitude as two related tasks for simultaneous prediction. With more dependable data accuracy, problems can be analysed rapidly and more efficiently, enabling better decision-making with a proactive versus reactive posture. When multiple modalities coexist, features can be extracted from them simultaneously to supplement each other's information. An actual case study, the typhoon Lichma that swept China in 2019, has demonstrated that the algorithm can effectively reduce the number of unnecessary flight cancellations compared to existing flight scheduling and assist the new generation of flight scheduling systems under extreme weather.
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Online Task Planning Method of Anti-Ship Missile Based on Rolling Optimization
Faxing LuQiuyang DaiGuang YangZhengrong Jia
Keywords:MissilesAnalytical modelsSpace missionsReal-time systemsProbability distributionPlanningIndexesTask PlanningAnti-ship MissilesRolling OptimizationIndex ValuesParameter SpaceDynamic EnvironmentFailure EventsTriggering EventTarget InformationTime ComplexityParticle SwarmUnmanned Aerial VehiclesPath PlanningModel Predictive ControlTargeting VectorIntelligence AlgorithmsProbability MatrixOptimal PathCooperative ControlRoute PlanningDynamic PlanningTask SchedulingVector Of NodeMission PlanningProbability Of DamageKey VectorScenario ParametersResults Of Previous AnalysesFalse TargetsVector Of Valuestarget allocation of anti-ship missiledefense arearolling optimizationtask re-planning
Abstracts:Based on the wave attack task planning method in static complex environment and the rolling optimization framework, an online task planning method in dynamic complex environment based on rolling optimization is proposed. In the process of online task planning in dynamic complex environment, online task planning is based on event triggering including target information update event, new target addition event, target failure event, weapon failure event, etc., and the methods include defense area reanalysis, parameter space update, and mission re-planning. Simulation is conducted for different events and the result shows that the index value of the attack scenario after re-planning is better than that before re-planning and according to the probability distribution of statistical simulation method, the index value distribution after re-planning is obviously in the region of high index value, and the index value gap before and after re-planning is related to the degree of posture change.
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Equipment Damage Measurement Method of Wartime Based on FCE-PCA-RF
Mingyu LiLu GaoHongwei XuKai LiYisong Huang
Keywords:Radio frequencyEstimationArtificial neural networksForestryPrediction algorithmsMaintenancePlanningEquipment DamageNeural NetworkArtificial Neural NetworkRandom ForestComprehensive EvaluationImplementation SupportPerformed Principal Component AnalysisEquipment MaintenanceMaintenance TasksFuzzy Comprehensive EvaluationSubjective ExperienceRandom Forest ModelCumulative RateRate SetMeasures Of FactorsPrecise PredictionElectromagnetic InterferenceOriginal VariablesContribution RateCause Of DamageCumulative Contribution RateKey Influencing FactorsLevenberg-Marquardt AlgorithmVariance Contribution RateTypes Of EquipmentMeasure Of DamageNumber Of EquipmentPrincipal Component ExtractionPrincipal Component LoadingsDimension Of Layerwartimeequipment damagefuzzy comprehensive evaluation (FCE)principal component analysis (PCA)artificial neural network (ANN)random forest (RF)
Abstracts:As the “engine” of equipment continuous operation and repeated operation, equipment maintenance support plays a more prominent role in the confrontation of symmetrical combat systems. As the basis and guide for the planning and implementation of equipment maintenance tasks, the equipment damage measurement is an important guarantee for the effective implementation of maintenance support. Firstly,this article comprehensively analyses the influence factors to damage measurement from the enemy's attributes, our attributes and the battlefield environment starting from the basic problem of wartime equipment damage measurement. Secondly, this article determines the key factors based on fuzzy comprehensive evaluation (FCE) and performed principal component analysis (PCA) on the key factors. Finally, the principal components representing more than 85% of the data features are taken as the input and the equipment damage quantity is taken as the output. The data are trained and tested by artificial neural network (ANN) and random forest (RF). In a word, FCE-PCA-RF can be used as a reference for the research of equipment damage estimation in wartime.
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Risk Identification and Safety Assessment of Human-Computer Interaction in Integrated Avionics Based on STAMP
Changxiao ZhaoHao LiWei ZhangJun DaiLei Dong
Keywords:Analytical modelsAerospace electronicsProcess controlHazardsAtmospheric modelingTask analysisStatistical analysisSafety AssessmentHuman-computer InteractionRisk IdentificationQuantitative AnalysisProcess ModelActive ControlReliability AnalysisInteractive ProcessControl StructureSafety AnalysisFormal ToolsCausal PathFormal VerificationCognitive FunctionBayesian ModelHuman FactorHuman BehaviorSystem ArchitectureCurrent DesignControl ModeProbability Of FailureBasic Cognitive FunctionsHuman ErrorInteraction ScenariosHazard AnalysisAirborne SystemsCognitive ActivityCognitive FailuresFailure ModesBasic Probabilityavionicshuman-computer interaction (HCI)safety assessmentsystem-theoretic accident model and processhuman reliability analysis
Abstracts:To solve the problem of risk identification and quantitative assessment for human-computer interaction (HCI) in complex avionics systems, an HCI safety analysis framework based on system-theoretical process analysis (STPA) and cognitive reliability and error analysis method (CREAM) is proposed. STPA-CREAM can identify unsafe control actions and find the causal path during the interaction of avionics systems and pilot with the help of formal verification tools automatically. The common performance conditions (CPC) of avionics systems in the aviation environment is established and a quantitative analysis of human failure is carried out. Taking the head-up display (HUD) system interaction process as an example, a case analysis is carried out, the layered safety control structure and formal model of the HUD interaction process are established. For the interactive behavior “Pilots approaching with HUD”, four unsafe control actions and 35 causal scenarios are identified and the impact of common performance conditions at different levels on the pilot decision model are analyzed. The results show that HUD's HCI level gradually improves as the scores of CPC increase, and the quality of crew member cooperation and time sufficiency of the task is the key to its HCI. Through case analysis, it is shown that STPA-CREAM can quantitatively assess the hazards in HCI and identify the key factors that impact safety.
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How to Implement a Knowledge Graph Completeness Assessment with the Guidance of User Requirements
Ying ZhangGang Xiao
Keywords:Knowledge graphsTask analysisQuality assessmentProposalsTime-frequency analysisTime measurementStatisticsUser RequirementsQuality AssessmentNumber Of ValuesProperty ValuesSpecific CalculationUnique ValueActual RequirementsMultiple ValuesAmount Of KnowledgeChanges In UsageRelease DateUser DemandSearch VolumeUpdated KnowledgeNumber Of SearchesUser ChoiceClick-throughUser SearchTotal SearchCurrent GraphPopular KnowledgeSearch TrendsBox OfficeGold StandardUser's UsageAssessment ProcessComposersOutcome AssessmentUser PreferencesPopularityknowledge graph completeness assessmentrelative completenessuser requirementquality management
Abstracts:In the context of big data, many large-scale knowledge graphs have emerged to effectively organize the explosive growth of web data on the Internet. To select suitable knowledge graphs for use from many knowledge graphs, quality assessment is particularly important. As an important thing of quality assessment, completeness assessment generally refers to the ratio of the current data volume to the total data volume. When evaluating the completeness of a knowledge graph, it is often necessary to refine the completeness dimension by setting different completeness metrics to produce more complete and understandable evaluation results for the knowledge graph. However, lack of awareness of requirements is the most problematic quality issue. In the actual evaluation process, the existing completeness metrics need to consider the actual application. Therefore, to accurately recommend suitable knowledge graphs to many users, it is particularly important to develop relevant measurement metrics and formulate measurement schemes for completeness. In this paper, we will first clarify the concept of completeness, establish each metric of completeness, and finally design a measurement proposal for the completeness of knowledge graphs.