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Stoica, Peter
Alternative names
Publications (10 of 385) Show all publications
Das, A., Zachariah, D. & Stoica, P. (2018). Comparison of two hyperparameter-free sparse signal processing methods for direction-of-arrival tracking in the HF97 ocean acoustic experiment. IEEE Journal of Oceanic Engineering, 43(3), 725-734
Open this publication in new window or tab >>Comparison of two hyperparameter-free sparse signal processing methods for direction-of-arrival tracking in the HF97 ocean acoustic experiment
2018 (English)In: IEEE Journal of Oceanic Engineering, ISSN 0364-9059, E-ISSN 1558-1691, Vol. 43, no 3, p. 725-734Article in journal (Refereed) Published
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-335107 (URN)10.1109/JOE.2017.2706100 (DOI)
Available from: 2017-06-06 Created: 2017-11-30 Last updated: 2018-07-13Bibliographically approved
Mattsson, P., Zachariah, D. & Stoica, P. (2018). Identification of cascade water tanks using a PWARX model. Mechanical systems and signal processing, 106, 40-48
Open this publication in new window or tab >>Identification of cascade water tanks using a PWARX model
2018 (English)In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 106, p. 40-48Article in journal (Refereed) Published
Abstract [en]

In this paper we consider the identification of a discrete-time nonlinear dynamical model for a cascade water tank process. The proposed method starts with a nominal linear dynamical model of the system, and proceeds to model its prediction errors using a model that is piecewise affine in the data. As data is observed, the nominal model is refined into a piecewise ARX model which can capture a wide range of nonlinearities, such as the saturation in the cascade tanks. The proposed method uses a likelihood-based methodology which adaptively penalizes model complexity and directly leads to a computationally efficient implementation.

Place, publisher, year, edition, pages
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD, 2018
Keywords
System identification, Nonlinear systems, Piecewise ARX models
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-350267 (URN)10.1016/j.ymssp.2017.12.027 (DOI)000426229100004 ()
Funder
Swedish Research Council, 621-2014-5874, 2016-06079
Available from: 2018-05-14 Created: 2018-05-14 Last updated: 2018-05-14Bibliographically approved
Liang, J., Stoica, P., Jing, Y. & Li, J. (2018). Phase retrieval via the alternating direction method of multipliers. IEEE Signal Processing Letters, 25(1), 5-9
Open this publication in new window or tab >>Phase retrieval via the alternating direction method of multipliers
2018 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 25, no 1, p. 5-9Article in journal (Refereed) Published
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-342196 (URN)10.1109/LSP.2017.2767826 (DOI)000415190400001 ()
Available from: 2017-10-30 Created: 2018-02-20 Last updated: 2018-03-03Bibliographically approved
Hu, H., Soltanalian, M., Stoica, P. & Zhu, X. (2017). Locating the Few: Sparsity-aware waveform design for active radar. IEEE Transactions on Signal Processing, 65(3), 651-662
Open this publication in new window or tab >>Locating the Few: Sparsity-aware waveform design for active radar
2017 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 65, no 3, p. 651-662Article in journal (Refereed) Published
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-315055 (URN)10.1109/TSP.2016.2620966 (DOI)000391293800009 ()
Funder
EU, European Research CouncilSwedish Research Council
Available from: 2016-10-25 Created: 2017-03-08 Last updated: 2017-11-29Bibliographically approved
Carotenuto, V., De Maio, A., Orlando, D. & Stoica, P. (2017). Model order selection rules for covariance structure classification in radar. IEEE Transactions on Signal Processing, 65(20), 5305-5317
Open this publication in new window or tab >>Model order selection rules for covariance structure classification in radar
2017 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 65, no 20, p. 5305-5317Article in journal (Refereed) Published
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-333737 (URN)10.1109/TSP.2017.2728523 (DOI)000407465900004 ()
Available from: 2017-07-17 Created: 2017-11-20 Last updated: 2017-11-25Bibliographically approved
Wågberg, J., Zachariah, D., Schön, T. B. & Stoica, P. (2017). Prediction Performance After Learning in Gaussian Process Regression. In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics: . Paper presented at 20th International Conference on Artificial Intelligence and Statistics (pp. 1264-1272). PMLR, 54
Open this publication in new window or tab >>Prediction Performance After Learning in Gaussian Process Regression
2017 (English)In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR , 2017, Vol. 54, p. 1264-1272Conference paper, Published paper (Refereed)
Abstract [en]

This paper considers the quantification of the prediction performance in Gaussian process regression. The standard approach is to base the prediction error bars on the theoretical predictive variance, which is a lower bound on the mean square-error (MSE). This approach, however, does not take into account that the statistical model is learned from the data. We show that this omission leads to a systematic underestimation of the prediction errors. Starting from a generalization of the Cramér-Rao bound, we derive a more accurate MSE bound which provides a measure of uncertainty for prediction of Gaussian processes. The improved bound is easily computed and we illustrate it using synthetic and real data examples.

Place, publisher, year, edition, pages
PMLR, 2017
Series
Proceedings of Machine Learning Research, ISSN 1938-7228
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-324975 (URN)
Conference
20th International Conference on Artificial Intelligence and Statistics
Funder
Swedish Research Council, 621-2013-5524Swedish Research Council, 621-2014-5874Swedish Foundation for Strategic Research , RIT15-0012
Available from: 2017-06-20 Created: 2017-06-20 Last updated: 2017-06-20Bibliographically approved
Zachariah, D., Dwivedi, S., Handel, P. & Stoica, P. (2017). Scalable and Passive Wireless Network Clock Synchronization in LOS Environments. IEEE Transactions on Wireless Communications, 16(6), 3536-3546
Open this publication in new window or tab >>Scalable and Passive Wireless Network Clock Synchronization in LOS Environments
2017 (English)In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 16, no 6, p. 3536-3546Article in journal (Refereed) Published
Abstract [en]

Clock synchronization is ubiquitous in wireless systems for communication, sensing, and control. In this paper, we design a scalable system in which an indefinite number of passively receiving wireless units can synchronize to a single master clock at the level of discrete clock ticks. Accurate synchronization requires an estimate of the node positions to compensate the time-of-flight transmission delay in line-of-sight environments. If such information is available, the framework developed here takes position uncertainties into account. In the absence of such information, as in indoor scenarios, we propose an auxiliary localization mechanism. Furthermore, we derive the Cramer-Rao bounds for the system, which show that it enables synchronization accuracy at sub-nanosecond levels. Finally, we develop and evaluate an online estimation method, which is statistically efficient.

Keywords
Wireless time synchronization, wireless localization, nanosecond accuracy
National Category
Communication Systems Telecommunications
Identifiers
urn:nbn:se:uu:diva-329726 (URN)10.1109/TWC.2017.2683486 (DOI)000403495400035 ()
Funder
Swedish Research Council, 621-2014-5874Swedish Research Council, 2016-06079
Available from: 2017-09-21 Created: 2017-09-21 Last updated: 2017-09-21Bibliographically approved
Soltanalian, M., Naghsh, M. M., Shariati, N., Stoica, P. & Hassibi, B. (2017). Training signal design for correlated massive MIMO channel estimation. IEEE Transactions on Wireless Communications, 16(2), 1135-1143
Open this publication in new window or tab >>Training signal design for correlated massive MIMO channel estimation
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2017 (English)In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 16, no 2, p. 1135-1143Article in journal (Refereed) Published
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-319112 (URN)10.1109/TWC.2016.2639485 (DOI)000395825200036 ()
Available from: 2016-12-14 Created: 2017-04-03 Last updated: 2017-11-29Bibliographically approved
Björk, M., Zachariah, D., Kullberg, J. & Stoica, P. (2016). A multicomponent T2 relaxometry algorithm for myelin water imaging of the brain. Magnetic Resonance in Medicine, 75(1), 390-402
Open this publication in new window or tab >>A multicomponent T2 relaxometry algorithm for myelin water imaging of the brain
2016 (English)In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 75, no 1, p. 390-402Article in journal (Refereed) Published
Keywords
multicomponent T2 relaxometry, estimation algorithm, myelin water fraction, in vivo brain
National Category
Signal Processing Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:uu:diva-237160 (URN)10.1002/mrm.25583 (DOI)000367739200040 ()25604436 (PubMedID)
Projects
SysTEAM (ERC)
Funder
EU, European Research Council, 247035
Available from: 2015-01-21 Created: 2014-11-28 Last updated: 2017-12-05Bibliographically approved
Surana, K., Mitra, S., Mitra, A. & Stoica, P. (2016). Estimating the order of sinusoidal models using the adaptively penalized likelihood approach: Large sample consistency properties. Signal Processing, 128, 204-211
Open this publication in new window or tab >>Estimating the order of sinusoidal models using the adaptively penalized likelihood approach: Large sample consistency properties
2016 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 128, p. 204-211Article in journal (Refereed) Published
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-300425 (URN)10.1016/j.sigpro.2016.04.001 (DOI)000379706500021 ()
Funder
Swedish Research CouncilEU, European Research Council
Available from: 2016-04-06 Created: 2016-08-09 Last updated: 2017-11-28Bibliographically approved
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