Parametric Models for Doa Trajectory Localization

Abstract

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.

A Comprehensive Review of Direction-of-Arrival Estimation and Localization Approaches in Mixed-Field Sources Scenario

“…While significant strides have been made in addressing the former, the latter, pertaining to sources with dynamic and non-static positions, remains an active and evolving research frontier, warranting further investigation and scholarly attention. Although approaches have been presented for parameters tracking [244][245][246][247][248][249], none have been developed for the mixed-field sources scenario. Regarding tracking parameters in the scenario of mixed sources, two cases may be imagined.…”

Multitarget direction of arrival trajectory estimation using BiLSTM and sequential allocation

“…Some common approaches are based on subspace fitting, [9][10][11][12] maximum likelihood (ML) method in a Kalman filtering framework, 13 adaptive beamforming, 14 and compressed sensing. [15][16][17][18] These algorithms have relied on simplifying assumptions such as sparsity constraint for off-grid DoA estimation and the slow subspace change over time. Also, they involve a high level of complexity, and sometimes they require multidimensional search to achieve their extreme precision.…”

“…In most of the above‐mentioned applications, the time‐varying DoA estimation problems have attracted the interest of many researchers in the past decades. Some common approaches are based on subspace fitting, 9‐12 maximum likelihood (ML) method in a Kalman filtering framework, 13 adaptive beamforming, 14 and compressed sensing 15‐18 . These algorithms have relied on simplifying assumptions such as sparsity constraint for off‐grid DoA estimation and the slow subspace change over time.…”

“…Several significant challenges have been addressed: Trajectory generation: Due to infinite possible source trajectories in the hypotheses space, the generation of different paths is a challenge. In Reference 18, the author proposes linear relations for the various DoA trajectory modeling, whereas the source trajectories are not necessarily linear in practical scenarios. In Reference 28, the DoA of the sources are quantized (into the grid points), and it has been defined as the states of the sources.…”