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Statistical Signal Processing

Cramer-Rao lower bounds of joint delay-Doppler estimation for an extended target


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Authors:
Tianyao Huang
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23 February 2016 - 1:43pm
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ppt.pdf

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[1] Tianyao Huang, "Cramer-Rao lower bounds of joint delay-Doppler estimation for an extended target", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/241. Accessed: Sep. 20, 2018.
@article{241-15,
url = {http://sigport.org/241},
author = {Tianyao Huang },
publisher = {IEEE SigPort},
title = {Cramer-Rao lower bounds of joint delay-Doppler estimation for an extended target},
year = {2015} }
TY - EJOUR
T1 - Cramer-Rao lower bounds of joint delay-Doppler estimation for an extended target
AU - Tianyao Huang
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/241
ER -
Tianyao Huang. (2015). Cramer-Rao lower bounds of joint delay-Doppler estimation for an extended target. IEEE SigPort. http://sigport.org/241
Tianyao Huang, 2015. Cramer-Rao lower bounds of joint delay-Doppler estimation for an extended target. Available at: http://sigport.org/241.
Tianyao Huang. (2015). "Cramer-Rao lower bounds of joint delay-Doppler estimation for an extended target." Web.
1. Tianyao Huang. Cramer-Rao lower bounds of joint delay-Doppler estimation for an extended target [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/241

A novel wavelet based shock wave detector


In this paper, the detection of shock wave that generated by supersonic bullet is considered. We present a new framework based on wavelet multi-scale products method. We analyze the method under the standard likelihood ratio test. It is found that the multi-scale product method is made in an assumption that is extremely restricted, just hold for a special noise condition. Based on the analysis, a general condition is considered for the detection. An optimal detector under the standard likelihood ratio test is proposed.

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ChinaSIP Poster_final.pptx

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An improved wavelet based shock wave detector.pdf

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[1] , "A novel wavelet based shock wave detector", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/211. Accessed: Sep. 20, 2018.
@article{211-15,
url = {http://sigport.org/211},
author = { },
publisher = {IEEE SigPort},
title = {A novel wavelet based shock wave detector},
year = {2015} }
TY - EJOUR
T1 - A novel wavelet based shock wave detector
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/211
ER -
. (2015). A novel wavelet based shock wave detector. IEEE SigPort. http://sigport.org/211
, 2015. A novel wavelet based shock wave detector. Available at: http://sigport.org/211.
. (2015). "A novel wavelet based shock wave detector." Web.
1. . A novel wavelet based shock wave detector [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/211

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
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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: Sep. 20, 2018.
@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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Sinusodial_Frequency_Estimation.pdf

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[1] , "Simple and Accurate Algorithms for Sinusoidal Frequency Estimation", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/201. Accessed: Sep. 20, 2018.
@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

Source Localization: Applications and Algorithms


Finding the position of a target based on measurements from an array of spatially separated sensors has been an important problem in radar, sonar, global positioning system, mobile communications, multimedia and wireless sensor networks. Time-of-arrival (TOA), time-difference-of-arrival (TDOA), received signal strength (RSS) and direction-of-arrival (DOA) of the emitted signal are commonly used measurements for source localization. Basically, TOAs, TDOAs and RSSs provide the distance information between the source and sensors while DOAs are the source bearings relative to the receivers.

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23 February 2016 - 1:38pm
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Source_Localization.pdf

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[1] , "Source Localization: Applications and Algorithms", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/200. Accessed: Sep. 20, 2018.
@article{200-15,
url = {http://sigport.org/200},
author = { },
publisher = {IEEE SigPort},
title = {Source Localization: Applications and Algorithms},
year = {2015} }
TY - EJOUR
T1 - Source Localization: Applications and Algorithms
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/200
ER -
. (2015). Source Localization: Applications and Algorithms. IEEE SigPort. http://sigport.org/200
, 2015. Source Localization: Applications and Algorithms. Available at: http://sigport.org/200.
. (2015). "Source Localization: Applications and Algorithms." Web.
1. . Source Localization: Applications and Algorithms [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/200

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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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: Sep. 20, 2018.
@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

Graph Signal Processing – A Probabilistic Framework


This theoretical paper aims to provide a probabilistic framework for graph signal processing. By modeling signals on graphs as Gaussian Markov Random Fields, we present numerous important aspects of graph signal processing, including graph construction, graph transform, graph downsampling, graph prediction, and graph-based regularization, from a probabilistic point of view.

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Authors:
Dinei Florencio
Submitted On:
23 February 2016 - 1:43pm
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graphSP_prob.pdf

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[1] Dinei Florencio, "Graph Signal Processing – A Probabilistic Framework", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/184. Accessed: Sep. 20, 2018.
@article{184-15,
url = {http://sigport.org/184},
author = {Dinei Florencio },
publisher = {IEEE SigPort},
title = {Graph Signal Processing – A Probabilistic Framework},
year = {2015} }
TY - EJOUR
T1 - Graph Signal Processing – A Probabilistic Framework
AU - Dinei Florencio
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/184
ER -
Dinei Florencio. (2015). Graph Signal Processing – A Probabilistic Framework. IEEE SigPort. http://sigport.org/184
Dinei Florencio, 2015. Graph Signal Processing – A Probabilistic Framework. Available at: http://sigport.org/184.
Dinei Florencio. (2015). "Graph Signal Processing – A Probabilistic Framework." Web.
1. Dinei Florencio. Graph Signal Processing – A Probabilistic Framework [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/184

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