Adaptive Sampling Based on Deterministic Compressive Sensing using Autonomous Underwater Vehicles | The NSF Cloud and Autonomic Computing Center at Rutgers

Title	Adaptive Sampling Based on Deterministic Compressive Sensing using Autonomous Underwater Vehicles
Publication Type	Conference Proceedings
Year of Publication	2012
Authors	Chen, B, Pandey, P, Pompili, D
Conference Name	Proc. of IFAC Conference on Manoeuvring and Control of Marine Craft (MCMC)
Date Published	Sept. 2012
Conference Location	Arenzano (GE), Italy
Abstract	To achieve efficient and cost-effective sensing coverage of the vast under-sampled 3D aquatic volume, intelligent adaptive sampling strategies involving Autonomous Underwater Vehicles (AUVs) endowed with underwater wireless (acoustic) communication capabilities become essential. These AUVs should coordinate and steer through the region of interest, and cooperatively sense, preprocess and transmit measured data to onshore stations for processing and analysis. Given a scalar field, i.e, a phenomenon (e.g, temperature, salinity etc.) to sample, the AUVs should coordinate to take measurements using minimal resources (time or energy) in order to reconstruct the field with admissible error. A novel adaptive sampling solution to minimize the sampling cost is proposed, which requires the AUVs to take small number of samples from the field. We observe via simulations that our proposed solution outperforms existing solutions that are based on Compressive Sensing techniques.

Document:

Main menu