Accuracy and consistency in estimation and fusion over long-haul sensor networks

Long-haul sensor networks can be found in many real-world applications, such as tracking and/or monitoring of one or more dynamic targets in space. In such networks, sensors are remotely deployed over a large geographical area, whereas a remote fusion center fuses the information provided by these sensors in order to improve the accuracy of the final estimates of certain target characteristics. We consider the accuracy as well as consistency of information measures such as the error covariance matrices used to describe the theoretical error performance of sensor and fuser estimates. In particular, the impact of filtering and fusion, communication loss and delay, sensor bias, and information feedback on the accuracy and consistency of error measures is investigated by means of studying a maneuvering target tracking application.