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Fritsche, Carsten
Publikationer (10 of 44) Visa alla publikationer
Zhao, Y., Fritsche, C., Hendeby, G., Yin, F., Chen, T. & Gunnarsson, F. (2019). Cramér–Rao Bounds for Filtering Based on Gaussian Process State-Space Models. IEEE Transactions on Signal Processing, 67(23), 5936-5951
Öppna denna publikation i ny flik eller fönster >>Cramér–Rao Bounds for Filtering Based on Gaussian Process State-Space Models
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2019 (Engelska)Ingår i: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 67, nr 23, s. 5936-5951Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Posterior Cramér-Rao bounds (CRBs) are derived for the estimation performance of three Gaussian process-based state-space models. The parametric CRB is derived for the case with a parametric state transition and a Gaussian process-based measurement model. We illustrate the theory with a target tracking example and derive both parametric and posterior filtering CRBs for this specific application. Finally, the theory is illustrated with a positioning problem, with experimental data from an office environment where the obtained estimation performance is compared to the derived CRBs.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers (IEEE), 2019
Nyckelord
Cramér-Rao bound; Gaussian process; statespace model; nonlinear estimation
Nationell ämneskategori
Reglerteknik Signalbehandling
Identifikatorer
urn:nbn:se:liu:diva-162979 (URN)10.1109/TSP.2019.2949508 (DOI)000575512900002 ()2-s2.0-85077773756 (Scopus ID)
Forskningsfinansiär
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Anmärkning

Funding agencies: ELLIT - Swedish Government; European Union FP7 Marie Curie training program on Tracking in Complex Sensor Systems (TRAX) [607400]; Senion; Shenzhen Science and Technology Innovation Council [JCYJ20170307155957688, JCYJ20170411102101881]; National Natural 

Tillgänglig från: 2020-01-03 Skapad: 2020-01-03 Senast uppdaterad: 2021-07-29Bibliografiskt granskad
Bacharach, L., Fritsche, C., Orguner, U. & Chaumette, E. (2019). Some Inequalities Between Pairs of Marginal and Joint Bayesian Lower Bounds. In: 2019 22th 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).
Öppna denna publikation i ny flik eller fönster >>Some Inequalities Between Pairs of Marginal and Joint Bayesian Lower Bounds
2019 (Engelska)Ingår i: 2019 22th International Conference on Information Fusion (FUSION), 2019, s. 1-8Konferensbidrag, Publicerat paper (Refereegranskat)
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

Nyckelord
Bayesian lower bounds, marginal probability density function, joint probability density function, Bayesian linear regression
Nationell ämneskategori
Signalbehandling
Identifikatorer
urn:nbn:se:liu:diva-159989 (URN)000567728800285 ()978-0-9964527-8-6 (ISBN)978-1-7281-1840-6 (ISBN)
Konferens
22nd International Conference on Information Fusion (FUSION), Ottawa, Canada, July 2-5, 2019
Forskningsfinansiär
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Anmärkning

Funding agencies: DGA/AID [2018.60.0072.00.470.75.01]; Excellence Center at Linkoping and Lund in Information Technology (ELLIIT)

Tillgänglig från: 2019-08-31 Skapad: 2019-08-31 Senast uppdaterad: 2020-09-26Bibliografiskt granskad
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. 159-165).
Öppna denna publikation i ny flik eller fönster >>A General Class of Bayesian Lower Bounds Tighter than the Weiss-Weinstein Family
2018 (Engelska)Ingår i: 2018 21th International Conference on Information Fusion (FUSION), 2018, s. 159-165Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Nyckelord
Bayesian lower bounds, Bayesian estimation, Weiss-Weinstein family, a-posteriori probability density function
Nationell ämneskategori
Signalbehandling
Identifikatorer
urn:nbn:se:liu:diva-151670 (URN)10.23919/ICIF.2018.8455577 (DOI)000495071900021 ()978-0-9964-5276-2 (ISBN)
Konferens
2018 21st International Conference on Information Fusion (FUSION), Cambridge, UK, 2018
Anmärkning

Funding agencies: DGA/MRIS [2015.60.0090.00.470.75.01]

Tillgänglig från: 2018-09-29 Skapad: 2019-12-06 Senast uppdaterad: 2019-12-06
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).
Öppna denna publikation i ny flik eller fönster >>Bobrovsky-Zakai Bound for Filtering, Prediction and Smoothing of Nonlinear Dynamic Systems
2018 (Engelska)Ingår i: 2018 21st International Conference on Information Fusion (FUSION), 2018, s. 1-8Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Nyckelord
Bayesian lower bounds, Bobrovsky-Zakai bound, filtering, prediction, smoothing, nonlinear dynamic systems
Nationell ämneskategori
Signalbehandling
Identifikatorer
urn:nbn:se:liu:diva-151671 (URN)10.23919/ICIF.2018.8455541 (DOI)000495071900023 ()
Konferens
2018 21st International Conference on Information Fusion (FUSION), Cambrdige, UK, 2018
Forskningsfinansiär
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Anmärkning

Funding agencies: Linkoping-Lund Initiative on IT and Mobile Communications (ELLIT)

Tillgänglig från: 2018-09-29 Skapad: 2018-09-29 Senast uppdaterad: 2019-12-06
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
Öppna denna publikation i ny flik eller fönster >>Marginal Bayesian Bhattacharyya Bounds for discrete-time filtering
2018 (Engelska)Ingår i: Proc. of 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada, 2018, IEEE, 2018, s. 4289-4293Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
IEEE, 2018
Serie
IEEE International Conference on Acoustics, Speech and Signal Processing
Nyckelord
Performance bounds, Bayesian estimation, Bhattacharyya bounds, nonlinear filtering, particle filter
Nationell ämneskategori
Signalbehandling
Identifikatorer
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)
Konferens
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 10-20 April, Calgary, Canada, 2018
Forskningsfinansiär
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Anmärkning

Funding agencies: ELLIIT

Tillgänglig från: 2018-06-08 Skapad: 2018-06-08 Senast uppdaterad: 2019-06-19Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>Sequential Monte Carlo Methods and Theoretical Bounds for Proximity Report Based Indoor Positioning
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2018 (Engelska)Ingår i: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 67, nr 6, s. 5372-5386Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers (IEEE), 2018
Nyckelord
Proximity, indoor positioning, particle filtering and smoothing, Cramer-Rao lower bounds
Nationell ämneskategori
Signalbehandling
Identifikatorer
urn:nbn:se:liu:diva-147834 (URN)10.1109/TVT.2018.2799174 (DOI)000435553400053 ()2-s2.0-85041415767 (Scopus ID)
Anmärkning

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]

Tillgänglig från: 2018-05-15 Skapad: 2018-05-15 Senast uppdaterad: 2019-02-12Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>Supplementary Material for “Bobrovsky-Zakai Bound for Filtering, Prediction and Smoothing of Nonlinear Dynamic Systems”
2018 (Engelska)Rapport (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Linköping: Linköping University Electronic Press, 2018. s. 27
Serie
LiTH-ISY-R, ISSN 1400-3902 ; 3105
Nyckelord
Performance bounds, nonlinear dynamic systems, mean square error
Nationell ämneskategori
Signalbehandling Reglerteknik
Identifikatorer
urn:nbn:se:liu:diva-149450 (URN)LiTH-ISY-R-3105 (ISRN)
Forskningsfinansiär
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Tillgänglig från: 2018-07-01 Skapad: 2018-07-01 Senast uppdaterad: 2018-07-02Bibliografiskt granskad
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).
Öppna denna publikation i ny flik eller fönster >>Bayesian Bhattacharyya bound for discrete-time filtering revisited
2017 (Engelska)Ingår i: Proc. of 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017, s. 719-723Konferensbidrag, Publicerat paper (Refereegranskat)
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

Nyckelord
Bhattacharyya bound, nonlinear filtering, mean square error inequality
Nationell ämneskategori
Signalbehandling
Identifikatorer
urn:nbn:se:liu:diva-144022 (URN)10.1109/CAMSAP.2017.8313201 (DOI)000428438100145 ()9781538612514 (ISBN)9781538612507 (ISBN)9781538612521 (ISBN)
Konferens
2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Curacao, Dutch Antilles, Dec. 10-13, 2017
Forskningsfinansiär
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Tillgänglig från: 2018-01-03 Skapad: 2018-01-03 Senast uppdaterad: 2021-07-15Bibliografiskt granskad
Fritsche, C. (2017). Derivation of a Bayesian Bhattacharyya bound for discrete-time filtering. Linköping: Linköping University Electronic Press
Öppna denna publikation i ny flik eller fönster >>Derivation of a Bayesian Bhattacharyya bound for discrete-time filtering
2017 (Engelska)Rapport (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Linköping: Linköping University Electronic Press, 2017. s. 33
Serie
LiTH-ISY-R, ISSN 1400-3902 ; 3099
Nyckelord
Bayesian bounds, Bhattacharyya bounds, nonlinear filtering, state estimation
Nationell ämneskategori
Signalbehandling
Identifikatorer
urn:nbn:se:liu:diva-139162 (URN)LiTH-ISY-R-3099 (ISRN)
Forskningsfinansiär
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Tillgänglig från: 2017-07-03 Skapad: 2017-07-03 Senast uppdaterad: 2018-06-12Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>Rapid System Identification for Jump Markov Non-Linear Systems
2017 (Engelska)Ingår i: Proc. 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), IEEE, 2017, s. 714-718Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
IEEE, 2017
Nyckelord
parameter estimation, system indentification, jump Markov systems, particle filtering
Nationell ämneskategori
Signalbehandling
Identifikatorer
urn:nbn:se:liu:diva-144023 (URN)10.1109/CAMSAP.2017.8313089 (DOI)000428438100033 ()9781538612514 (ISBN)9781538612507 (ISBN)9781538612521 (ISBN)
Konferens
2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Curacao, Dutch Antilles, Dec. 10-13, 2017
Projekt
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Tillgänglig från: 2018-01-03 Skapad: 2018-01-03 Senast uppdaterad: 2018-07-06Bibliografiskt granskad
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