在导向向量空间中估计多个回波的到达角（AoA）
Estimating Angle of Arrival (AoA) of multiple Echoes in a Steering Vector Space
摘要：
考虑麦克风阵列，例如Amazon Echo、会议电话或自动驾驶汽车中的麦克风阵列。这些阵列的目标之一是解码声信号到达它们的角度。本文考虑了估计K到达角（AoA）的问题，即，直接路径的AoA和后续回波的AoA。在这个问题上已经取得了重大进展，然而，当源信号未知（如人声）和信道强相关（如多径设置）时，解决方案仍然难以捉摸。今天的算法可靠地估计直接路径的AoA，但随后的AoA在嘈杂的现实世界条件下发散。我们设计了SubAoA，一种改进当前工作的算法。我们的核心思想是在一个新的AoA子空间中对信号进行建模，并采用一种消除方法，依次消除每个AoA来解码下一个AoA。我们解释的行为和算法的复杂性，从第一原则，在一系列参数的性能模拟，并从现实世界的实验结果。与现有的多个算法，如GCC-PHAT，MUSIC和VoLoc的比较显示，后者AoA的增益不断增加，而我们的计算复杂度允许实时操作。我们相信多AoA估计的进展是各种声学和RF应用的基本构建块，包括人类或车辆定位，多用户分离，甚至（盲）信道估计。
英文摘要：
Consider a microphone array, such as those present in Amazon Echos, conference phones, or self-driving cars. One of the goals of these arrays is to decode the angles in which acoustic signals arrive at them. This paper considers the problem of estimating K angle of arrivals (AoA), i.e., the direct path's AoA and the AoA of subsequent echoes. Significant progress has been made on this problem, however, solutions remain elusive when the source signal is unknown (such as human voice) and the channel is strongly correlated (such as in multipath settings). Today's algorithms reliably estimate the direct-path-AoA, but the subsequent AoAs diverge in noisy real-world conditions. We design SubAoA, an algorithm that improves on the current body of work. Our core idea models signal in a new AoA sub-space, and employs a cancellation approach that successively cancels each AoA to decode the next. We explain the behavior and complexity of the algorithm from the first principles, simulate the performance across a range of parameters, and present results from real-world experiments. Comparison against multiple existing algorithms like GCC-PHAT, MUSIC, and VoLoc shows increasing gains for the latter AoAs, while our computation complexity allows real-time operation. We believe progress in multi-AoA estimation is a fundamental building block to various acoustic and RF applications, including human or vehicle localization, multi-user separation, and even (blind) channel estimation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
