Date of Award


Level of Access Assigned by Author

Open-Access Thesis

Degree Name

Master of Science (MS)


Computer Engineering


Donald M. Hummels

Second Committee Member

Yifeng Zhu

Third Committee Member

Richard Eason


An algorithm called the extended Kalman-Consensus filter is developed as an extension of the Kalman-Consensus filter to the non-linear case. The extended Kalman- Consensus filter is a technique for estimating the state of a non-linear process disturbed by noise using multiple observations from a distributed set of sense nodes. All sense nodes attempt to estimate the same state by determining how their observations affect that state, and by communicating with neighbor nodes. The algorithm is designed to be more accurate through measurement diversity, scalable to a large number of nodes, and robust against loss of nodes during operation. Simulations are used to compare the performance of the algorithm to the standard extended Kalman filter, the central extended Kalman filter, and the distributed extended Kalman filter. The extended Kalman-Consensus filter performs more accurate estimation than the standard and distributed extended Kalman filters, and is more scalable than the central extended Kalman filter, with a similar degree of estimation accuracy.