A Quaternion Kernel Minimum Error Entropy Adaptive Filter

Ogunfunmi, Tokunbo; Safarian, Carlo

doi:10.1109/icassp.2018.8461428

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…where •, • H is the inner product operator in H . Main objective of an estimator based on MEE is to find the optimum Ω Ω Ω o such that the cost function ξ -entropy 1 of the error minimizes [26], [30] i.e…”

Section: Proposed Estimator Based On Kmee-ncmentioning

confidence: 99%

Kernel Minimum Error Entropy Based Estimator for MIMO Radar in Non-Gaussian Clutter

et al. 2021

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In this paper, a kernel minimum error entropy (KMEE) based estimator is proposed for the estimation of multiple targets' direction of departure (DOD), the direction of arrival (DOA), and the Doppler shift with multiple input multiple output radar in the presence of non-Gaussian clutter. Most existing estimation approaches are based on optimization of a complex cost function which often leads to a sub-optimum solution. Therefore, for the accurate estimation of DOD, DOA and Doppler shift, an efficient, kernel adaptive filter (KAF) based estimation approach is proposed. The proposed estimator utilizes the minimum error entropy (MEE) criterion and minimizes the error entropy function. The MEE, being an information-theoretic criterion, optimizes the higher-order statistics of error and thus makes the proposed estimator robust against the effects of outliers like clutter. The KMEE based estimator without any sparsification suffers from a linear increase in computational complexity. Thus, subsequently, the computational complexity of the proposed KMEE based estimator is reduced by incorporation of novelty criterion (NC) based sparsification technique, and the resulting estimator is called KMEE-NC. The performance of the proposed KMEE-NC based estimator is compared with the recently introduced sparse estimators based on kernel maximum correntropy criterion, and kernel minimum mean square error criterion. Additionally, KMEE-NC based estimator is also compared with other existing conventional estimators. Further, for assessing the accuracy of the proposed estimator, the modified Cramer-Rao lower bound is derived using the modified Fisher information matrix.

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Section: Proposed Estimator Based On Kmee-ncmentioning

confidence: 99%

Kernel Minimum Error Entropy Based Estimator for MIMO Radar in Non-Gaussian Clutter

et al. 2021

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“…An interesting approach is to use an improperness measure based on the Kullback–Leibler (KL) divergence between the augmented covariance matrix and its closest proper version in the KL sense [ 51 ]. Quaternion second-order statistics have been used in several applications, from independent component analysis to canonical transform [ 54 , 55 ], from linear to nonlinear adaptive filtering [ 56 , 57 , 58 ].…”

Section: Introductionmentioning

confidence: 99%

An Information-Theoretic Perspective on Proper Quaternion Variational Autoencoders

Grassucci

Comminiello

Uncini

2021

Entropy

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Variational autoencoders are deep generative models that have recently received a great deal of attention due to their ability to model the latent distribution of any kind of input such as images and audio signals, among others. A novel variational autoncoder in the quaternion domain H, namely the QVAE, has been recently proposed, leveraging the augmented second order statics of H-proper signals. In this paper, we analyze the QVAE under an information-theoretic perspective, studying the ability of the H-proper model to approximate improper distributions as well as the built-in H-proper ones and the loss of entropy due to the improperness of the input signal. We conduct experiments on a substantial set of quaternion signals, for each of which the QVAE shows the ability of modelling the input distribution, while learning the improperness and increasing the entropy of the latent space. The proposed analysis will prove that proper QVAEs can be employed with a good approximation even when the quaternion input data are improper.

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“…Recently, an increasing interest has been shown on signal processing and machine learning algorithms in quaternion and hypercomplex domains [7][8][9][10][11][12][13][14]. In such a context, significant advances have been proposed on quaternion neural networks (QNNs) [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Quaternion Convolutional Neural Networks for Detection and Localization of 3D Sound Events

Comminiello

Lella

Scardapane

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Learning from data in the quaternion domain enables us to exploit internal dependencies of 4D signals and treating them as a single entity. One of the models that perfectly suits with quaternion-valued data processing is represented by 3D acoustic signals in their spherical harmonics decomposition. In this paper, we address the problem of localizing and detecting sound events in the spatial sound field by using quaternion-valued data processing. In particular, we consider the spherical harmonic components of the signals captured by a first-order ambisonic microphone and process them by using a quaternion convolutional neural network. Experimental results show that the proposed approach exploits the correlated nature of the ambisonic signals, thus improving accuracy results in 3D sound event detection and localization.

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A Quaternion Kernel Minimum Error Entropy Adaptive Filter

Cited by 7 publications

References 10 publications

Kernel Minimum Error Entropy Based Estimator for MIMO Radar in Non-Gaussian Clutter

Kernel Minimum Error Entropy Based Estimator for MIMO Radar in Non-Gaussian Clutter

An Information-Theoretic Perspective on Proper Quaternion Variational Autoencoders

Quaternion Convolutional Neural Networks for Detection and Localization of 3D Sound Events

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