论文标题
DOA轨迹定位的参数模型
Parametric Models for DOA Trajectory Localization
论文作者
论文摘要
在许多应用中，已经提出了许多算法的到达方向（DOA）估计或来源定位是一个重要的问题。大多数本地化方法都使用块级处理，该处理结合了多个数据快照来估算一个块内的DOA。假定DOA在块持续时间内是恒定的。但是，这些假设通常由于源运动而违反。在本文中，我们提出了一个信号模型，该模型捕获块内DOA中的线性变化。我们将常规光束成型（CBF）算法应用于该模型，以估计线性DOA轨迹。此外，我们将提出的信号模型提出为块稀疏模型，然后得出稀疏的贝叶斯学习（SBL）算法。我们的仿真结果表明，该线性参数DOA模型和相应的算法比传统的信号模型和方法更准确地捕获了移动源的DOA轨迹。
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.
