A-LOAF: A Real-Time Acoustic Localization Framework Based on AOA-FMCW Estimation

Abstract

Accurate and stable position and orientation estimation in indoor environments is critical for many real-world applications, yet remains an open challenge without a universally optimal solution. In this work, we propose A-LOAF, a novel acoustic-based system that achieves real-time and precise indoor localization and orientation estimation using commercial speakers and a compact microphone array. The system employs spatially distributed acoustic sources to emit inaudible near-ultrasonic linear frequency-modulated (LFM) signals, which are captured by a microphone array mounted on the mobile device to be localized. An angle-of-arrival (AOA) estimation algorithm is employed to determine source directions in real time, and device pose is jointly estimated by minimizing an AOA-related objective function. To further enhance localization accuracy, frequency-modulated continuous wave (FMCW) ranging is integrated to compute the relative displacement between keyframes as interframe constraints, supported by a dedicated keyframe selection strategy. The FMCW-based constraint is then incorporated as a penalty term into the AOA-based optimization framework, enabling robust and high-precision pose estimation. Experimental results in complex real-world indoor environments demonstrate that A-LOAF achieves a median localization error of 0.1171 m and a median orientation error of 0.60°, with an average computational time of 0.0884 s per frame, confirming its effectiveness and practical applicability. To benefit the community, we make the source code publicly available at https://github.com/zjuersdsd/ALOAF.git