SpyDir: Spy Device Localization Through Accurate Direction Finding

2026-01-31

AI Summary

This work addresses the challenge of detecting and precisely localizing covert spy cameras in complex indoor environments, where their small form factor, low power consumption, and lack of prominent side-channel signatures render conventional detection methods ineffective. To overcome this, the authors propose a high-precision localization approach leveraging unintentional electromagnetic emissions. The system integrates a portable switched antenna array, a non-coherent averaging technique to decorrelate noise, and a novel optimization-based sparse angle-of-arrival (AoA) estimation algorithm that effectively mitigates noise correlation induced by square-wave radiation and multipath interference. Experimental results demonstrate that the proposed method achieves an average AoA error of 6.30 degrees and a localization error of merely 19.86 cm, representing accuracy improvements of 10.41× and 14.8× over MUSIC and SpotFi, respectively.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Local Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Hidden spy cameras have become a great privacy threat recently, as these low-cost, low-power, and small form-factor IoT devices can quietly monitor human activities in the indoor environment without generating any side-channel information. As such, it is difficult to detect and even more challenging to localize them in the rich-scattering indoor environment. To this end, this paper presents the design, implementation, and evaluation of SpyDir, a system that can accurately localize the hidden spy IoT devices by harnessing the electromagnetic emanations automatically and unintentionally emitted from them. Our system design mainly consists of a portable switching antenna array to sniff the spectrum-spread emanations, an emanation enhancement algorithm through non-coherent averaging that can de-correlate the correlated noise effect due to the square-wave emanation structure, and a multipath-resolving algorithm that can exploit the relative channels using a novel optimization-based sparse AoA derivation. Our real-world experimental evaluation across different indoor environments demonstrates an average AoA error of 6.30 deg, whereas the baseline algorithm yields 21.06 deg, achieving over a 3.3 times improvement in accuracy, and a mean localization error of 19.86cm over baseline algorithms of 206.79cm (MUSIC) and 294.75cm (SpotFi), achieving over a 10.41 times and 14.8 times improvement in accuracy.

Problem

• spy device localization
• direction finding
• indoor multipath environment
• electromagnetic emanations
• privacy threat

Innovation

• direction finding
• spy device localization
• electromagnetic emanations
• sparse AoA estimation
• multipath mitigation