Analysis of the widely linear complex Kalman filter

A Widely Linear Complex Unscented Kalman Filter

IEEE Signal Processing Letters ◽

10.1109/lsp.2011.2166259 ◽

2011 ◽

Vol 18 (11) ◽

pp. 623-626 ◽

Cited By ~ 28

Author(s):

D. H. Dini ◽

D. P. Mandic ◽

S. J. Julier

Keyword(s):

Kalman Filter ◽

Unscented Kalman Filter ◽

Linear Complex ◽

Widely Linear

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Widely Linear Complex-Valued Diffusion Subband Adaptive Filter Algorithm

IEEE Transactions on Signal and Information Processing over Networks ◽

10.1109/tsipn.2018.2873242 ◽

2019 ◽

Vol 5 (2) ◽

pp. 248-257 ◽

Cited By ~ 8

Author(s):

Pengwei Wen ◽

Jiashu Zhang

Keyword(s):

Adaptive Filter ◽

Linear Complex ◽

Subband Adaptive Filter ◽

Widely Linear ◽

Complex Valued

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Variable Step-Size Widely Linear Complex-Valued Affine Projection Algorithm and Performance Analysis

IEEE Transactions on Signal Processing ◽

10.1109/tsp.2020.3029884 ◽

2020 ◽

Vol 68 ◽

pp. 5940-5953

Author(s):

Long Shi ◽

Haiquan Zhao ◽

Yuriy Zakharov ◽

Badong Chen ◽

Yaoru Yang

Keyword(s):

Performance Analysis ◽

Projection Algorithm ◽

Variable Step Size ◽

Step Size ◽

Linear Complex ◽

Affine Projection ◽

Variable Step ◽

And Performance ◽

Widely Linear ◽

Complex Valued

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Variable step-size widely linear complex-valued NLMS algorithm and its performance analysis

Signal Processing ◽

10.1016/j.sigpro.2019.06.029 ◽

2019 ◽

Vol 165 ◽

pp. 1-6 ◽

Cited By ~ 4

Author(s):

Long Shi ◽

Haiquan Zhao ◽

Xiangping Zeng ◽

Yi Yu

Keyword(s):

Performance Analysis ◽

Variable Step Size ◽

Step Size ◽

Linear Complex ◽

Nlms Algorithm ◽

Variable Step ◽

Widely Linear ◽

Complex Valued

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Widely Linear Complex-Valued Estimated-Input LMS Algorithm for Bias-Compensated Adaptive Filtering With Noisy Measurements

IEEE Transactions on Signal Processing ◽

10.1109/tsp.2019.2919412 ◽

2019 ◽

Vol 67 (13) ◽

pp. 3592-3605 ◽

Cited By ~ 9

Author(s):

Sheng Zhang ◽

Jiashu Zhang ◽

Wei Xing Zheng ◽

Hing Cheung So

Keyword(s):

Adaptive Filtering ◽

Lms Algorithm ◽

Linear Complex ◽

Noisy Measurements ◽

Widely Linear ◽

Complex Valued

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Widely linear complex partial least squares for latent subspace regression

Signal Processing ◽

10.1016/j.sigpro.2018.06.018 ◽

2018 ◽

Vol 152 ◽

pp. 350-362 ◽

Cited By ~ 6

Author(s):

Alexander E. Stott ◽

Sithan Kanna ◽

Danilo P. Mandic

Keyword(s):

Least Squares ◽

Partial Least Squares ◽

Linear Complex ◽

Widely Linear

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Shrinkage Linear and Widely Linear Complex-Valued Least Mean Squares Algorithms for Adaptive Beamforming

IEEE Transactions on Signal Processing ◽

10.1109/tsp.2014.2367452 ◽

2015 ◽

Vol 63 (1) ◽

pp. 119-131 ◽

Cited By ~ 38

Author(s):

Yun-Mei Shi ◽

Lei Huang ◽

Cheng Qian ◽

H. C. So

Keyword(s):

Adaptive Beamforming ◽

Linear Complex ◽

Least Mean Squares ◽

Widely Linear ◽

Complex Valued

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Widely Linear Complex-Valued Kernel Methods for Regression

IEEE Transactions on Signal Processing ◽

10.1109/tsp.2017.2726991 ◽

2017 ◽

Vol 65 (19) ◽

pp. 5240-5248 ◽

Cited By ~ 5

Author(s):

Rafael Boloix-Tortosa ◽

Juan Jose Murillo-Fuentes ◽

Irene Santos ◽

Fernando Perez-Cruz

Keyword(s):

Kernel Methods ◽

Linear Complex ◽

Widely Linear ◽

Complex Valued

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Blind signal separation based on widely linear complex autoregressive process of order one

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-021-01920-8 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Jiong Li ◽

Yuan Qin ◽

Menglan Fan ◽

Xiaogang Tang ◽

Lijuan Gao ◽

...

Keyword(s):

Convex Combination ◽

Temporal Structure ◽

Autoregressive Process ◽

Signal Separation ◽

Blind Signal Separation ◽

Linear Complex ◽

Contrast Function ◽

Blind Signal ◽

Baseband Signal ◽

Widely Linear

AbstractIn this paper, the blind signal separation problem of complex baseband signal is addressed. A widely linear complex autoregressive process of order one is employed to represent the temporal structure of complex sources. We formulate a new contrast function by a convex combination of generalized autocorrelations and the statistics of the innovation. And the proposed contrast function is optimized by gradient method. Simulation results show that the proposed algorithm is better than the comparison algorithm in convergence speed and convergence accuracy.

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