MASSLOC: A Massive Sound Source Localization System Based on Direction-of-Arrival Estimation and Complementary Zadoff–Chu Sequences

Abstract

Acoustic indoor localization offers the potential for highly accurate position estimation while generally exhibiting low hardware requirements compared to radio frequency (RF)-based solutions. Furthermore, angular-based localization significantly reduces installation effort by minimizing the number of required fixed anchor nodes. In this article, we propose the so-called MASSLOC system, which leverages sparse 2-D array geometries to localize and identify a large number of concurrently active sources. Additionally, the use of complementary Zadoff–Chu sequences is introduced to enable efficient, beamforming-based source identification. These sequences provide a tradeoff between favorable correlation properties and accurate, unsynchronized direction-of-arrival (DoA) estimation by exhibiting a spectrally balanced waveform. The system is evaluated in both a controlled anechoic chamber and a highly reverberant lobby environment with a reverberation time of 1.6 s. In a laboratory setting, successful DoA estimation and identification of up to 14 simultaneously emitting sources are demonstrated. Adopting a Perspective-n-Point (PnP) calibration approach, the system achieves a median 3-D localization error of 55.7 mm and a median angular error of 0.84° with dynamic source movement of up to 1.9 ms⁻¹ in the challenging reverberant environment. The multisource capability is also demonstrated and evaluated in that environment with a total of three tags. These results indicate the scalability and robustness of the MASSLOC system, even under challenging acoustic conditions.