Multi-Point Acoustic Sensing Overview

• Multi-point acoustic sensing is a method that uses spatially distributed sensors to capture and analyze sound fields for precise localization and environmental assessment.
• It employs techniques such as TOA, DoA estimation, and fiber-optic sensing to achieve accuracies ranging from 15 cm to 55.7 mm and high temporal resolution.
• Applications include robotics, smart environments, oceanography, and infrastructure monitoring, boosted by machine learning and multimodal sensor fusion.

Multi-point acoustic sensing refers to the simultaneous acquisition and interpretation of acoustic signals at multiple spatially separated locations. Unlike single-point sensing, which captures sound at a single position, multi-point schemes are designed to extract spatial information about sound fields, source locations, or environment properties by leveraging coordinated data from a set of distributed sensors or emitters. This paradigm underpins advancements in localization, environmental monitoring, source separation, and context-aware systems across domains such as robotics, smart environments, telecommunication, structural health monitoring, and oceanography.

1. Principles and Methods for Multi-Point Acoustic Sensing

Multi-point acoustic sensing systems combine sensor network architectures, signal processing, and, increasingly, machine learning to extract spatial information from distributed measurements. The foundational distinction is between systems that infer the properties of the sound field itself (e.g., source localization, scattering, environment mapping), and those that infer environmental properties (e.g., room acoustics, structural integrity) via distributed acoustic responses.

Canonical strategies include:

• Time-of-Arrival (TOA) and Elapsed Time of Arrival (ETOA): Systems such as those in (Moosavi-Dezfooli et al., 2015) use coordinated pulse emissions among devices (e.g., smartphones), where the propagation times—measured with careful scheduling and sample counting due to unsynchronized clocks—yield pairwise distances. The distances collectively inform a multidimensional scaling (MDS) problem, often solved using algorithms like s-stress minimization to estimate device positions in Euclidean space.
• Direction-of-Arrival (DoA) Estimation: Approaches such as MASSLOC (Fischer et al., 16 Aug 2025) employ sparse 2D or 3D microphone arrays with advanced beamforming (e.g., MUSIC algorithm) and source-coding via complementary Zadoff–Chu sequences to resolve angular information on multiple concurrent sources, eliminating the need for dense anchor networks.
• Acoustic Vector Sensing: Sensor fusion with particle-velocity vector measurements (e.g., AVS on smartglasses, (Levin et al., 2016)) augments traditional pressure-based recording, improving near-field gain and enabling robust separation of spatially proximate sources even in noisy environments.
• Distributed Fiber Optic Sensing: Techniques such as DAS (Xenaki et al., 25 Feb 2025, Shen et al., 2022, Shi et al., 26 Mar 2025) and event-based speckle interrogation (Lopes et al., 26 Sep 2025) convert optical fibers into continuous acoustic arrays. These systems measure strain-induced phase changes in the fiber, translating to distributed acoustic measurements over kilometers.
• Collaborative and Fused Modalities: Multi-agent robot teams with active exploration (Yu et al., 2023), multi-modal sensor fusion (acoustics + LiDAR, (Riemens et al., 2022)), and joint passive/active schemes (Bradley et al., 19 Apr 2024) further extend multi-point sensing to cover spatial, angular, and spectral deficiencies of single-modality approaches.

2. System Architectures and Signal Processing Workflows

The system architectures of multi-point acoustic sensing platforms are tailored to balance spatial resolution, coverage, robustness, and deployment constraints. Key architectural elements include:

• Sensor Topology: Microphone arrays can be placed in uniform grids, sparse/coprime/nested configurations (Fischer et al., 16 Aug 2025), or distributed arbitrarily (e.g., networks of wireless nodes or smartphones, (Moosavi-Dezfooli et al., 2015)). In fiber-optic systems, the spatial granularity is controlled by the pulse width, channel spacing, and gauge length (Xenaki et al., 25 Feb 2025).
• Acoustic Scheduling and Pulse Design: The emission of acoustic pulses is carefully scheduled across multiple devices to avoid collision and ensure identifiability (Moosavi-Dezfooli et al., 2015). Pseudo-noise sequences are favored for their favorable autocorrelation properties, supporting CDMA-style separation even with amplitude variability.
• Sampling and Feature Extraction: In settings where timestamp precision is lacking (e.g., commodity smartphones), sample counting at known acquisition rates replaces reliance on OS clocks. In fiber-optic and event-based vision systems, raw data are transformed into feature spaces via tensor decomposition or machine-learned optimal interrogation modes (Lopes et al., 26 Sep 2025).
• Localization and Mapping Algorithms: Recovery of spatial configuration is typically via MDS (using pairwise distances), reverse ray-tracing (computing possible source locations from time delays), DoA estimation (angular spectra), or fusion with external modalities (LiDAR-informed inverse problems, (Riemens et al., 2022)). Advanced approaches solve convex or sparse estimation problems, optimizing over incomplete, noisy, or spatially biased data.
• Machine Learning Integration: DAS platforms increasingly leverage representation learning, variational autoencoders, and deep convolutional models to classify, augment, and interpret distributed acoustic measurements (Shen et al., 2022, Shi et al., 26 Mar 2025). Transformers and LSTMs are applied to complex, high-dimensional sequential data.

3. Applications and Use Cases

Multi-point acoustic sensing has catalyzed numerous applications:

Application Domain	System Type(s)	Characteristic Feature / Result
Indoor localization and asset tracking	Smartphone arrays, MASSLOC	15 cm position error (Moosavi-Dezfooli et al., 2015); 55.7 mm error for 14 sources (Fischer et al., 16 Aug 2025)
Smart wearables and voice acquisition	AVS smartglasses	21.8 dB noise reduction, fixed RTF geometry (Levin et al., 2016)
Virtual/Augmented Reality and motion tracking	MilliSonic	0.7 mm 1D / 2.6 mm 3D accuracy (Wang et al., 2019)
Oceanographic and seismic monitoring	DAS	Fin whale, ship, T-wave detection over 10–100 km (Xenaki et al., 25 Feb 2025)
Acoustic environment mapping	Multi-agent robotics	Enhanced room coverage and RIR prediction accuracy (Yu et al., 2023)
Automated infrastructure and security monitoring	ML-enhanced DAS	Vehicle/slope event recognition at 95.8% accuracy (Shi et al., 26 Mar 2025)
Smart cities and environmental noise monitoring	Wireless sensor networks, DAS	Cross-layer optimization for energy, rich event detection (Dekkers et al., 2018)
Industrial/robotic haptic perception in occlusion	Contact-mic arrays (SonicBoom)	0.43–2.22 cm contact localization (Lee et al., 13 Dec 2024)

Specific studies have shown that, for example, the Techtile system (Delabie et al., 2022) provides a real-world testbed with measured RT60 values (up to 1.17 s at 5 kHz), facilitating the development of robust indoor multi-point sensing algorithms suitable for environments ranging from offices to concert halls.

4. Performance, Limitations, and Comparative Analysis

Performance metrics in multi-point acoustic sensing are highly application dependent:

• Localization accuracy: Sub-centimeter to ~15 cm in indoor mobile device arrays (Moosavi-Dezfooli et al., 2015, Wang et al., 2019, Fischer et al., 16 Aug 2025).
• Concurrent source capacity: MASSLOC demonstrates simultaneous localization of up to 14 sources with DoA median angular error of 0.84° (Fischer et al., 16 Aug 2025), while Symphony achieves median localization error of 0.694 m with a single small array (Wang et al., 2022).
• Dynamic and bandwidth range: Event-based speckle interrogation (Lopes et al., 26 Sep 2025) achieves high temporal resolution (up to 20 kHz) and dynamic range (120 dB), outperforming conventional imaging frame rates.
• Power and deployment constraints: WASN-EM evaluates energy usage across sensing, processing, and communications, guiding system design for smart, battery-powered WiFi-enabled acoustic sensor nodes (Dekkers et al., 2018).

Comparatively, DoA-based schemes require fewer static anchors than typical ToA/TDoA systems, while optical fiber and event-based methods achieve spatial resolution and temporal bandwidth unachievable by microphone arrays alone. However, each approach has trade-offs—array geometries place requirements on array calibration and physical deployment, dense DAS systems may be limited by interrogation bandwidth and gauge length, and learning-based methods require substantial labeled datasets for robust generalization.

5. Emerging Techniques and Multimodal Fusion

Recent developments in multi-point acoustic sensing are marked by:

• Hybrid Sensing and Multimodal Fusion: Fused active and passive approaches combine range and bearing measurements for SLAM when single-modality sensors suffer from angular coverage limitations (Bradley et al., 19 Apr 2024). Integration of LiDAR provides a priori geometric constraints to acoustic reflector estimation, improving model robustness and minimizing computational burden (Riemens et al., 2022).
• Quantum-enhanced and Picometric Sensing: Hybrid quantum networks (Novikov et al., 16 Dec 2024) leverage entangled EPR beams and atomic spin ensembles to suppress quantum noise over kHz–MHz bands, targeting applications like broadband seismic or gravitational wave detection with ultimate sensitivity. Remote ultrastable laser interferometry achieves 0.5 pm/Hz^1/2 displacement sensitivity for acoustic signals up to 100 kHz across ≥60 m standoff (Jang et al., 11 Nov 2024).
• Reinforcement and Collaborative Learning: Policies for collaborative robotic agents allow robots to actively explore to optimize acoustic environment estimation—with reward structures balancing spatial coverage and RIR prediction error (Yu et al., 2023).

6. Challenges and Future Directions

Despite notable advances, multi-point acoustic sensing faces ongoing challenges:

• Multipath Robustness and Reverberation: High RT60 values and complex geometries challenge robust direct-path extraction and localization. Advanced statistical and ML-based filtering, calibration redundancy (as in Techtile, (Delabie et al., 2022)), and multi-modal discrimination remain essential.
• Scalability and Synchronization: Methods for handling large numbers of sources/tags (e.g., using overcomplete array co-arrays, assignment algorithms for beamformed outputs, as in MASSLOC (Fischer et al., 16 Aug 2025)) and maintaining tight synchronization across distributed nodes (wired or wireless) are crucial.
• Energy, Data Throughput, and Edge Intelligence: Energy-efficient design (WASN-EM, (Dekkers et al., 2018)), compressed sensing methods (Shen et al., 2022), and edge processing architectures balance the computational and communication overhead for real-time, persistent deployment.
• Data-driven versus Analytical Models: Data-driven methods (such as those in SonicBoom (Lee et al., 13 Dec 2024) and compressed DAS frameworks (Shen et al., 2022, Shi et al., 26 Mar 2025)) provide generalization in complex media but require extensive, high-quality labeled data and may lack interpretability compared to analytically grounded approaches.

Anticipated research directions include integration of quantum-enhanced metrology, fusion with visual and environmental modalities, rapid and distributed calibration/self-calibration, and standardized open-source testbeds (as exemplified by Techtile (Delabie et al., 2022)).

7. Summary Table: Key Features Across Selected Multi-Point Acoustic Sensing Systems

System / Study	Sensor Modality	Algorithmic Core	Demonstrated Accuracy / Capacity	Domain / Application
(Moosavi-Dezfooli et al., 2015)	Smartphone microphones	ETOA sample counting, MDS (s-stress)	~15 cm indoor position error	Relative device localization, asset tracking
(Levin et al., 2016)	Acoustic vector-sensors	Adaptive MVDR, near-field gain	21.8 dB noise reduction	Smartglasses, near-field speech enhancement
(Wang et al., 2019)	4-mic compact array	Phase-based FMCW, multipath rejection	0.7 mm (1D) / 2.6 mm (3D)	VR/AR tracking, concurrent device localization
(Xenaki et al., 25 Feb 2025, Shen et al., 2022)	Distributed optical fiber	Phase differential, CS, ML	Varies: km-scale, TPR >99%	Ocean monitoring, structural/acoustic surveillance
(Fischer et al., 16 Aug 2025)	Sparse 2D mic array	MUSIC, Zadoff–Chu, self-calibration	55.7 mm / 0.84° for 14 simultaneous	Indoor localization, multi-user scenarios
(Delabie et al., 2022)	Dense MEMS mic array	Synchronized multi-tile acquisition	Sample-level precision (0.27 mm/sample)	Testbed for complex reverberant environments
(Lee et al., 13 Dec 2024)	Contact-mic robot array	Phase-aware transformer fusion	0.43–2.22 cm contact localization	Robot haptic mapping under visual occlusion

Each system is tuned for its operational environment, balancing spatial and temporal resolution, robustness, and hardware deployment cost.

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Multi-point acoustic sensing is a mature yet rapidly advancing field underpinned by mathematical models for wave propagation and coupled with a progression toward distributed, intelligent, and multi-modal architectures. They enable high-resolution, robust acoustic inference across diverse engineering and scientific domains, with ongoing research focused on scalability, noise robustness, quantum-limited performance, and seamless integration into complex environments.