Target Detection Using 3-D Sparse Underwater Senor Array Network

Underwater target detection has been widely used nowadays. In this paper, we show that the 3-D nested-array system can provide O(N2) degree of freedom(DOF) by using only N physical sensors when the second order statistics of the received data is used, which means we can use less sensors to get a better performance. A maximum likelihood (ML) estimation algorithm for underwater target size detection is also introduced. Theoretical analysis illustrates that our underwater sensor network can greatly reduce the variance of target estimation. We show that our maximum likelihood estimator is unbiased, also the Cramer-Rao lower bound can be achieved when estimating the variance of parameter. Simulations further validate these theoretical results.