| 1. |
- Elvander, Filip, et al.
(författare)
-
NON-COHERENT SENSOR FUSION VIA ENTROPY REGULARIZED OPTIMAL MASS TRANSPORT
- 2019
-
Ingår i: 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP). - : IEEE. - 9781479981311 ; , s. 4415-4419
-
Konferensbidrag (refereegranskat)abstract
- This work presents a method for information fusion in source localization applications. The method utilizes the concept of optimal mass transport in order to construct estimates of the spatial spectrum using a convex barycenter formulation. We introduce an entropy regularization term to the convex objective, which allows for low-complexity iterations of the solution algorithm and thus makes the proposed method applicable also to higher-dimensional problems. We illustrate the proposed method's inherent robustness to misalignment and miscalibration of the sensor arrays using numerical examples of localization in two dimensions.
|
|
| 2. |
- Adalbjörnsson, Stefan Ingi, et al.
(författare)
-
Estimating Periodicities in Symbolic Sequences Using Sparse Modeling
- 2015
-
Ingår i: IEEE Transactions on Signal Processing. - 1053-587X. ; 63:8, s. 2142-2150
-
Tidskriftsartikel (refereegranskat)abstract
- In this paper, we propose a method for estimating statistical periodicities in symbolic sequences. Different from other common approaches used for the estimation of periodicities of sequences of arbitrary, finite, symbol sets, that often map the symbolic sequence to a numerical representation, we here exploit a likelihood-based formulation in a sparse modeling framework to represent the periodic behavior of the sequence. The resulting criterion includes a restriction on the cardinality of the solution; two approximate solutions are suggested—one greedy and one using an iterative convex relaxation strategy to ease the cardinality restriction. The performance of the proposed methods are illustrated using both simulated and real DNA data, showing a notable performance gain as compared to other common estimators.
|
|
| 3. |
- Elvander, Filip, et al.
(författare)
-
Interpolation and Extrapolation of Toeplitz Matrices via Optimal Mass Transport
- 2018
-
Ingår i: IEEE Transactions on Signal Processing. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1053-587X .- 1941-0476. ; 66:20, s. 5285-5298
-
Tidskriftsartikel (refereegranskat)abstract
- In this work, we propose a novel method for quantifying distances between Toeplitz structured covariance matrices. By exploiting the spectral representation of Toeplitz matrices, the proposed distance measure is defined based on an optimal mass transport problem in the spectral domain. This may then be interpreted in the covariance domain, suggesting a natural way of interpolating and extrapolating Toeplitz matrices, such that the positive semi-definiteness and the Toeplitz structure of these matrices are preserved. The proposed distance measure is also shown to be contractive with respect to both additive and multiplicative noise, and thereby allows for a quantification of the decreased distance between signals when these are corrupted by noise. Finally, we illustrate how this approach can be used for several applications in signal processing. In particular, we consider interpolation and extrapolation of Toeplitz matrices, as well as clustering problems and tracking of slowly varying stochastic processes.
|
|
| 4. |
- Elvander, Filip, et al.
(författare)
-
Tracking and sensor fusion in direction of arrival estimation using optimal mass transport
- 2018
-
Ingår i: 2018 26th European Signal Processing Conference (EUSIPCO). - : European Signal Processing Conference, EUSIPCO. - 9789082797015 ; , s. 1617-1621
-
Konferensbidrag (refereegranskat)abstract
- In this work, we propose new methods for information fusion and tracking in direction of arrival (DOA) estimation by utilizing an optimal mass transport framework. Sensor array measurements in DOA estimation may not be consistent due to misalignments and calibration errors. By using optimal mass transport as a notion of distance for combining the information obtained from all the sensor arrays, we obtain an approach that can prevent aliasing and is robust to array misalignments. For the case of sequential tracking, the proposed method updates the DOA estimate using the new measurements and an optimal mass transport prior. In the case of sensor fusion, information from several, individual, sensor arrays is combined using a barycenter formulation of optimal mass transport.
|
|
| 5. |
- Glentis, G. -O, et al.
(författare)
-
Efficient spectral analysis in the missing data case using sparse ML methods
- 2014
-
Ingår i: European Signal Processing Conference. - 2219-5491.
-
Konferensbidrag (refereegranskat)abstract
- Given their wide applicability, several sparse high-resolution spectral estimation techniques and their implementation have been examined in the recent literature. In this work, we further the topic by examining a computationally efficient implementation of the recent SMLA algorithms in the missing data case. The work is an extension of our implementation for the uniformly sampled case, and offers a notable computational gain as compared to the alternative implementations in the missing data case.
|
|
| 6. |
- Elvander, Filip, et al.
(författare)
-
Multi-marginal optimal transport using partial information with applications in robust localization and sensor fusion
- 2020
-
Ingår i: Signal Processing. - : Elsevier BV. - 0165-1684 .- 1872-7557. ; 171
-
Tidskriftsartikel (refereegranskat)abstract
- During recent decades, there has been a substantial development in optimal mass transport theory and methods. In this work, we consider multi-marginal problems wherein only partial information of each marginal is available, a common setup in many inverse problems in, e.g., remote sensing and imaging. By considering an entropy regularized approximation of the original transport problem, we propose an algorithm corresponding to a block-coordinate ascent of the dual problem, where Newton’s algorithm is used to solve the sub-problems. In order to make this computationally tractable for large-scale settings, we utilize the tensor structure that arises in practical problems, allowing for computing projections of the multi-marginal transport plan using only matrix-vector operations of relatively small matrices. As illustrating examples, we apply the resulting method to tracking and barycenter problems in spatial spectral estimation. In particular, we show that the optimal mass transport framework allows for fusing information from different time steps, as well as from different sensor arrays, also when the sensor arrays are not jointly calibrated. Furthermore, we show that by incorporating knowledge of underlying dynamics in tracking scenarios, one may arrive at accurate spectral estimates, as well as faithful reconstructions of spectra corresponding to unobserved time points.
|
|
| 7. |
- Elvander, Filip, et al.
(författare)
-
USING OPTIMAL MASS TRANSPORT FOR TRACKING AND INTERPOLATION OF TOEPLITZ COVARIANCE MATRICES
- 2018
-
Ingår i: 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP). - : IEEE. - 9781538646588 ; , s. 4469-4473
-
Konferensbidrag (refereegranskat)abstract
- In this work, we propose a novel method for interpolation and extrapolation of Toeplitz structured covariance matrices. By considering a spectral representation of Toeplitz matrices, we use an optimal mass transport problem in the spectral domain in order to define a notion of distance between such matrices. The obtained optimal transport plan naturally induces a way of interpolating, as well as extrapolating, Toeplitz matrices. The constructed covariance matrix interpolants and extrapolants preserve the Toeplitz structure, as well as the positive semi-definiteness and the zeroth covariance of the original matrices. We demonstrate the proposed method's ability to model locally linear shifts of spectral power for slowly varying stochastic processes, illustrating the achievable performance using a simple tracking problem.
|
|
| 8. |
- Elvander, Filip, et al.
(författare)
-
Using optimal transport for estimating inharmonic pitch signals
- 2017
-
Ingår i: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509041176 ; , s. 331-335
-
Konferensbidrag (refereegranskat)abstract
- In this work, we propose a novel multi-pitch estimation technique that is robust with respect to the inharmonicity commonly occurring in many applications. The method does not require any a priori knowledge of the number of signal sources, the number of harmonics of each source, nor the structure or scope of any possibly occurring inharmonicity. Formulated as a minimum transport distance problem, the proposed method finds an estimate of the present pitches by mapping any found spectral line to the closest harmonic structure. The resulting optimization is a convex and highly tractable linear programming problem. The preferable performance of the proposed method is illustrated using both simulated and real audio signals.
|
|
| 9. |
- Adalbjörnsson, Stefan, et al.
(författare)
-
Conjugate priors for Gaussian emission plsa recommender systems
- 2016
-
Ingår i: 2016 24th European Signal Processing Conference, EUSIPCO 2016. - 9780992862657 ; 2016-November, s. 2096-2100
-
Konferensbidrag (refereegranskat)abstract
- Collaborative filtering for recommender systems seeks to learn and predict user preferences for a collection of items by identifying similarities between users on the basis of their past interest or interaction with the items in question. In this work, we present a conjugate prior regularized extension of Hofmann's Gaussian emission probabilistic latent semantic analysis model, able to overcome the over-fitting problem restricting the performance of the earlier formulation. Furthermore, in experiments using the EachMovie and MovieLens data sets, it is shown that the proposed regularized model achieves significantly improved prediction accuracy of user preferences as compared to the latent semantic analysis model without priors.
|
|
| 10. |
- Adalbjörnsson, Stefan Ingi, et al.
(författare)
-
Efficient Block and Time-Recursive Estimation of Sparse Volterra Systems
- 2012
-
Ingår i: 2012 IEEE Statistical Signal Processing Workshop (SSP), Proceedings of. - 9781467301831 ; , s. 173-176
-
Konferensbidrag (refereegranskat)abstract
- We investigate the application of non-convex penalized least squares for parameter estimation in the Volterra model. Sparsity is promoted by introducing a weighted !q penalty on the parameters and efficient batch and time recursive algorithms are devised based on the cyclic coordinate descent approach. Numerical examples illustrate the improved performance of the proposed algorithms as compared the weighted !1 norm.
|
|
