Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2611.02591

A Distributed Microphone Network for Real-Time Gunshot Detection and Localization in GNSS-Challenged Large Areas

Dengke Wang·Bodhibrata Mukhopadhyay·Mohamed-Slim Alouini
View original source ↗Read source document ↗Export metadata ↓

Abstract

This paper presents a scalable distributed acoustic surveillance system for real-time gunshot detection and localiza tion in large-area, GNSS-challenged environments. The system consists of multiple low-cost MEMS microphone array units (MAUs) deployed as distributed anchors. Each MAU performs edge-level sound-event detection using a lightweight Sound De tection and Echo Suppression (SDES) algorithm, followed by a TinyML-based classifier for gunshot identification. For detected gunshots, an enhanced Generalized Cross-Correlation (eGCC) al gorithm estimates intra-array time differences of arrival (TDoAs) with improved robustness under low signal-to-noise ratio and re verberant conditions. Angle-of-arrival (AoA) estimates obtained from individual MAUs are fused through a modified SCO-EV localization framework to determine the gunshot position in a global coordinate system. A misplacement calibration (MC) algorithm compensates for platform orientation errors, improv ing angular consistency and localization accuracy in practical field conditions. Extensive experiments conducted over coverage areas ranging from 4,985 m2 to 49,063 m2 demonstrate reliable operation across varying spatial scales. The proposed framework achieves a Matthews Correlation Coefficient (MCC) of 0.70 for gunshot recognition and average localization errors below 6.5 m over 25,000 m2 , while maintaining errors under 15 m at larger scales. The results demonstrate the practical potential of the proposed system for distributed gunshot surveillance in GNSS challenged large areas.

Explore related subjects

eess.SP

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Dengke Wang, Bodhibrata Mukhopadhyay, Mohamed-Slim Alouini. 2026-11-02. A Distributed Microphone Network for Real-Time Gunshot Detection and Localization in GNSS-Challenged Large Areas. https://arxiv.org/abs/2611.02591

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

SpellerSSL: Self-Supervised Learning with P300 Aggregation for Speller BCIs

Electroencephalogram (EEG)-based P300 speller brain-computer interfaces (BCIs) face three main challenges: low signal-to-noise ratio (SNR), poor generalization, and time-consuming calibration. We propose SpellerSSL, a framework that combines self-supervised learning (SSL) with P300 aggregation to address these issues. First, we introduce an aggregation strategy to enhance SNR. Second, to achieve generalization in training, we employ a customized 1D U-Net backbone and pre-train the model on both cross-domain and in-domain EEG data. The pretrained model is subsequently fine-tuned with a lightweight ERP-Head classifier for P300 detection, which adapts the learned representations to subject-specific data. Our evaluations on calibration time demonstrate that combining the aggregation strategy with SSL significantly reduces the calibration burden per subject and improves robustness across subjects. Experimental results show that SSL learns effective EEG representations in both in-domain and cross-domain, with in-domain achieving a state-of-the-art character recognition rate of 94% with only 7 repetitions and the highest information transfer rate (ITR) of 21.86 bits/min on the public II-B dataset. Moreover, in-domain SSL with P300 aggregation reduces the required calibration size by 60% while maintaining a comparable character recognition rate. To the best of our knowledge, this is the first study to apply SSL to P300 spellers, highlighting its potential to improve both efficiency and generalization in speller BCIs and paving the way toward an EEG foundation model for P300 speller BCIs. The code is available at: https://github.com/Emotiv/SpellerSSL-NeurIPS2025

eess.SP↗
arXiv2026

Outage Probability Analysis of MRC-Based Fluid Antenna Systems under Rician Fading

This paper investigates a fluid antenna system (FAS) where a single-antenna transmitter communicates with a receiver equipped with a fluid antenna (FA) over Rician fading channels. Two channel models are considered to incorporate the correlation among the ports into the fading gains, namely: i) the widely used physical reference port model, implemented by considering the first physical port as the reference port; and ii) the virtual reference port model, considering a common port correlation coefficient and implemented by assuming the presence of a virtual reference port. Assuming that multiple ports among the M available FA ports can be activated, the receiver selects the best K with the highest instantaneous signal-to-noise ratio (SNR) and combines the received signals at the selected ports using maximum ratio combining. For both port models, the statistics of the post-combining SNR are derived using a characteristic function approach, which allows to analyze the outage probabilities (OPs) of the FAS. Additional closed-form lower bounds on the OPs and the asymptotic OPs at high SNR are derived, revealing the diversity order of the FAS to be M. Numerical results validate the analytical framework and demonstrate the interplay of key system parameters on the performance of the considered MRC-based FAS. Specifically, the performance difference between the two reference port models becomes increasingly evident as M, K, and the average SNR of the FAS increase.

eess.SP↗
arXiv2026

The Symmetric Location Problem: a Song of Efficiency and Robustness

The aim of this Lecture Note is to introduce the Signal Processing (SP) community to a powerful yet still under-utilised tool: the semiparametric statistics. In short, the semiparametric framework allows us to estimate or perform hypothesis testing on a finite-dimensional parameter $\boldsymbolθ \in Θ\subseteq \mathbb{R}^p$ in the presence of an \textit{infinite-dimensional nuisance parameter} $g \in \mathcal{S}$ (i.e. a function), such as the density of the noise. Clearly, this framework is general enough to include almost every SP application. Remarkably, as the title suggests drawing on George R. R. Martin's famous book series, the greatest advantage of semiparametric statistics over parametric and non-parametric ones lies in the fact that it is able to reconcile two seemingly dichotomous concepts: statistical efficiency and distributional robustness. To explain exactly what this means, in this Lecture Note we will focus our attention on the famous and fundamental symmetric location problem.

eess.SP↗