Characterization of acoustic detection efficiency using an unmanned surface vessel as a mobile receiver platform
Links
- More information: Publisher Index Page (via DOI)
- Open Access Version: Publisher Index Page
- Download citation as: RIS | Dublin Core
Abstract
Studies involving acoustic telemetry typically use stationary acoustic receivers arranged in an array or grid. Unmanned surface vehicle (USV)-based mobile receivers offer advantages over the latter approach: the USV can be programmed to autonomously carry a receiver to and from target locations, more readily adapting to a survey’s spatial scope and scale. This work examines the acoustic detection performance of a low-cost USV developed as a flexible sensing platform. The USV was fitted with an acoustic receiver and operated over multiple waypoints set at increasing distances from the transmitter in two modes: drifting and station-keeping. While drifting, the USV was allowed to drift from the waypoint; while station-keeping, the USV used its thruster to hold position. Detection performance of the USV was similar to that of stationary receivers while drifting, but significantly worse while station-keeping. Noise from the USV thruster was hypothesized as a potential cause of poor detection performance during station-keeping. Detection performance varied with the depth of the tethered receiver such that detection range was greater during the deepest (4.6 m) trials than during shallower (1.1 and 2.9 m) trials. These results provide insight and guidance on how a USV can be best used for acoustic telemetry, namely, navigating to a planned waypoint, drifting and lowering the receiver to a desired depth for listening, and then navigating to the next waypoint.
Study Area
Publication type | Article |
---|---|
Publication Subtype | Journal Article |
Title | Characterization of acoustic detection efficiency using an unmanned surface vessel as a mobile receiver platform |
Series title | Animal Biotelemetry |
DOI | 10.1186/s40317-023-00350-1 |
Volume | 11 |
Year Published | 2023 |
Language | English |
Publisher | Springer |
Contributing office(s) | Great Lakes Science Center |
Description | 41, 13 p. |
Country | United States |
State | Michigan |
Other Geospatial | Hammond Bay |
Google Analytic Metrics | Metrics page |