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Measure of nonlinearity for underwater target tracking using hull-mounted sensor

International Journal of Intelligent Computing and Cybernetics 2022年第3期15卷 333-344页

作者： b.omkar lakshmi jagan S.Koteswara Rao Department of Electrical and Electronics Engineering Koneru Lakshmaiah Education FoundationGunturIndia Department of Electronics and Communication Engineering Koneru Lakshmaiah Education FoundationGunturIndia

Purpose-Doppler-bearing Tracking(DbT)is commonly used in target tracking applications for the underwater environment using the Hull-Mounted Sensor(HMS).It is an important and challenging problem in an underwater environment.Design/methodology/approach-The system nonlinearity in an underwater environment increases due to several reasons such as the type of measurements taken,the speeds of target and observer,environmental conditions,number of sensors considered for measurements and so on.Degrees of nonlinearity(DoNL)for these problems are analyzed using a proposed measure of nonlinearity(MoNL)for state estimation.Findings-In this research,the authors analyzed MoNL for state estimation and computed the conditional MoNL(normalized)using different filtering algorithms where measurements are obtained from a single sensor array(i.e.HMS).MoNL is implemented to find out the system nonlinearity for different filtering algorithms and identified how much nonlinear the system is,that is,to measure nonlinearity of a problem.Originality/value-Algorithms are evaluated for various scenarios with different angles on the target bow(ATb)in Monte-Carlo simulation.Computation of root mean squared(RMS)errors in position and velocity is carried out to assess the state estimation accuracy using MATLAb.

关键词： Nonlinear filtering Statistical signal processing Underwater target tracking Estimation theory Hull-mounted sensor

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Gait Recognition by Cross Wavelet Transform and Graph Model

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IEEE/CAA Journal of Automatica Sinica 2018年第3期5卷 718-726页

作者： Sagar Arun More Pramod jagan Deore Department of Electronics and Telecommunication Engineering R.C.Patel Institute of Technology

In this paper, a multi-view gait based human recognition system using the fusion of two kinds of features is proposed.We use cross wavelet transform to extract dynamic feature and bipartite graph model to extract static feature which are coefficients of quadrature mirror filter(QMF)-graph wavelet filter bank. Feature fusion is done after normalization. For normalization of features, min-max rule is used and mean-variance method is used to find weights for normalized features. Euclidean distance between each feature vector and center of the cluster which is obtained by k-means clustering is used as similarity measure in bayesian framework. Experiments performed on widely used CASIA b gait database show that, the fusion of these two feature sets preserve discriminant information. We report 99.90 % average recognition rate.

关键词： binary sequences feature extraction identification of persons linear discriminant analysis(LDA)

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Determination of effective stress parameter of unsaturated soils:A Gaussian process regression approach

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Frontiers of Structural and Civil Engineering 2013年第2期7卷 133-136页

作者： Pijush Samui jagan J Centre for Disaster Mitigation and Management VIT UniversityVellore-632014India

This article examines the capability of Gaussian process regression(GPR)for prediction of effective stress parameter(χ)of unsaturated soil.GPR method proceeds by parameterising a covariance function,and then infers the parameters given the data set.Input variables of GPR are net confining pressure(σ_(3)),saturated volumetric water content(θ_(s)),residual water content(θ_(r)),bubbling pressure(h_(b)),suction(s)and fitting parameter(l).A comparative study has been carried out between the developed GPR and Artificial Neural Network(ANN)models.A sensitivity analysis has been done to determine the effect of each input parameter onχ.The developed GPR gives the variance of predictedχ.The results show that the developed GPR is reliable model for prediction ofχof unsaturated soil.

关键词： unsaturated soil effective stress parameter Gaussian process regression(GPR) artificial neural network(ANN) variance

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