Estimating Angle of Arrival (AoA) of multiple Echoes in a Steering Vector Space

TL;DR

This paper introduces SubAoA, a novel algorithm for estimating multiple AoAs in complex acoustic environments, improving accuracy and real-time performance over existing methods, especially for subsequent echoes.

Contribution

The paper presents SubAoA, a new AoA estimation algorithm that models signals in a novel sub-space and successively cancels each AoA, enhancing multi-echo localization accuracy.

Findings

SubAoA outperforms GCC-PHAT, MUSIC, and VoLoc in estimating later AoAs.

The algorithm operates in real-time with manageable computational complexity.

Real-world experiments validate the simulation results and demonstrate practical applicability.

Abstract

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.