Fast LASSO based DOA tracking
Paper i proceeding, 2011

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.

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


Simple random walk


Ashkan Panahi

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Mats Viberg

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

4 th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2011

397-400 6136036
9781457721052 (ISBN)







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