Low Energy, Passive Acoustic Sensing for Wireless Underwater Monitoring Networks

Abstract

This thesis presents the research conducted to develop low energy passive acoustic monitoring (PAM) algorithms. There are many signal processing techniques and machine learning systems which are capable of detecting and classifying target signals. However, this project aims to produce PAM detection and classification results using a low energy budget. The benefit of using this approach is that physical devices can be developed and deployed in open sea for several months using only battery power. This opens up the deployment area to very deep water where power sources are not readily available. Using passive acoustic communication to relay the detection data produced by the algorithm, it is expected that these systems could form an underwater network of sensor nodes. There are three targets for passive acoustic detection/classification included in this thesis, which are motorised surface vessels, cetacean clicks and cetacean whistles. The surface vessel detection method is based on a low energy implementation of Detection of Envelope Modulation On Noise (DEMON). Vessels produce high frequency modulated noise during propeller cavitation which the DEMON method aims to extract for the purposes of automated detection. The vessel detector design has different approaches with mixtures of analogue and digital processing, continuous and duty-cycled sampling/processing. The detector has been integrated with a low cost/power acoustic modem platform to provide acoustic communication of data in near real time. The vessel detector has been deployed at 20m depth for a total of 84 days in the North Sea providing a large data set, which the results are based on. Open sea field trial results have shown the detection of single and multiple vessels with a 94% corroboration rate with local Automatic Identification System (AIS) data. Results have shown additional information about the detected vessel, such as the number of propeller blades, can been extracted solely based on the detection data. The attention to energy efficiency has led to an average power consumption of 11.4mW enabling long term deployments of up to 6 months using only four alkaline C cells. Additional battery packs and a modified enclosure could enable a longer deployment duration. As the detector was still deployed during the first UK lockdown, the impact of Covid-19 on North Sea fishing activity has been captured in the results. Cetacean click detection is based on identifying and classifying the high frequency impulsive click trains created by cetaceans during navigation and foraging. A low energy method of detecting these vocalisations is proposed alongside a statistical based method of classification. The algorithm developed was tested using real recordings of cetacean activity and comparisons have been conducted against a commercially available cetacean monitoring system. The results show that the energy efficient algorithm produces comparable results to the commercial system when real recordings are processed. The cetacean whistle detection algorithm is based on a low energy phase locked loop (PLL) technique. PLL methodology has been adapted for this project to aid in developing a low energy approach to detecting cetacean whistles by tracking the sweeps in frequency they produce. Results are based on offline processing using real recordings of these animals. The results have shown a 75% success rate when comparing against human analysis of the recording. Future work includes the further development of the cetacean related algorithms into fully deployable, battery-powered, nodes for open sea field trails. The future work related to vessel detection includes adding a tracking feature to the passive acoustic monitoring technology.