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Fast LASSO based DOA tracking

Ashkan Panahi (Institutionen för signaler och system, Signalbehandling) ; Mats Viberg (Institutionen för signaler och system, Signalbehandling)
4 th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2011 p. 397-400. (2011)
[Konferensbidrag, refereegranskat]

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.

Nyckelord: Bayesian estimations, Class of priors, DOA tracking, Estimation problem, Far field, Kalman filtering method, Linear sensor, Narrow band sources, Selection operators, Sequential filter, Simple random walk, Sparse approximations, Sparse representation, Wave forms, Bayesian networks, Sensor arrays, Optimization

Denna post skapades 2012-04-19. Senast ändrad 2015-05-08.
CPL Pubid: 156889


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Institutionen för signaler och system, Signalbehandling (1900-2017)



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