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Fast LASSO based DO...
Fast LASSO based DOA tracking
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- Panahi, Ashkan, 1986 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Viberg, Mats, 1961 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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(creator_code:org_t)
- ISBN 9781457721052
- 2011
- 2011
- Engelska.
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Ingår i: 4 th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2011. - 9781457721052 ; , s. 397-400
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- In this paper, we propose a sequential, fast DOA tracking technique using the measurements of a uniform linear sensor array in the far field of a set of narrow band sources. Our approach is based on sparse approximation technique LASSO (Least Absolute Shrincage and Selection Operator), which has recently gained considerable interest for DOA and other estimation problems. Considering the LASSO optimization as a Bayesian estimation, we first define a class of prior distributions suitable for the sparse representation of the model and discuss its relation to the priors over DOAs and waveforms. Inspired by the Kalman filtering method, we introduce a nonlinear sequential filter on this family of distributions. We derive the filter for a simple random walk motion model of the DOAs. The method consists of consecutive implementation of weighted LASSO optimizations using each new measurement and updating the LASSO weights for the next step.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
Nyckelord
- Narrow band sources
- Selection operators
- Sequential filter
- Far field
- Kalman filtering method
- Bayesian networks
- Sparse representation
- Bayesian estimations
- Wave forms
- DOA tracking
- Estimation problem
- Sensor arrays
- Sparse approximations
- Linear sensor
- Class of priors
- Optimization
- Simple random walk
Publikations- och innehållstyp
- kon (ämneskategori)
- ref (ämneskategori)
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