Underwater Acoustic Sensor Networks: Target Size Detection and Performance Analysis

In this paper, we propose a maximum-likelihood (ML) estimation algorithm for underwater target size detection using 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.