Rapid assessment of ground surface conditions is essential in disaster response and search-and-rescue operations, where drones are increasingly deployed for aerial inspection and victim localization. This paper proposes an active acoustic sensing method for estimating ground surface conditions using a drone-mounted speaker and microphone array. The method is based on the multiple signal classification framework and enables three-dimensional localization of reflection points according to the principle of echolocation. A key feature of the proposed approach is that it shares both hardware and signal processing components with acoustic-based victim search, allowing simultaneous execution of surface sensing and sound source localization (SSL) on a single drone platform without increasing system complexity. Outdoor experiments were conducted to evaluate sensing performance for ground surface anomalies, specifically ground surface depressions and cracks. The experimental results clarify the achievable sensing performance and coverage in real environments and reveal key factors affecting detection performance. The feasibility of simultaneous execution of active acoustic sensing and SSL was also investigated, and the mutual interactions between sensing and localization performance were clarified. These findings highlight both the potential and the practical limitations of integrating environmental sensing and victim localization on a single drone platform.
Hoshiba et al. (2026) studied this question.