The Quaternion LMS Algorithm for Adaptive Filtering of Hypercomplex Processes

Took, CC and Mandic, D (2009) The Quaternion LMS Algorithm for Adaptive Filtering of Hypercomplex Processes IEEE Transactions on Signal Processing, 57 (4). pp. 1316-1327.

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Abstract

The quaternion least mean square (QLMS) algorithm is introduced for adaptive filtering of three- and four-dimensional processes, such as those observed in atmospheric modeling (wind, vector fields). These processes exhibit complex nonlinear dynamics and coupling between the dimensions, which make their component-wise processing by multiple univariate LMS, bivariate complex LMS (CLMS), or multichannel LMS (MLMS) algorithms inadequate. The QLMS accounts for these problems naturally, as it is derived directly in the quaternion domain. The analysis shows that QLMS operates inherently based on the so called ldquoaugmentedrdquo statistics, that is, both the covariance E{ xx^H} and pseudocovariance E{ xx^T} of the tap input vector x are taken into account. In addition, the operation in the quaternion domain facilitates fusion of heterogeneous data sources, for instance, the three vector dimensions of the wind field and air temperature. Simulations on both benchmark and real world data support the approach.

Item Type:	Article
Authors :	Name Email ORCID Took, CC Mandic, D
Date :	April 2009
DOI :	10.1109/TSP.2008.2010600
Related URLs :	Publisher
Depositing User :	Symplectic Elements
Date Deposited :	28 Mar 2017 14:12
Last Modified :	31 Oct 2017 14:53
URI:	http://epubs.surrey.ac.uk/id/eprint/739351

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