Detection-Guided Beamforming for Efficient Bat Localisation
- 1Tilburg University
Abstract
Passive acoustic monitoring is widely used to study wildlife, but current approaches provide limited insight into the spatial behaviour of animals. In bat ecology, reconstructing flight trajectories is essential for studying habitat use, movement patterns, and interactions, yet it remains difficult to achieve under field conditions. Acoustic cameras offer a potential solution by enabling sound source localisation, but their practical application is limited by the computational cost of beamforming and by the non-stationary, broadband, and transient nature of echolocation calls. In particular, exhaustive beamforming over wide ultrasonic bandwidths and dense spatial grids becomes infeasible for continuous monitoring. In this work, we propose a detection-guided beamforming framework for efficient localisation of free-flying bats. The method exploits the sparsity of echolocation signals by restricting beamforming to detector-identified time-frequency regions and combines this with physically consistent short-time analysis parameters and dense spatial sampling. The framework is evaluated on field recordings acquired with a Sorama CAM iV64s acoustic camera. Results show that detection-guided processing reduces the number of beamformer evaluations by approximately 77 times, corresponding to a reduction of about 98.7 % in computational effort, while preserving spatial resolution. At the same time, the proposed parameter configuration improves the stability and sharpness of reconstructed trajectories by avoiding artefacts associated with temporal averaging. These findings demonstrate that high-resolution acoustic localisation can be achieved under realistic computational constraints, supporting the integration of acoustic cameras into ecological monitoring workflows. The proposed framework enables the extraction of spatial trajectories from passive acoustic monitoring data, facilitating spatially resolved analyses of bat behaviour in field conditions.
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BibTeX
@misc{alessandri2026detectionguided,
title = {{Detection-Guided Beamforming for Efficient Bat Localisation}},
author = {R. Alessandri and L. Gilmour and J. J. Tan and A. Osses and D. Stowell},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.06.749406},
url = {https://www.biorxiv.org/content/10.64898/2026.09.06.749406v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - Detection-Guided Beamforming for Efficient Bat Localisation
AU - R. Alessandri
AU - L. Gilmour
AU - J. J. Tan
AU - A. Osses
AU - D. Stowell
PY - 2026
DA - 2026/09/06
PB - bioRxiv
DO - 10.64898/2026.09.06.749406
UR - https://www.biorxiv.org/content/10.64898/2026.09.06.749406v1
AB - Passive acoustic monitoring is widely used to study wildlife, but current approaches provide limited insight into the spatial behaviour of animals. In bat ecology, reconstructing flight trajectories is essential for studying habitat use, movement patterns, and interactions, yet it remains difficult to achieve under field conditions. Acoustic cameras offer a potential solution by enabling sound source localisation, but their practical application is limited by the computational cost of beamforming and by the non-stationary, broadband, and transient nature of echolocation calls. In particular, exhaustive beamforming over wide ultrasonic bandwidths and dense spatial grids becomes infeasible for continuous monitoring. In this work, we propose a detection-guided beamforming framework for efficient localisation of free-flying bats. The method exploits the sparsity of echolocation signals by restricting beamforming to detector-identified time-frequency regions and combines this with physically consistent short-time analysis parameters and dense spatial sampling. The framework is evaluated on field recordings acquired with a Sorama CAM iV64s acoustic camera. Results show that detection-guided processing reduces the number of beamformer evaluations by approximately 77 times, corresponding to a reduction of about 98.7 % in computational effort, while preserving spatial resolution. At the same time, the proposed parameter configuration improves the stability and sharpness of reconstructed trajectories by avoiding artefacts associated with temporal averaging. These findings demonstrate that high-resolution acoustic localisation can be achieved under realistic computational constraints, supporting the integration of acoustic cameras into ecological monitoring workflows. The proposed framework enables the extraction of spatial trajectories from passive acoustic monitoring data, facilitating spatially resolved analyses of bat behaviour in field conditions.
ER -