Underwater acoustic sensor networks: Target size detection and performance analysis

Underwater acoustic sensor network consists of a variable number of sensors and vehicles that are deployed to perform collaborative monitoring tasks over a given area. Scalability concern suggests a hierarchical organization of underwater sensor networks with the lowest level in the hierarchy being a cluster. In this paper, we show that an ultra-wide band (UWB) channel can be used for underwater channel modeling and propose a maximum-likelihood (ML) estimation algorithm for underwater target size detection using collaborative signal processing within a cluster in underwater acoustic sensor networks. Theoretical analysis demonstrates that our underwater sensor network can tremendously reduce the variance of target size estimation. We show that our ML estimator is unbiased and the variance of parameter estimation matches the Cramer–Rao lower bound. Simulations further validate these theoretical results.