A stochastic geometric approach to sensor array processing

Abstract

A new unified mathematical framework for sensor array processing is proposed. The proposed framework combines Bayesian estimation with stochastic geometry to accommodate prior information, uncertainty in array parameters, and unknown and stochastically time-varying number of nonstationary sources. A system model for a common signal setting is constructed to demonstrate the proposed framework.