Tracking of small vessels with passive acoustic sensors

Abstract

This paper introduces a small boat vessel tracking method in marine environment using autonomous, low-complexity acoustic sensors. A cross-correlation of time series between sensors generates a coarse localization, and an application of the extended Kalman filter (EKF) gives the vessel track. Since each sensor has a local clock that operates asynchronously, the time series received by multiple sensors are first synchronized with one another by measuring a sequence of impulses played at the beginning of the recording. The algorithm detects a moving boat by the increase in the broadband sound level and confirms it by extracting the amplitudes at harmonic frequencies due to the propeller movement. To calculate the time delay of arrival, the boat-present signals are divided into small segments that are cross-correlated with signals received by other sensors. Thresholding and clustering are introduced to extract multiple tracks from the cross-correlation. The EKF is trained using the estimated time delays to provide the vessel track. The algorithm has been successful with Bellhop simulated boat signals and field data collected on the Willamette River and Columbia River in Oregon. Limiting factors such as network topology and sensor movement during deployment are also investigated. [Work sponsored by the Nature Conservancy.]