### 2009

Achutegui, Katrin; Martino, Luca; Rodas, Javier; Escudero, Carlos J; Miguez, Joaquin

A Multi-Model Particle Filtering Algorithm for Indoor Tracking of Mobile Terminals Using RSS Data Artículo en actas

En: 2009 IEEE International Conference on Control Applications, pp. 1702–1707, IEEE, Saint Petersburg, 2009, ISBN: 978-1-4244-4601-8.

Resumen | Enlaces | BibTeX | Etiquetas: Bayesian methods, Control systems, Filtering algorithms, generalized interacting multiple model, GIMM, indoor radio, Indoor tracking, mobile radio, mobile terminal, Monte Carlo methods, multipath propagation, position-dependent data measurement, random process, random processes, Rao-Blackwellized sequential Monte Carlo tracking, received signal strength, RSS data, Sliding mode control, State-space methods, state-space model, Target tracking, tracking, transmitter-to-receiver distance, wireless network, wireless technology

@inproceedings{Achutegui2009,

title = {A Multi-Model Particle Filtering Algorithm for Indoor Tracking of Mobile Terminals Using RSS Data},

author = {Katrin Achutegui and Luca Martino and Javier Rodas and Carlos J Escudero and Joaquin Miguez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5280960},

isbn = {978-1-4244-4601-8},

year = {2009},

date = {2009-01-01},

booktitle = {2009 IEEE International Conference on Control Applications},

pages = {1702--1707},

publisher = {IEEE},

address = {Saint Petersburg},

abstract = {In this paper we address the problem of indoor tracking using received signal strength (RSS) as a position-dependent data measurement. This type of measurements is very appealing because they can be easily obtained with a variety of wireless technologies which are relatively inexpensive. The extraction of accurate location information from RSS in indoor scenarios is not an easy task, though. Since RSS is highly influenced by multipath propagation, it turns out very hard to adequately model the correspondence between the received power and the transmitter-to-receiver distance. The measurement models proposed in the literature are site-specific and require a great deal of information regarding the structure of the building where the tracking will be performed and therefore are not useful for a general application. For that reason we propose the use of a compound model that combines several sub-models, whose parameters are adjusted to specific and different propagation environments. This methodology, is called interacting multiple models (IMM), has been used in the past for modeling the motion of maneuvering targets. Here, we extend its application to handle also the uncertainty in the RSS observations and we refer to the resulting state-space model as a generalized IMM (GIMM) system. The flexibility of the GIMM approach is attained at the expense of an increase in the number of random processes that must be accurately tracked. To overcome this difficulty, we introduce a Rao-Blackwellized sequential Monte Carlo tracking algorithm that exhibits good performance both with synthetic and experimental data.},

keywords = {Bayesian methods, Control systems, Filtering algorithms, generalized interacting multiple model, GIMM, indoor radio, Indoor tracking, mobile radio, mobile terminal, Monte Carlo methods, multipath propagation, position-dependent data measurement, random process, random processes, Rao-Blackwellized sequential Monte Carlo tracking, received signal strength, RSS data, Sliding mode control, State-space methods, state-space model, Target tracking, tracking, transmitter-to-receiver distance, wireless network, wireless technology},

pubstate = {published},

tppubtype = {inproceedings}

}

Djuric, Petar M; Bugallo, Monica F; Closas, Pau; Miguez, Joaquin

Measuring the Robustness of Sequential Methods Artículo en actas

En: 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, pp. 29–32, IEEE, Aruba, Dutch Antilles, 2009, ISBN: 978-1-4244-5179-1.

Resumen | Enlaces | BibTeX | Etiquetas: Additive noise, cumulative distribution functions, data processing method, extended Kalman filtering, Extraterrestrial measurements, Filtering, Gaussian distribution, Gaussian noise, Kalman filters, Kolmogorov-Smirnov distance, Least squares approximation, Noise robustness, nonlinear filters, robustness, sequential methods, statistical distributions, telecommunication computing

@inproceedings{Djuric2009a,

title = {Measuring the Robustness of Sequential Methods},

author = {Petar M Djuric and Monica F Bugallo and Pau Closas and Joaquin Miguez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5413275},

isbn = {978-1-4244-5179-1},

year = {2009},

date = {2009-01-01},

booktitle = {2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop},

pages = {29--32},

publisher = {IEEE},

address = {Aruba, Dutch Antilles},

abstract = {Whenever we apply methods for processing data, we make a number of model assumptions. In reality, these assumptions are not always correct. Robust methods can withstand model inaccuracies, that is, despite some incorrect assumptions they can still produce good results. We often want to know how robust employed methods are. To that end we need to have a yardstick for measuring robustness. In this paper, we propose an approach for constructing such metrics for sequential methods. These metrics are derived from the Kolmogorov-Smirnov distance between the cumulative distribution functions of the actual observations and the ones based on the assumed model. The use of the proposed metrics is demonstrated with simulation examples.},

keywords = {Additive noise, cumulative distribution functions, data processing method, extended Kalman filtering, Extraterrestrial measurements, Filtering, Gaussian distribution, Gaussian noise, Kalman filters, Kolmogorov-Smirnov distance, Least squares approximation, Noise robustness, nonlinear filters, robustness, sequential methods, statistical distributions, telecommunication computing},

pubstate = {published},

tppubtype = {inproceedings}

}

Martino, Luca; Miguez, Joaquin

New Accept/Reject Methods for Independent Sampling from Posterior Probability Distributions Artículo en actas

En: 17th European Signal Processing Conference (EUSIPCO 2009), Glasgow, 2009.

Resumen | Enlaces | BibTeX | Etiquetas:

@inproceedings{Martino2009a,

title = {New Accept/Reject Methods for Independent Sampling from Posterior Probability Distributions},

author = {Luca Martino and Joaquin Miguez},

url = {http://www.academia.edu/2355641/NEW_ACCEPT_REJECT_METHODS_FOR_INDEPENDENT_SAMPLING_FROM_POSTERIOR_PROBABILITY_DISTRIBUTIONS},

year = {2009},

date = {2009-01-01},

booktitle = {17th European Signal Processing Conference (EUSIPCO 2009)},

address = {Glasgow},

abstract = {Rejection sampling (RS) is a well-known method to generate(pseudo-)random samples from arbitrary probability distributionsthat enjoys important applications, either by itself or as a tool inmore sophisticated Monte Carlo techniques. Unfortunately, the useof RS techniques demands the calculation of tight upper bounds forthe ratio of the target probability density function (pdf) over theproposal density from which candidate samples are drawn. Exceptfor the class of log-concave target pdf’s, for which an efﬁcientalgorithm exists, there are no general methods to analyticallydetermine this bound, which has to be derived from scratch foreach speciﬁc case. In this paper, we tackle the general problemof applying RS to draw from an arbitrary posterior pdf using theprior density as a proposal function. This is a scenario that appearsfrequently in Bayesian signal processing methods. We derive ageneral geometric procedure for the calculation of upper boundsthat can be used with a broad class of target pdf’s, includingscenarios with correlated observations, multimodal and/or mixturemeasurement noises. We provide some simple numerical examplesto illustrate the application of the proposed techniques},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Perez-Cruz, Fernando; Kulkarni, S R

Distributed Least Square for Consensus Building in Sensor Networks Artículo en actas

En: 2009 IEEE International Symposium on Information Theory, pp. 2877–2881, IEEE, Seoul, 2009, ISBN: 978-1-4244-4312-3.

Resumen | Enlaces | BibTeX | Etiquetas: Change detection algorithms, Channel Coding, Distributed computing, distributed least square method, graphical models, Inference algorithms, Kernel, Least squares methods, nonparametric statistics, Parametric statistics, robustness, sensor-network learning, statistical analysis, Telecommunication network reliability, Wireless sensor network, Wireless Sensor Networks

@inproceedings{Perez-Cruz2009,

title = {Distributed Least Square for Consensus Building in Sensor Networks},

author = {Fernando Perez-Cruz and S R Kulkarni},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5205336},

isbn = {978-1-4244-4312-3},

year = {2009},

date = {2009-01-01},

booktitle = {2009 IEEE International Symposium on Information Theory},

pages = {2877--2881},

publisher = {IEEE},

address = {Seoul},

abstract = {We present a novel mechanism for consensus building in sensor networks. The proposed algorithm has three main properties that make it suitable for general sensor-network learning. First, the proposed algorithm is based on robust nonparametric statistics and thereby needs little prior knowledge about the network and the function that needs to be estimated. Second, the algorithm uses only local information about the network and it communicates only with nearby sensors. Third, the algorithm is completely asynchronous and robust. It does not need to coordinate the sensors to estimate the underlying function and it is not affected if other sensors in the network stop working. Therefore, the proposed algorithm is an ideal candidate for sensor networks deployed in remote and inaccessible areas, which might need to change their objective once they have been set up.},

keywords = {Change detection algorithms, Channel Coding, Distributed computing, distributed least square method, graphical models, Inference algorithms, Kernel, Least squares methods, nonparametric statistics, Parametric statistics, robustness, sensor-network learning, statistical analysis, Telecommunication network reliability, Wireless sensor network, Wireless Sensor Networks},

pubstate = {published},

tppubtype = {inproceedings}

}

Fresia, Maria; Perez-Cruz, Fernando; Poor, Vincent H

Optimized Concatenated LDPC Codes for Joint Source-Channel Coding Artículo en actas

En: 2009 IEEE International Symposium on Information Theory, pp. 2131–2135, IEEE, Seoul, 2009, ISBN: 978-1-4244-4312-3.

Resumen | Enlaces | BibTeX | Etiquetas: approximation theory, asymptotic behavior analysis, Channel Coding, combined source-channel coding, Concatenated codes, Decoding, Entropy, EXIT chart, extrinsic information transfer, H infinity control, Information analysis, joint belief propagation decoder, joint source-channel coding, low-density-parity-check code, optimized concatenated independent LDPC codes, parity check codes, Redundancy, source coding, transmitter, Transmitters

@inproceedings{Fresia2009,

title = {Optimized Concatenated LDPC Codes for Joint Source-Channel Coding},

author = {Maria Fresia and Fernando Perez-Cruz and Vincent H Poor},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5205766},

isbn = {978-1-4244-4312-3},

year = {2009},

date = {2009-01-01},

booktitle = {2009 IEEE International Symposium on Information Theory},

pages = {2131--2135},

publisher = {IEEE},

address = {Seoul},

abstract = {In this paper a scheme for joint source-channel coding based on low-density-parity-check (LDPC) codes is investigated. Two concatenated independent LDPC codes are used in the transmitter: one for source coding and the other for channel coding, with a joint belief propagation decoder. The asymptotic behavior is analyzed using EXtrinsic Information Transfer (EXIT) charts and this approximation is corroborated with illustrative experiments. The optimization of the degree distributions for our sparse code to maximize the information transmission rate is also considered.},

keywords = {approximation theory, asymptotic behavior analysis, Channel Coding, combined source-channel coding, Concatenated codes, Decoding, Entropy, EXIT chart, extrinsic information transfer, H infinity control, Information analysis, joint belief propagation decoder, joint source-channel coding, low-density-parity-check code, optimized concatenated independent LDPC codes, parity check codes, Redundancy, source coding, transmitter, Transmitters},

pubstate = {published},

tppubtype = {inproceedings}

}

Martino, Luca; Miguez, Joaquin

An Adaptive Accept/Reject Sampling Algorithm for Posterior Probability Distributions Artículo en actas

En: 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, pp. 45–48, IEEE, Cardiff, 2009, ISBN: 978-1-4244-2709-3.

Resumen | Enlaces | BibTeX | Etiquetas: adaptive accept/reject sampling, Adaptive rejection sampling, arbitrary target probability distributions, Computer Simulation, Filtering, Monte Carlo integration, Monte Carlo methods, posterior probability distributions, Probability, Probability density function, Probability distribution, Proposals, Rejection sampling, Sampling methods, sensor networks, Signal processing algorithms, signal sampling, Testing

@inproceedings{Martino2009b,

title = {An Adaptive Accept/Reject Sampling Algorithm for Posterior Probability Distributions},

author = {Luca Martino and Joaquin Miguez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5278644},

isbn = {978-1-4244-2709-3},

year = {2009},

date = {2009-01-01},

booktitle = {2009 IEEE/SP 15th Workshop on Statistical Signal Processing},

pages = {45--48},

publisher = {IEEE},

address = {Cardiff},

abstract = {Accept/reject sampling is a well-known method to generate random samples from arbitrary target probability distributions. It demands the design of a suitable proposal probability density function (pdf) from which candidate samples can be drawn. These samples are either accepted or rejected depending on a test involving the ratio of the target and proposal densities. In this paper we introduce an adaptive method to build a sequence of proposal pdf's that approximate the target density and hence can ensure a high acceptance rate. In order to illustrate the application of the method we design an accept/reject particle filter and then assess its performance and sampling efficiency numerically, by means of computer simulations.},

keywords = {adaptive accept/reject sampling, Adaptive rejection sampling, arbitrary target probability distributions, Computer Simulation, Filtering, Monte Carlo integration, Monte Carlo methods, posterior probability distributions, Probability, Probability density function, Probability distribution, Proposals, Rejection sampling, Sampling methods, sensor networks, Signal processing algorithms, signal sampling, Testing},

pubstate = {published},

tppubtype = {inproceedings}

}

Vinuelas-Peris, Pablo; Artés-Rodríguez, Antonio

Sensing Matrix Optimization in Distributed Compressed Sensing Artículo en actas

En: 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, pp. 638–641, IEEE, Cardiff, 2009, ISBN: 978-1-4244-2709-3.

Resumen | Enlaces | BibTeX | Etiquetas: Compressed sensing, Computer Simulation, computer simulations, correlated signal, Correlated signals, correlation theory, Dictionaries, distributed coding strategy, distributed compressed sensing, Distributed control, efficient projection method, Encoding, joint recovery method, Matching pursuit algorithms, Optimization methods, orthogonal matching pursuit, Projection Matrix Optimization, sensing matrix optimization, Sensor Network, Sensor phenomena and characterization, Sensor systems, Signal processing, Sparse matrices, Technological innovation

@inproceedings{Vinuelas-Peris2009,

title = {Sensing Matrix Optimization in Distributed Compressed Sensing},

author = {Pablo Vinuelas-Peris and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5278496},

isbn = {978-1-4244-2709-3},

year = {2009},

date = {2009-01-01},

booktitle = {2009 IEEE/SP 15th Workshop on Statistical Signal Processing},

pages = {638--641},

publisher = {IEEE},

address = {Cardiff},

abstract = {Distributed compressed sensing (DCS) seeks to simultaneously measure signals that are each individually sparse in some domain(s) and also mutually correlated. In this paper we consider the scenario in which the (overcomplete) bases for common component and innovations are different. We propose and analyze a distributed coding strategy for the common component, and also the use of efficient projection (EP) method for optimizing the sensing matrices in this setting. We show the effectiveness of our approach by computer simulations using the orthogonal matching pursuit (OMP) as joint recovery method, and we discuss the configuration of the distribution strategy.},

keywords = {Compressed sensing, Computer Simulation, computer simulations, correlated signal, Correlated signals, correlation theory, Dictionaries, distributed coding strategy, distributed compressed sensing, Distributed control, efficient projection method, Encoding, joint recovery method, Matching pursuit algorithms, Optimization methods, orthogonal matching pursuit, Projection Matrix Optimization, sensing matrix optimization, Sensor Network, Sensor phenomena and characterization, Sensor systems, Signal processing, Sparse matrices, Technological innovation},

pubstate = {published},

tppubtype = {inproceedings}

}

Perez-Cruz, Fernando; Rodrigues, Miguel R D; Verdu, Sergio

Optimal Precoding for Multiple-Input Multiple-Output Gaussian Channels Artículo en actas

En: Seminar PIIRS, Princeton, 2009.

Resumen | Enlaces | BibTeX | Etiquetas: Theory &amp; Algorithms

@inproceedings{Perez-Cruz2009a,

title = {Optimal Precoding for Multiple-Input Multiple-Output Gaussian Channels},

author = {Fernando Perez-Cruz and Miguel R D Rodrigues and Sergio Verdu},

url = {http://eprints.pascal-network.org/archive/00006754/},

year = {2009},

date = {2009-01-01},

booktitle = {Seminar PIIRS},

address = {Princeton},

abstract = {We investigate the linear precoding and power allocation policies that maximize the mutual information for general multiple-input multiple-output (MIMO) Gaussian channels with arbitrary input distributions, by capitalizing on the relationship between mutual information and minimum mean-square error. The optimal linear precoder satisfies a fixed-point equation as a function of the channel and the input constellation. For nonGaussian inputs, a nondiagonal precoding matrix in general increases the information transmission rate, even for parallel noninteracting channels. Whenever precoding is precluded, the optimal power allocation policy also satisfies a fixed-point equation; we put forth a generalization of the mercury/waterfilling algorithm, previously proposed for parallel noninterfering channels, in which the mercury level accounts not only for the nonGaussian input distributions, but also for the interference among inputs.},

keywords = {Theory \&amp; Algorithms},

pubstate = {published},

tppubtype = {inproceedings}

}

Miguez, Joaquin; Maiz, Cristina S; Djuric, Petar M; Crisan, Dan

Sequential Monte Carlo Optimization Using Artificial State-Space Models Artículo en actas

En: 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, pp. 268–273, IEEE, Marco Island, FL, 2009.

Resumen | Enlaces | BibTeX | Etiquetas: Acceleration, Cost function, Design optimization, discrete-time dynamical system, Educational institutions, Mathematics, maximum a posteriori estimate, maximum likelihood estimation, minimisation, Monte Carlo methods, Optimization methods, Probability distribution, sequential Monte Carlo optimization, Sequential optimization, Signal design, State-space methods, state-space model, Stochastic optimization

@inproceedings{Miguez2009,

title = {Sequential Monte Carlo Optimization Using Artificial State-Space Models},

author = {Joaquin Miguez and Cristina S Maiz and Petar M Djuric and Dan Crisan},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4785933},

year = {2009},

date = {2009-01-01},

booktitle = {2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop},

pages = {268--273},

publisher = {IEEE},

address = {Marco Island, FL},

abstract = {We introduce a method for sequential minimization of a certain class of (possibly non-convex) cost functions with respect to a high dimensional signal of interest. The proposed approach involves the transformation of the optimization problem into one of estimation in a discrete-time dynamical system. In particular, we describe a methodology for constructing an artificial state-space model which has the signal of interest as its unobserved dynamic state. The model is \"{a}dapted" to the cost function in the sense that the maximum a posteriori (MAP) estimate of the system state is also a global minimizer of the cost function. The advantage of the estimation framework is that we can draw from a pool of sequential Monte Carlo methods, for particle approximation of probability measures in dynamic systems, that enable the numerical computation of MAP estimates. We provide examples of how to apply the proposed methodology, including some illustrative simulation results.},

keywords = {Acceleration, Cost function, Design optimization, discrete-time dynamical system, Educational institutions, Mathematics, maximum a posteriori estimate, maximum likelihood estimation, minimisation, Monte Carlo methods, Optimization methods, Probability distribution, sequential Monte Carlo optimization, Sequential optimization, Signal design, State-space methods, state-space model, Stochastic optimization},

pubstate = {published},

tppubtype = {inproceedings}

}

Fresia, Maria; Perez-Cruz, Fernando; Poor, Vincent H; Verdu, Sergio

Joint Source-Channel Coding with Concatenated LDPC Codes Artículo en actas

En: Information Theory and Applications (ITA), San Diego, 2009.

Resumen | Enlaces | BibTeX | Etiquetas: Learning/Statistics &amp; Optimisation

@inproceedings{Fresia2009a,

title = {Joint Source-Channel Coding with Concatenated LDPC Codes},

author = {Maria Fresia and Fernando Perez-Cruz and Vincent H Poor and Sergio Verdu},

url = {http://eprints.pascal-network.org/archive/00004905/},

year = {2009},

date = {2009-01-01},

booktitle = {Information Theory and Applications (ITA)},

address = {San Diego},

abstract = {The separation principle, a milestone in information theory, establishes that for stationary sources and channels there is no loss of optimality when a channel-independent source encoder followed by a source-independent channel encoder are used to transmit the data, as the code length tends to infinity. Thereby, the source and channel encoding have been typically treated as independent problems. For finite-length codes, the separation principle does not hold and a joint encoder and decoder can potentially increase the achieved information transmission rate. In this paper, a scheme for joint source-channel coding based on low-density parity-check (LDPC) codes is presented. The source is compressed and protected with two concatenated LDPC codes and a joint belief propagation decoder is implemented. EXIT chart performance of the proposed schemes is studied. The results are verified with some illustrative experiments.},

keywords = {Learning/Statistics \&amp; Optimisation},

pubstate = {published},

tppubtype = {inproceedings}

}

Murillo-Fuentes, Juan Jose; Perez-Cruz, Fernando

Gaussian Process Regressors for Multiuser Detection in DS-CDMA Systems Artículo de revista

En: IEEE Transactions on Communications, vol. 57, no. 8, pp. 2339–2347, 2009, ISSN: 0090-6778.

Resumen | Enlaces | BibTeX | Etiquetas: analytical nonlinear multiuser detectors, code division multiple access, communication systems, Detectors, digital communication, digital communications, DS-CDMA systems, Gaussian process for regressi, Gaussian process regressors, Gaussian processes, GPR, Ground penetrating radar, least mean squares methods, maximum likelihood, maximum likelihood detection, maximum likelihood estimation, mean square error methods, minimum mean square error, MMSE, Multiaccess communication, Multiuser detection, nonlinear estimator, nonlinear state-ofthe- art solutions, radio receivers, Receivers, regression analysis, Support vector machines

@article{Murillo-Fuentes2009,

title = {Gaussian Process Regressors for Multiuser Detection in DS-CDMA Systems},

author = {Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5201027},

issn = {0090-6778},

year = {2009},

date = {2009-01-01},

journal = {IEEE Transactions on Communications},

volume = {57},

number = {8},

pages = {2339--2347},

abstract = {In this paper we present Gaussian processes for Regression (GPR) as a novel detector for CDMA digital communications. Particularly, we propose GPR for constructing analytical nonlinear multiuser detectors in CDMA communication systems. GPR can easily compute the parameters that describe its nonlinearities by maximum likelihood. Thereby, no cross-validation is needed, as it is typically used in nonlinear estimation procedures. The GPR solution is analytical, given its parameters, and it does not need to solve an optimization problem for building the nonlinear estimator. These properties provide fast and accurate learning, two major issues in digital communications. The GPR with a linear decision function can be understood as a regularized MMSE detector, in which the regularization parameter is optimally set. We also show the GPR receiver to be a straightforward nonlinear extension of the linear minimum mean square error (MMSE) criterion, widely used in the design of these receivers. We argue the benefits of this new approach in short codes CDMA systems where little information on the users' codes, users' amplitudes or the channel is available. The paper includes some experiments to show that GPR outperforms linear (MMSE) and nonlinear (SVM) state-ofthe- art solutions.},

keywords = {analytical nonlinear multiuser detectors, code division multiple access, communication systems, Detectors, digital communication, digital communications, DS-CDMA systems, Gaussian process for regressi, Gaussian process regressors, Gaussian processes, GPR, Ground penetrating radar, least mean squares methods, maximum likelihood, maximum likelihood detection, maximum likelihood estimation, mean square error methods, minimum mean square error, MMSE, Multiaccess communication, Multiuser detection, nonlinear estimator, nonlinear state-ofthe- art solutions, radio receivers, Receivers, regression analysis, Support vector machines},

pubstate = {published},

tppubtype = {article}

}

Mariño, Inés P.; Miguez, Joaquin; Meucci, Riccardo

Monte Carlo Method for Adaptively Estimating the Unknown Parameters and the Dynamic State of Chaotic Systems Artículo de revista

En: Physical Review E, vol. 79, no. 5, pp. 056218, 2009, ISSN: 1539-3755.

Resumen | Enlaces | BibTeX | Etiquetas:

@article{Marino2009,

title = {Monte Carlo Method for Adaptively Estimating the Unknown Parameters and the Dynamic State of Chaotic Systems},

author = {In\'{e}s P. Mari\~{n}o and Joaquin Miguez and Riccardo Meucci},

url = {http://link.aps.org/doi/10.1103/PhysRevE.79.056218},

issn = {1539-3755},

year = {2009},

date = {2009-01-01},

journal = {Physical Review E},

volume = {79},

number = {5},

pages = {056218},

publisher = {American Physical Society},

abstract = {We propose a Monte Carlo methodology for the joint estimation of unobserved dynamic variables and unknown static parameters in chaotic systems. The technique is sequential, i.e., it updates the variable and parameter estimates recursively as new observations become available, and, hence, suitable for online implementation. We demonstrate the validity of the method by way of two examples. In the first one, we tackle the estimation of all the dynamic variables and one unknown parameter of a five-dimensional nonlinear model using a time series of scalar observations experimentally collected from a chaotic CO2