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Fritsche, Carsten
Publications (10 of 44) Show all publications
Bacharach, L., Fritsche, C., Orguner, U. & Chaumette, E. (2019). A Tighter Bayesian Cramer-Rao Bound. In: Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): . Paper presented at IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, May 12-17, 2019 (pp. 5277-5281).
Open this publication in new window or tab >>A Tighter Bayesian Cramer-Rao Bound
2019 (English)In: Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019, p. 5277-5281Conference paper, Published paper (Refereed)
Abstract [en]

It has been shown lately that any ”standard” Bayesian lower bound (BLB) on the mean squared error (MSE) of the Weiss-Weinstein family (WWF) admits a ”tighter” form which upper bounds the ”standard” form. Applied to the Bayesian Cramer-Rao bound (BCRB), this result suggests to redefine the concept of efficient estimator relatively to the tighter form of the BCRB, an update supported by a noteworthy example. This paper lays the foundation to revisit some Bayesian estimation problems where the BCRB is not tight in the asymptotic region.

Keywords
Mean Squared Error, Bayesian Lower Bounds, Bayesian Cramer-Rao bound, Minimum Mean Squared Error
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-159990 (URN)
Conference
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, May 12-17, 2019
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2019-09-01 Created: 2019-09-01 Last updated: 2019-09-04Bibliographically approved
Bacharach, L., Fritsche, C., Orguner, U. & Chaumette, E. (2019). Some Inequalities Between Pairs of Marginal and Joint Bayesian Lower Bounds. In: Proc. of 22nd International Conference on Information Fusion (FUSION): . Paper presented at 22nd International Conference on Information Fusion (FUSION), Ottawa, Canada, July 2-5, 2019 (pp. 1-8).
Open this publication in new window or tab >>Some Inequalities Between Pairs of Marginal and Joint Bayesian Lower Bounds
2019 (English)In: Proc. of 22nd International Conference on Information Fusion (FUSION), 2019, p. 1-8Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, tightness relations (or inequalities) between Bayesian lower bounds (BLBs) on the mean-squared-error are derived which result from the marginalization of a joint probability density function (pdf) depending on both parameters of interest and extraneous or nuisance parameters. In particular,it is shown that for a large class of BLBs, the BLB derived from the marginal pdf is at least as tight as the corresponding BLB derived from the joint pdf. A Bayesian linear regression example is used to illustrate the tightness relations

Keywords
Bayesian lower bounds, marginal probability density function, joint probability density function, Bayesian linear regression
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-159989 (URN)
Conference
22nd International Conference on Information Fusion (FUSION), Ottawa, Canada, July 2-5, 2019
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2019-08-31 Created: 2019-08-31 Last updated: 2019-09-04Bibliographically approved
Chaumette, E. & Fritsche, C. (2018). A General Class of Bayesian Lower Bounds Tighter than the Weiss-Weinstein Family. In: 2018 21th International Conference on Information Fusion (FUSION): . Paper presented at 2018 21st International Conference on Information Fusion (FUSION), Cambridge, UK, 2018 (pp. 1-7).
Open this publication in new window or tab >>A General Class of Bayesian Lower Bounds Tighter than the Weiss-Weinstein Family
2018 (English)In: 2018 21th International Conference on Information Fusion (FUSION), 2018, p. 1-7Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, Bayesian lower bounds (BLBs) are obtained via a general form of the Pythagorean theorem where the inner product derives from the joint or the a-posteriori probability density function (pdf). When joint pdf is considered, the BLBs obtained encompass the Weiss-Weinstein family (WWF). When a-posteriori pdf is considered, by resorting to an embedding between two ad hoc subspaces, it is shown that any ”standard” BLBs of the WWF admits a ”tighter” form which upper bounds the ”standard” form. Interestingly enough, this latter result may explain why the ”standard” BLBs of the WWF are not always as tight as expected, as exemplified in the case of the Bayesian Cram´er-Rao Bound. As a consequence an updated definition of efficiency is proposed, as well as the introduction of an updated class of efficient estimators.

Keywords
Bayesian lower bounds, Bayesian estimation, Weiss-Weinstein family, a-posteriori probability density function
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-151670 (URN)
Conference
2018 21st International Conference on Information Fusion (FUSION), Cambridge, UK, 2018
Available from: 2018-09-29 Created: 2018-09-29 Last updated: 2018-10-22
Fritsche, C., Orguner, U. & Gustafsson, F. (2018). Bobrovsky-Zakai Bound for Filtering, Prediction and Smoothing of Nonlinear Dynamic Systems. In: 2018 21st International Conference on Information Fusion (FUSION): . Paper presented at 2018 21st International Conference on Information Fusion (FUSION), Cambrdige, UK, 2018 (pp. 1-8).
Open this publication in new window or tab >>Bobrovsky-Zakai Bound for Filtering, Prediction and Smoothing of Nonlinear Dynamic Systems
2018 (English)In: 2018 21st International Conference on Information Fusion (FUSION), 2018, p. 1-8Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, recursive Bobrovsky-Zakai bounds for filtering, prediction and smoothing of nonlinear dynamic systems are presented. The similarities and differences to an existing Bobrovsky-Zakai bound in the literature for the filtering case are highlighted. The tightness of the derived bounds are illustrated on a simple example where a linear system with non-Gaussian measurement likelihood is considered. The proposed bounds are also compared with the performance of some well known filters/predictors/smoothers and other Bayesian bounds.

Keywords
Bayesian lower bounds, Bobrovsky-Zakai bound, filtering, prediction, smoothing, nonlinear dynamic systems
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-151671 (URN)
Conference
2018 21st International Conference on Information Fusion (FUSION), Cambrdige, UK, 2018
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2018-09-29 Created: 2018-09-29 Last updated: 2018-10-22
Fritsche, C., Orguner, U., Özkan, E. & Gustafsson, F. (2018). Marginal Bayesian Bhattacharyya Bounds for discrete-time filtering. In: Proc. of 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada, 2018: . Paper presented at IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 10-20 April, Calgary, Canada, 2018 (pp. 4289-4293). IEEE
Open this publication in new window or tab >>Marginal Bayesian Bhattacharyya Bounds for discrete-time filtering
2018 (English)In: Proc. of 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada, 2018, IEEE, 2018, p. 4289-4293Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, marginal versions of the Bayesian Bhattacharyya lower bound (BBLB), which is a tighter alternative to the classical Bayesian Cramer-Rao bound, for discrete-time filtering are proposed. Expressions for the second and third-order marginal BBLBs are obtained and it is shown how these can be approximately calculated using particle filtering. A simulation example shows that the proposed bounds predict the achievable performance of the filtering algorithms better.

Place, publisher, year, edition, pages
IEEE, 2018
Series
IEEE International Conference on Acoustics, Speech and Signal Processing
Keywords
Performance bounds, Bayesian estimation, Bhattacharyya bounds, nonlinear filtering, particle filter
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-148405 (URN)10.1109/ICASSP.2018.8462163 (DOI)000446384604091 ()978-1-5386-4659-5 (ISBN)978-1-5386-4658-8 (ISBN)
Conference
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 10-20 April, Calgary, Canada, 2018
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Note

Funding agencies: ELLIIT

Available from: 2018-06-08 Created: 2018-06-08 Last updated: 2019-06-19Bibliographically approved
Zhao, Y., Fritsche, C., Yin, F., Gunnarsson, F. & Gustafsson, F. (2018). Sequential Monte Carlo Methods and Theoretical Bounds for Proximity Report Based Indoor Positioning. IEEE Transactions on Vehicular Technology, 67(6), 5372-5386
Open this publication in new window or tab >>Sequential Monte Carlo Methods and Theoretical Bounds for Proximity Report Based Indoor Positioning
Show others...
2018 (English)In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 67, no 6, p. 5372-5386Article in journal (Refereed) Published
Abstract [en]

The commercial interest in proximity services is increasing. Application examples include location-based information and advertisements, logistics, social networking, file sharing, etc. In this paper, we consider positioning of devices based on a time series of proximity reports from a mobile device to a network node. This corresponds to nonlinear measurements with respect to the device position in relation to the network nodes. Motion model will be needed together with the measurements to determine the position of the device. Therefore, sequential Monte Carlo methods, namely particle filtering and smoothing, are applicable for positioning. Positioning performance is evaluated in a typical office area with Bluetooth-low-energy beacons deployed for proximity detection and report, and is further compared to parametric Cramér-Rao lower bounds. Finally, the position accuracy is also evaluated with real experimental data.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Keywords
Proximity, indoor positioning, particle filtering and smoothing, Cramer-Rao lower bounds
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-147834 (URN)10.1109/TVT.2018.2799174 (DOI)000435553400053 ()2-s2.0-85041415767 (Scopus ID)
Note

Funding agencies: European Union FP7 Marie Curie Training Programme on Tracking in Complex Sensor Systems (TRAX) [607400]; NSFC [61701426]; Shenzhen Science and Technology Innovation Council [JCYJ20170307155957688, JCYJ20170411102101881]

Available from: 2018-05-15 Created: 2018-05-15 Last updated: 2019-02-12Bibliographically approved
Fritsche, C. & Orguner, U. (2018). Supplementary Material for “Bobrovsky-Zakai Bound for Filtering, Prediction and Smoothing of Nonlinear Dynamic Systems”. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Supplementary Material for “Bobrovsky-Zakai Bound for Filtering, Prediction and Smoothing of Nonlinear Dynamic Systems”
2018 (English)Report (Other academic)
Abstract [en]

This report contains supplementary material for the paper [1], and gives detailed proofs of all lemmas and theorems that could not be included into the paper due to space limitations. The notation is adapted from the paper.

[1] C. Fritsche, U. Orguner, and F. Gustafsson, “Bobrovsky-Zakai bound for filtering, prediction and smoothing ofnonlinear dynamic systems,” in International Conference on Information Fusion (FUSION), Cambridge, UK, Jul.2018, pp. 1–8.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2018. p. 27
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3105
Keywords
Performance bounds, nonlinear dynamic systems, mean square error
National Category
Signal Processing Control Engineering
Identifiers
urn:nbn:se:liu:diva-149450 (URN)LiTH-ISY-R-3105 (ISRN)
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2018-07-01 Created: 2018-07-01 Last updated: 2018-07-02Bibliographically approved
Fritsche, C. & Gustafsson, F. (2017). Bayesian Bhattacharyya bound for discrete-time filtering revisited. In: Proc. of 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP): . Paper presented at 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Curacao, Dutch Antilles, Dec. 10-13, 2017 (pp. 719-723).
Open this publication in new window or tab >>Bayesian Bhattacharyya bound for discrete-time filtering revisited
2017 (English)In: Proc. of 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017, p. 719-723Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, the derivation of the Bayesian Bhattacharyya bound for discrete-time filtering as proposed ina paper by Reece and Nicholson is revisited. It turns out that the results presented in the aforementioned contribution are incorrect, as some expectations appearing in the information matrix recursions are missing. This paper gives a generalized derivation of the N-th order Bayesian Bhattacharyya bound and presents corrected expressions for the case N = 2. A nonlinear toy example is used to illustrate the results

Keywords
Bhattacharyya bound, nonlinear filtering, mean square error inequality
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-144022 (URN)10.1109/CAMSAP.2017.8313201 (DOI)000428438100145 ()9781538612514 (ISBN)9781538612507 (ISBN)9781538612521 (ISBN)
Conference
2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Curacao, Dutch Antilles, Dec. 10-13, 2017
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2018-01-03 Created: 2018-01-03 Last updated: 2018-06-11Bibliographically approved
Fritsche, C. (2017). Derivation of a Bayesian Bhattacharyya bound for discrete-time filtering. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Derivation of a Bayesian Bhattacharyya bound for discrete-time filtering
2017 (English)Report (Other academic)
Abstract [en]

In this report, the derivation of the Bayesian Bhattacharyya bound for discrete-time filtering as proposed by Reece and Nicholson [1] is revisited. It turns out that the general results presented in [1] are incorrect, as some expectations appearing in the information matrix recursions are missing. This report presents the corrected results and it is argued that the missing expectations are only zero in a number of special cases. A nonlinear toy example is used to illustrate when this is not the case.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2017. p. 33
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3099
Keywords
Bayesian bounds, Bhattacharyya bounds, nonlinear filtering, state estimation
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-139162 (URN)LiTH-ISY-R-3099 (ISRN)
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2017-07-03 Created: 2017-07-03 Last updated: 2018-06-12Bibliographically approved
Braga, A. R., Fritsche, C., Bruno, M. G. S. & Gustafsson, F. (2017). Rapid System Identification for Jump Markov Non-Linear Systems. In: Proc. 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP): . Paper presented at 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Curacao, Dutch Antilles, Dec. 10-13, 2017 (pp. 714-718). IEEE
Open this publication in new window or tab >>Rapid System Identification for Jump Markov Non-Linear Systems
2017 (English)In: Proc. 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), IEEE, 2017, p. 714-718Conference paper, Published paper (Refereed)
Abstract [en]

This work evaluates a previously introduced algorithm called Particle-Based Rapid Incremental Smoother within the framework of state inference and parameter identification in Jump Markov Non-Linear System. It is applied to the recursive form of two well-known Maximum Likelihood based algorithms who face the common challenge of online computation of smoothed additive functionals in order to accomplish the task of model parameter estimation. This work extends our previous contributions on identification of Markovian switching systems with the goal to reduce the computational complexity. A benchmark problem is used to illustrate the results.

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
parameter estimation, system indentification, jump Markov systems, particle filtering
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-144023 (URN)10.1109/CAMSAP.2017.8313089 (DOI)000428438100033 ()9781538612514 (ISBN)9781538612507 (ISBN)9781538612521 (ISBN)
Conference
2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Curacao, Dutch Antilles, Dec. 10-13, 2017
Projects
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2018-01-03 Created: 2018-01-03 Last updated: 2018-07-06Bibliographically approved
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