Adaptive Sampling Based on Deterministic Compressive Sensing using Autonomous Underwater Vehicles - B. Chen; P. Pandey; D Pompili (Proc. of IFAC Conference on Manoeuvring and Control of Marine Craft (MCMC), 2012)
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.