On 4-Dimensional Hypercomplex Algebras in Adaptive Signal Processing

The degree of diffusion of hypercomplex algebras in adaptive and non-adaptive filtering research topics is growing faster and faster. The debate today concerns the usefulness and the benefits of representing multidimensional systems by means of these complicated mathematical structures and the criterions of choice between one algebra or another. This paper proposes a simple comparison between two isodimensional algebras (quaternions and tessarines) and shows by simulations how different choices may determine the system performance. Some general information about both algebras is also supplied.

[1]  Michael A. Gerzon,et al.  Ambisonics in Multichannel Broadcasting and Video , 1985 .

[2]  E. Kraft,et al.  A quaternion-based unscented Kalman filter for orientation tracking , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[3]  Andrzej Katunin THREE-DIMENSIONAL OCTONION WAVELET TRANSFORM , 2014 .

[4]  Aurelio Uncini,et al.  A new approach to acoustic beamforming from virtual microphones based on ambisonics for adaptive noise cancelling , 2016, 2016 IEEE 36th International Conference on Electronics and Nanotechnology (ELNANO).

[5]  Danilo P. Mandic,et al.  The widely linear quaternion recursive least squares filter , 2010, 2010 2nd International Workshop on Cognitive Information Processing.

[6]  D. Mandic,et al.  Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models , 2009 .

[7]  Pascal Chevalier,et al.  Widely linear estimation with complex data , 1995, IEEE Trans. Signal Process..

[8]  Danilo P. Mandic,et al.  A class of quaternion valued affine projection algorithms , 2013, Signal Process..

[9]  Danilo P. Mandic,et al.  The Quaternion LMS Algorithm for Adaptive Filtering of Hypercomplex Processes , 2009, IEEE Transactions on Signal Processing.

[10]  Danilo P. Mandic,et al.  Augmented second-order statistics of quaternion random signals , 2011, Signal Process..

[11]  Danilo Comminiello,et al.  Frequency domain quaternion adaptive filters: Algorithms and convergence performance , 2017, Signal Process..

[12]  N. Vakhania RANDOM VECTORS WITH VALUES IN QUATERNION HILBERT SPACES , 1999 .

[13]  James L. Massey,et al.  Proper complex random processes with applications to information theory , 1993, IEEE Trans. Inf. Theory.

[14]  Danilo Comminiello,et al.  The widely linear block quaternion least mean square algorithm for fast computation in 3D audio systems , 2016, 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).

[15]  Tülay Adali,et al.  Complex-Valued Linear and Widely Linear Filtering Using MSE and Gaussian Entropy , 2012, IEEE Transactions on Signal Processing.

[16]  Bernard C. Picinbono,et al.  On circularity , 1994, IEEE Trans. Signal Process..

[17]  Danilo P. Mandic,et al.  A Quaternion Widely Linear Adaptive Filter , 2010, IEEE Transactions on Signal Processing.

[18]  Danilo P. Mandic,et al.  Complex Valued Nonlinear Adaptive Filters , 2009 .

[19]  Quentin Barthelemy,et al.  About QLMS Derivations , 2014, IEEE Signal Processing Letters.

[20]  Nicolas Le Bihan,et al.  Fast Complexified Quaternion Fourier Transform , 2006, IEEE Transactions on Signal Processing.

[21]  Michael A. Gerzon,et al.  Ambisonics. Part two: Studio techniques , 1975 .