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Stoica, Peter
Alternative names
Publications (10 of 363) 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.
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. 43Article in journal (Refereed) Epub ahead of print
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: 2017-12-02Bibliographically 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, 651-662 p.Article 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, 5305-5317 p.Article 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, 1264-1272 p.Conference 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, 3536-3546 p.Article 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.

Keyword
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, 1135-1143 p.Article 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, 390-402 p.Article in journal (Refereed) Published
Keyword
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, 204-211 p.Article 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
Gianelli, C., Xu, L., Li, J. & Stoica, P. (2016). One-bit compressive sampling with time-varying thresholds for sparse parameter estimation. In: Proc. 9th Sensor Array and Multichannel Signal Processing Workshop: . Paper presented at SAM 2016, July 10–13, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE.
Open this publication in new window or tab >>One-bit compressive sampling with time-varying thresholds for sparse parameter estimation
2016 (English)In: Proc. 9th Sensor Array and Multichannel Signal Processing Workshop, Piscataway, NJ: IEEE, 2016Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2016
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-315575 (URN)10.1109/SAM.2016.7569634 (DOI)978-1-5090-2103-1 (ISBN)
Conference
SAM 2016, July 10–13, Rio de Janeiro, Brazil
Available from: 2016-09-19 Created: 2017-02-15 Last updated: 2017-02-17Bibliographically approved
Zachariah, D., Jaldén, N. & Stoica, P. (2016). Online prediction of spatial fields for radio-frequency communication. In: Proc. 24th European Signal Processing Conference: . Paper presented at EUSIPCO 2016, August 29 – September 2, Budapest, Hungary (pp. 1252-1256). Piscataway, NJ: IEEE.
Open this publication in new window or tab >>Online prediction of spatial fields for radio-frequency communication
2016 (English)In: Proc. 24th European Signal Processing Conference, Piscataway, NJ: IEEE, 2016, 1252-1256 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we predict spatial wireless channel characteristics using a stochastic model that takes into account both distance dependent pathloss and random spatial variation due to fading. This information is valuable for resource allocation, interference management, design in wireless communication systems. The spatial field model is trained using a convex covariance-based learning method which can be implemented online. The resulting joint learning and prediction method is suitable for large-scale or streaming data. The online method is first demonstrated on a synthetic dataset which models pathloss and medium-scale fading. We compare the method with a state-of-the-art scalable batch method. It is subsequently tested in a real dataset to capture small-scale variations.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2016
Series
European Signal Processing Conference, ISSN 2076-1465
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-315572 (URN)10.1109/EUSIPCO.2016.7760449 (DOI)000391891900239 ()9780992862657 (ISBN)
Conference
EUSIPCO 2016, August 29 – September 2, Budapest, Hungary
Available from: 2016-12-01 Created: 2017-02-15 Last updated: 2017-02-27Bibliographically approved
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