Underwater Acoustic Localisation in the context of Autonomous Submersibles

With the advancement of the field of underwater robotics, the amount of autonomy embodied in the vehicles themselves have considerably increased while making it possible to build and deploy swarms of small autonomous underwater vehicles (AUVs). Apart from the many environ- mental and mechanical challenges encountered in the underwater domain, the swarming paradigm demands the need for each vehicle to be aware of the positions of at least its near neighbours. The Serafina AUV project which was initiated with the goal of developing swarming technology for the small and highly agile Serafina class AUVs requires a localisation system which could cope with the dynamic and fast changing vehicle configurations while being small, reliable, robust, and energy efficient and not dependent on pre-deployed acoustic beacons. The acoustical relative localisation system proposed here uses hyperbolic and spherical localisation concepts and provides each vehicle with the azimuth, range and heading of its near neighbours. The implementation utilises an acoustically transmitted maximum length sequence (MLS) signal which provides extremely high robustness against interference by stochastic and systematic disturbances which are typical for underwater environments. The azimuth is obtained via hyperbolic positioning with improved resolution and accuracy with respect to conventional methods. Range and heading estimation is performed utilising two independent methods for increased robustness. The first method uses the implicit synchronisation provided by the underlying inter-vehicle communication scheduling system to measure the difference in time of arrival of the acoustic and long-wave radio signals to estimate the time of flight (TOF) of the acoustic signal and hence measure range. The second method relies on multiple time differences of arrival (TDOA) and a reverse hyperbolic localisation scheme to measure range without any explicit knowledge of the sending times of the acoustic signals. The localisation system performance with regard to accuracy, precision and robustness against interference is experimentally evaluated. Results of experiments conducted at a test tank as well as those obtained during open water lake experiments are presented along with detailed analyses of the behaviour of the errors associated with the measurements

Acoustical Ranging Techniques in Embedded Wireless Sensor Networked Devices

Location sensing provides endless opportunities for a wide range of applications in GPS-obstructed environments; where, typically, there is a need for higher degree of accuracy. In this article, we focus on robust range estimation, an important prerequisite for fine-grained localization. Motivated by the promise of acoustic in delivering high ranging accuracy, we present the design, implementation and evaluation of acoustic (both ultrasound and audible) ranging systems.We distill the limitations of acoustic ranging; and present efficient signal designs and detection algorithms to overcome the challenges of coverage, range, accuracy/resolution, tolerance to Doppler’s effect, and audible intensity. We evaluate our proposed techniques experimentally on TWEET, a low-power platform purpose-built for acoustic ranging applications. Our experiments demonstrate an operational range of 20 m (outdoor) and an average accuracy 2 cm in the ultrasound domain. Finally, we present the design of an audible-range acoustic tracking service that encompasses the benefits of a near-inaudible acoustic broadband chirp and approximately two times increase in Doppler tolerance to achieve better performance

Using feature vectors to detect frog calls in wireless sensor networks

A method for detecting vocalization of giant barred frogs (Mixophyes iteratus) in noisy audio is proposed. Audio recordings from remote wireless sensor nodes were segmented into individual sounds and from each sound a small set of features was extracted. Feature vectors were compared to those of example calls using a Euclidean distance formula as a detection system. The system achieved a sensitivity of 0.85 with specificity of 0.92 when distinguishing M. iteratus calls from other species’ calls and sensitivity of 0.88 with specificity 0.82 against background noise