Fast LASSO based DOA tracking
Paper in 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
Optimization
Simple random walk
Author
Ashkan Panahi
Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering
Mats Viberg
Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering
4 th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2011
397-400 6136036
9781457721052 (ISBN)
Subject Categories
Signal Processing
DOI
10.1109/CAMSAP.2011.6136036
ISBN
9781457721052