Fast converging and low complexity adaptive filtering using an averaged Kalman filter

IEEE Transactions on Signal Processing ◽

10.1109/78.655437 ◽

1998 ◽

Vol 46 (2) ◽

pp. 515-518 ◽

Cited By ~ 7

Author(s):

T. Wigren

Keyword(s):

Kalman Filter ◽

Adaptive Filtering ◽

Low Complexity

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Kalman filter and Iterative-Hungarian Algorithm implementation for low complexity point tracking as part of fast multiple object tracking system

2016 6th International Conference on System Engineering and Technology (ICSET) ◽

10.1109/icsengt.2016.7849633 ◽

2016 ◽

Cited By ~ 13

Author(s):

Bima Sahbani ◽

Widyawardana Adiprawita

Keyword(s):

Kalman Filter ◽

Object Tracking ◽

Tracking System ◽

Low Complexity ◽

Multiple Object Tracking ◽

Hungarian Algorithm ◽

Point Tracking ◽

Multiple Object ◽

Algorithm Implementation

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A Low Complexity Extended Kalman Filter Algorithm for Neural Network Digital Predistortion of Power Amplifier

2017 International Conference on Green Informatics (ICGI) ◽

10.1109/icgi.2017.26 ◽

2017 ◽

Cited By ~ 1

Author(s):

Linhuang Wu ◽

Kaixiong Su ◽

Zhifeng Chen ◽

Pingping Chen

Keyword(s):

Neural Network ◽

Kalman Filter ◽

Extended Kalman Filter ◽

Power Amplifier ◽

Low Complexity ◽

Digital Predistortion ◽

Kalman Filter Algorithm

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Exploiting Signal Nongaussianity and Nonlinearity for Performance Assessment of Adaptive Filtering Algorithms: Qualitative Performance of Kalman Filter

2006 IEEE Nonlinear Statistical Signal Processing Workshop ◽

10.1109/nsspw.2006.4378837 ◽

2006 ◽

Author(s):

Mo Chen ◽

Temujin Gautama ◽

Dragan Obradovic ◽

Jonathon Chambers ◽

Danilo Mandic

Keyword(s):

Kalman Filter ◽

Performance Assessment ◽

Adaptive Filtering ◽

Filtering Algorithms ◽

Qualitative Performance

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Low-Complexity Data Reusing Methodsin Adaptive Filtering

IEEE Transactions on Signal Processing ◽

10.1109/tsp.2003.821338 ◽

2004 ◽

Vol 52 (2) ◽

pp. 394-405 ◽

Cited By ~ 23

Author(s):

R.A. Soni ◽

K.A. Gallivan ◽

W.K. Jenkins

Keyword(s):

Adaptive Filtering ◽

Low Complexity

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Low Complexity and Robust Pilot-aided Frequency Kalman Filter Scheme for Extreme Polarization Effects Equalization

10.1364/oecc.2021.t4b.4 ◽

2021 ◽

Author(s):

Nannan Zhang ◽

Xiaoguang Zhang ◽

Qi Zhang ◽

Nan Cui ◽

Lixia Xi

Keyword(s):

Kalman Filter ◽

Low Complexity ◽

Polarization Effects

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A High Speed and Low Complexity Architecture Design Methodology for Square Root Unscented Kalman Filter based SLAM

2020 European Conference on Circuit Theory and Design (ECCTD) ◽

10.1109/ecctd49232.2020.9218287 ◽

2020 ◽

Author(s):

Rashi Dutt ◽

Amit Acharyya

Keyword(s):

Kalman Filter ◽

High Speed ◽

Design Methodology ◽

Unscented Kalman Filter ◽

Low Complexity ◽

Architecture Design ◽

Square Root

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Low-complexity Kalman filter-based carrier frequency offset estimation and tracking for OFDM systems

2013 IEEE International Conference on Acoustics, Speech and Signal Processing ◽

10.1109/icassp.2013.6638597 ◽

2013 ◽

Cited By ~ 1

Author(s):

Mahmoud Ashour ◽

Amr El-Keyi ◽

Ahmed Sultan

Keyword(s):

Kalman Filter ◽

Carrier Frequency ◽

Carrier Frequency Offset ◽

Low Complexity ◽

Frequency Offset ◽

Ofdm Systems ◽

Frequency Offset Estimation ◽

Carrier Frequency Offset Estimation

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Constructing numerically stable Kalman filter-based algorithms for gradient-based adaptive filtering

International Journal of Adaptive Control and Signal Processing ◽

10.1002/acs.2552 ◽

2015 ◽

Vol 29 (11) ◽

pp. 1411-1426 ◽

Cited By ~ 9

Author(s):

M. V. Kulikova ◽

J. V. Tsyganova

Keyword(s):

Kalman Filter ◽

Adaptive Filtering ◽

Gradient Based

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A Low-Complexity $L_{\infty}$-Norm Adaptive Filtering Algorithm

IEEE Transactions on Circuits & Systems II Express Briefs ◽

10.1109/tcsii.2007.906974 ◽

2007 ◽

Vol 54 (12) ◽

pp. 1092-1096 ◽

Cited By ~ 3

Author(s):

Jae-Eun Lee ◽

Young-Seok Choi ◽

Woo-Jin Song

Keyword(s):

Adaptive Filtering ◽

Low Complexity ◽

Filtering Algorithm

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An Adaptive Kalman Filter Based on Sage Windowing Weights and Variance Components

Journal of Navigation ◽

10.1017/s0373463303002248 ◽

2003 ◽

Vol 56 (2) ◽

pp. 231-240 ◽

Cited By ~ 81

Author(s):

Yuanxi Yang ◽

Tianhe Xu

Keyword(s):

Kalman Filter ◽

Adaptive Filtering ◽

Variance Components ◽

Covariance Matrices ◽

Adaptive Kalman Filter ◽

Dynamic States

In this paper a brief review of Sage adaptive filtering is followed by an analysis of the shortcomings of covariance matrices formed by windowing residual vectors, innovation vectors and correction vectors of the dynamic states. A new adaptive Kalman filter is developed by combining the Sage filter and the variance components and its use tested against various other schemes.

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