Undersea Acoustic Communication Maps for Collaborative Navigation

Abstract

The ability to accurately estimate signal strength can im- prove collaborative navigation algorithms by creating greater navigational exibility and system robustness. This extended abstract describes initial results on the building of underwa- ter acoustic communication maps for an entire 2-D surface area from a small number of signal samples collected at dis- crete points in the area. We formulate two types of maps - a local communication map (LCM) and global communication map (GCM). The LCM is de ned with respect to a xed destination point. It is built using kriging techniques to estimate the mean and variance of a random eld. The GCM is a compendium of local maps and is built using bayesian inferencing to esti- mate of a lattice of random elds. The GCM permits a vehicle to estimate communication e ectiveness to another location within the bounded region. We present an example LCM and GCM derived from acoustic modem SNR datasets recently collected in Monterey, CA.