Bivariate Empirical Mode Decomposition for Cognitive Radar Scene Analysis

Güntürkün, Ulaş

doi:10.1109/lsp.2014.2365361

Cited by 8 publications

(4 citation statements)

References 23 publications

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“…In this work, a signal model for a rotating element is derived for an linear frequency modulation (LFM) chirp, to which a bespoke micro‐Doppler selection algorithm is designed around; this is based on direct analysis of the intrinsic mode function. The implementation of this technique is pursued for its suitability in decomposing data which is non‐stationary and potentially non‐linear [55]; hence, the suitability for application in drone classification.…”

Section: Drone Detection and Classification Techniquesmentioning

confidence: 99%

Review of radar classification and RCS characterisation techniques for small UAVs or drones

Patel

Fioranelli

Anderson

2018

IET Radar, Sonar & Navigation

152

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This review explores radar based techniques currently utilised in literature to monitor small UAVs or drones; several challenges have arisen due to their rapid emergence and commercialisation within the mass market. The potential security threats posed by these systems are collectively presented and the legal issues surrounding their successful integration is briefly outlined. Key difficulties involved in the identification and hence tracking of these 'radar elusive' systems are discussed, along with how research efforts relating to drone detection, classification and RCS characterisation are being directed in order to address this emerging challenge. Such methods are thoroughly analysed and critiqued; finally, an overall picture of the field in its current state is painted, alongside scope for future work over a broad spectrum.

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Section: Drone Detection and Classification Techniquesmentioning

confidence: 99%

Review of radar classification and RCS characterisation techniques for small UAVs or drones

Patel

Fioranelli

Anderson

2018

IET Radar, Sonar & Navigation

152

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“…Although EMD has been widely used in various fields of engineering [20][21][22][23][24][25] due to its unique properties and preferable performance, it still suffers from the lack of firm theoretical basis and analytical interpretation of the outputs. Hence, it is extremely needed to recognize the statistical characteristics of this technique in terms of both model selection and parameter estimation for different applications in the real world.…”

Section: Introductionmentioning

confidence: 99%

Structural Damage Localization and Quantification Based on a CEEMDAN Hilbert Transform Neural Network Approach: A Model Steel Truss Bridge Case Study

Mousavi

Zhang

Masri

et al. 2020

Sensors

125

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Vibrations of complex structures such as bridges mostly present nonlinear and non-stationary behaviors. Recently, one of the most common techniques to analyze the nonlinear and non-stationary structural response is Hilbert–Huang Transform (HHT). This paper aims to evaluate the performance of HHT based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique using an Artificial Neural Network (ANN) as a proposed damage detection methodology. The performance of the proposed method is investigated for damage detection of a scaled steel-truss bridge model which was experimentally established as the case study subjected to white noise excitations. To this end, four key features of the intrinsic mode function (IMF), including energy, instantaneous amplitude (IA), unwrapped phase, and instantaneous frequency (IF), are extracted to assess the presence, severity, and location of the damage. By analyzing the experimental results through different damage indices defined based on the extracted features, the capabilities of the CEEMDAN-HT-ANN model in detecting, addressing the location and classifying the severity of damage are efficiently concluded. In addition, the energy-based damage index demonstrates a more effective approach in detecting the damage compared to those based on IA and unwrapped phase parameters.

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“…For example, in Bayesian target tracking [1], [2], [3], the posterior probability distribution of the target is calculated based upon a prior distribution on the states of targets and the likelihood function that characterizes the information in radar measurements. In cognitive radar research (see e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Let's consider a Bayesian model that involves measured data y, unknown parameters θ, and hidden variables u. Under the Bayesian framework, we treat θ as random variables and obtain the posterior density p(θ|y) by calculating the joint posterior density p(u, θ|y) using the Bayes rule p(u, θ|y) = p(y, u, θ)/p(y) (1) Copyright (c) 2015 IEEE. Personal use of this material is permitted.…”

Section: Introductionmentioning

confidence: 99%

Multi-Parameter Estimation in Compound Gaussian Clutter by Variational Bayesian

Turlapaty

Jin

2016

IEEE Trans. Signal Process.

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In this paper, we consider the problem of multiparameter estimation in the presence of compound Gaussian clutter for cognitive radar by the variational Bayesian method. The advantage of variational Bayesian is that the estimation of multi-variate parameters is decomposed to problems of estimation of univariate parameters by variational approximation, thus enabling analytically tractable approximate posterior densities in complex statistical models consisting of observed data, unknown parameters, and hidden variables. We derive the asymptotic Bayesian Cramer Rao bounds and demonstrate by numerical simulations that the proposed approach leads to improved estimation accuracy than the expectation maximization method and the exact Bayesian method in the case of non-Gaussian nonlinear signal models and small data sample size.Index Terms-Variational Bayesian, compound Gaussian clutter, cognitive radar 1053-587X (c)

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Bivariate Empirical Mode Decomposition for Cognitive Radar Scene Analysis

Cited by 8 publications

References 23 publications

Review of radar classification and RCS characterisation techniques for small UAVs or drones

Review of radar classification and RCS characterisation techniques for small UAVs or drones

Structural Damage Localization and Quantification Based on a CEEMDAN Hilbert Transform Neural Network Approach: A Model Steel Truss Bridge Case Study

Multi-Parameter Estimation in Compound Gaussian Clutter by Variational Bayesian

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