Joint Localization and Calibration in Partly and Fully Uncalibrated Array Sensor Networks

The performance of high-resolution direction finding methods can significantly degrade if mismatches between the actual array response and the modeled array response are not compensated. Using sources of opportunity, self-calibration techniques jointly estimate any unknown perturbations and source parameters. In this work, we propose a self-calibration method for sensor networks that fully exploits the source position by combining the well-known bearings-only localization method and existing eigenstructure based self-calibration techniques. Using numerical experiments we demonstrate that the proposed method can uniquely estimate the gain and phase perturbations of multiple sensors as well as the positions of a moving source. We outline the Cramer-Rao lower bound and show that the method is efficient. Finally, the self-calibration method is applied to measurement data collected in field trials.