## 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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- 23 February 2016 - 1:43pm
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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} }

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 -

## 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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url = {http://sigport.org/201},

author = { },

publisher = {IEEE SigPort},

title = {Simple and Accurate Algorithms for Sinusoidal Frequency Estimation},

year = {2015} }

T1 - Simple and Accurate Algorithms for Sinusoidal Frequency Estimation

AU -

PY - 2015

PB - IEEE SigPort

UR - http://sigport.org/201

ER -

## 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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url = {http://sigport.org/194},

author = { },

publisher = {IEEE SigPort},

title = {How to Derive Bias and Mean Square Error for an Estimator?},

year = {2015} }

T1 - How to Derive Bias and Mean Square Error for an Estimator?

AU -

PY - 2015

PB - IEEE SigPort

UR - http://sigport.org/194

ER -