Experimental Validation of Wideband SBL Models for DOA Estimation

Pandey, Ruchi; Nannuru, Santosh; Gerstoft, Peter

doi:10.23919/eusipco55093.2022.9909606

Cited by 2 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We extend the TL-CBF and TL-OMP to wideband observations by summing the spectrum across frequencies in ( 8) and (10). We do not include wideband 18,22 TL-SBL due to its high computational complexity. We examine the performance by increasing the number of frequencies processed as F = 1, 3, 5, and 7 with corresponding frequencies 1600, {1400, 1600, 1800}, {1000, 1200, 1400, 1600, 1800}, and {1000, 1200, 1400, 1600, 1800, 2000, 2200}.…”

Section: H Wideband Processingmentioning

confidence: 99%

See 1 more Smart Citation

Parametric Models for Doa Trajectory Localization

Pandey

Nannuru

2022

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

Directions of arrival (DOA) estimation or localization of sources is an important problem in many applications for which numerous algorithms have been proposed. Most localization methods use block-level processing that combines multiple data snapshots to estimate DOA within a block. The DOAs are assumed to be constant within the block duration. However, these assumptions are often violated due to source motion. In this paper, we propose a signal model that captures the linear variations in DOA within a block. We applied conventional beamforming (CBF) algorithm to this model to estimate linear DOA trajectories. Further, we formulate the proposed signal model as a block sparse model and subsequently derive sparse Bayesian learning (SBL) algorithm. Our simulation results show that this linear parametric DOA model and corresponding algorithms capture the DOA trajectories for moving sources more accurately than traditional signal models and methods.

show abstract

Section: H Wideband Processingmentioning

confidence: 99%

“…SBL has the unique property of automatic sparsity selection and does not require regularization 16,17 . It is a highresolution method but suffers from basis mismatch and can be computationally expensive, especially when dealing with large dictionaries or high-dimensional data [18][19][20][21][22] .…”

Section: Introductionmentioning

confidence: 99%