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Signal and System Modeling, Representation and Estimation

A novel solution to the filtering problem for mixed linear/nonlinear state-space models: turbo filtering


In this manuscript the application of a factor graph approach to the filtering problem for a mixed linear/nonlinear state-space model is investigated. In particular, after developing a factor graph for the considered model, a novel approximate recursive technique for solving such a problem is derived applying the sum-product algorithm and a specific scheduling procedure for message passing to this graph.

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Authors:
Francesco Montorsi, Matteo Sola, Marco Casparriello
Submitted On:
23 February 2016 - 1:43pm
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turbo_filtering_report.pdf

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[1] Francesco Montorsi, Matteo Sola, Marco Casparriello, "A novel solution to the filtering problem for mixed linear/nonlinear state-space models: turbo filtering", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/208. Accessed: Jul. 20, 2019.
@article{208-15,
url = {http://sigport.org/208},
author = {Francesco Montorsi; Matteo Sola; Marco Casparriello },
publisher = {IEEE SigPort},
title = {A novel solution to the filtering problem for mixed linear/nonlinear state-space models: turbo filtering},
year = {2015} }
TY - EJOUR
T1 - A novel solution to the filtering problem for mixed linear/nonlinear state-space models: turbo filtering
AU - Francesco Montorsi; Matteo Sola; Marco Casparriello
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/208
ER -
Francesco Montorsi, Matteo Sola, Marco Casparriello. (2015). A novel solution to the filtering problem for mixed linear/nonlinear state-space models: turbo filtering. IEEE SigPort. http://sigport.org/208
Francesco Montorsi, Matteo Sola, Marco Casparriello, 2015. A novel solution to the filtering problem for mixed linear/nonlinear state-space models: turbo filtering. Available at: http://sigport.org/208.
Francesco Montorsi, Matteo Sola, Marco Casparriello. (2015). "A novel solution to the filtering problem for mixed linear/nonlinear state-space models: turbo filtering." Web.
1. Francesco Montorsi, Matteo Sola, Marco Casparriello. A novel solution to the filtering problem for mixed linear/nonlinear state-space models: turbo filtering [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/208

Simple and Accurate Algorithms for Sinusoidal Frequency Estimation


The problem of estimating the frequencies of sinusoidal components from a finite number of noisy discrete-time measurements has attracted a great deal of attention and still is an active research area to date, because of its wide applications in science and engineering. In this presentation, simple and accurate solutions for sinusoidal frequency estimation of 1D and 2D signals in the presence of additive white Gaussian noise are presented.

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23 February 2016 - 1:43pm
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[1] , "Simple and Accurate Algorithms for Sinusoidal Frequency Estimation", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/201. Accessed: Jul. 20, 2019.
@article{201-15,
url = {http://sigport.org/201},
author = { },
publisher = {IEEE SigPort},
title = {Simple and Accurate Algorithms for Sinusoidal Frequency Estimation},
year = {2015} }
TY - EJOUR
T1 - Simple and Accurate Algorithms for Sinusoidal Frequency Estimation
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/201
ER -
. (2015). Simple and Accurate Algorithms for Sinusoidal Frequency Estimation. IEEE SigPort. http://sigport.org/201
, 2015. Simple and Accurate Algorithms for Sinusoidal Frequency Estimation. Available at: http://sigport.org/201.
. (2015). "Simple and Accurate Algorithms for Sinusoidal Frequency Estimation." Web.
1. . Simple and Accurate Algorithms for Sinusoidal Frequency Estimation [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/201

How to Derive Bias and Mean Square Error for an Estimator?


Analyzing the performance of estimators is a typical task in signal processing. Two fundamental performance measures in the aspect of accuracy are bias and mean square error (MSE). In this presentation, we revisit a simple technique to produce the bias and MSE of an estimator that minimizes or maximizes an unconstrained differentiable cost function over a continuous space of the parameter vector under the small error conditions. This presentation is a companion work of: H. C. So, Y. T. Chan, K. C. Ho and Y.

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23 February 2016 - 1:43pm
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compute_bias_mse.pdf

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[1] , "How to Derive Bias and Mean Square Error for an Estimator?", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/194. Accessed: Jul. 20, 2019.
@article{194-15,
url = {http://sigport.org/194},
author = { },
publisher = {IEEE SigPort},
title = {How to Derive Bias and Mean Square Error for an Estimator?},
year = {2015} }
TY - EJOUR
T1 - How to Derive Bias and Mean Square Error for an Estimator?
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/194
ER -
. (2015). How to Derive Bias and Mean Square Error for an Estimator?. IEEE SigPort. http://sigport.org/194
, 2015. How to Derive Bias and Mean Square Error for an Estimator?. Available at: http://sigport.org/194.
. (2015). "How to Derive Bias and Mean Square Error for an Estimator?." Web.
1. . How to Derive Bias and Mean Square Error for an Estimator? [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/194

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