Source localization performance of a multi-array network under sensor orientation and position uncertainty

There is increased interest in networking arrays of sensors for distributed source localization. An analysis of the generalized model and estimation performance for multiple sources being observed by a field of networked arrays has recently been studied in via the Cramer-Rao lower bound (CRLB). In previous work concerning the CRB and multiple sources, the models were formulated for observations with a distributed network of sensor arrays. However, previous work assumed completely known sensor orientation and position. In real world systems such as distributed sonar systems, this is rarely the case. In this paper, sensor orientation and position errors are incorporated into the signal model. Simulation examples are given that show that a network of a high number of low-complexity arrays outperforms a network of a low number of high resolution arrays when considering subarray position and orientation errors. This occurs even though the high resolution array network has four times as many sensing elements.