LAUSR

Passive acoustic monitoring (PAM) is a method that has been proved to be a powerful tool for monitoring sperm whales. Sperm whales frequently emit loud, short duration, clicks for the purposes of echolocation. The clicks are emitted at intervals of typically once per second, but the exact interval between pulses varies irregularly. To achieve the monitoring goals, it is necessary to localise the vocalising animal. This is typically achieved by detecting the animal’s echolocation clicks on a hydrophone array, estimating time delays between clicks and then employing a localisation algorithm. By adopting a tracking-based approach we are able to smooth the results, based on a model of whale motion, to remove noise and improve the estimates of location. The paper will consider the problem of tracking whales using the data from fixed bottom mounted sensors and using a tracking algorithm to follow the motion of such animals. The tracking will be implemented using a Kalman filter, but in this instance that filter needs to be designed to allow for data measured at irregular intervals. The method uses a version of the Kalman filter using a varying time-step. The time-step varying Kalman filter is applied to track data from simulated and measured datasets. The measured dataset is publicly available as part of the 2nd International Workshop on Detection and Localisation of Marine Mammals, Monaco, 2005 and comprises of a single animal moving within an array of 5 sensors.Passive acoustic monitoring (PAM) is a method that has been proved to be a powerful tool for monitoring sperm whales. Sperm whales frequently emit loud, short duration, clicks for the purposes of echolocation. The clicks are emitted at intervals of typically once per second, but the exact interval between pulses varies irregularly. To achieve the monitoring goals, it is necessary to localise the vocalising animal. This is typically achieved by detecting the animal’s echolocation clicks on a hydrophone array, estimating time delays between clicks and then employing a localisation algorithm. By adopting a tracking-based approach we are able to smooth the results, based on a model of whale motion, to remove noise and improve the estimates of location. The paper will consider the problem of tracking whales using the data from fixed bottom mounted sensors and using a tracking algorithm to follow the motion of such animals. The tracking will be implemented using a Kalman filter, but in this instance that filter...