TY - GEN
T1 - Heterogeneous Sensor Fusion with out of Sync Data
AU - Chen, Biao
AU - Varshney, Pramod K.
AU - Zulch, Peter
AU - Distasio, Marcello
AU - Niu, Ruixin
AU - Shen, Dan
AU - Lu, Jingyang
AU - Chen, Genshe
N1 - Funding Information:
VI. ACKNOWLEDGEMENT This work was supported by U.S. Air Force under Contract FA8750-17-C-0298. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the United States Air Force. This paper was cleared for public release by the United States Air Force, Case #: 88ABW-2019-4841.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/3
Y1 - 2020/3
N2 - This paper tackles sensor fusion when data from different sensors are out of sync - observations may be sampled asynchronously and there may be no timing information at the fusion center for observations from some sensors. This often happens when sensors are heterogeneous in nature hence may employ different and even time varying sampling cycles. Furthermore, observations may need to go through wireless channels when tagging each observation with a timestamp may be too expensive. Treating the timing information in out of sync data as a hidden variable, this paper employs the expectation-maximization (EM) algorithm to recover the posterior distribution of the timing information. Data fusion utilizing the out of sync data can subsequently be carried out. Using a simple two-sensor off-line state estimation experiment where one sensor's data is available but is out of sync at the other sensor, we demonstrate that the performance of the EM based approach improves on the fusion performance when the out of sync data is discarded. Extensions to high dimensional observations to systems involving multiple out of sync data streams, and to online state tracking with out of sync data are discussed.
AB - This paper tackles sensor fusion when data from different sensors are out of sync - observations may be sampled asynchronously and there may be no timing information at the fusion center for observations from some sensors. This often happens when sensors are heterogeneous in nature hence may employ different and even time varying sampling cycles. Furthermore, observations may need to go through wireless channels when tagging each observation with a timestamp may be too expensive. Treating the timing information in out of sync data as a hidden variable, this paper employs the expectation-maximization (EM) algorithm to recover the posterior distribution of the timing information. Data fusion utilizing the out of sync data can subsequently be carried out. Using a simple two-sensor off-line state estimation experiment where one sensor's data is available but is out of sync at the other sensor, we demonstrate that the performance of the EM based approach improves on the fusion performance when the out of sync data is discarded. Extensions to high dimensional observations to systems involving multiple out of sync data streams, and to online state tracking with out of sync data are discussed.
KW - Expectation-Maximization
KW - Heterogeneous Sensor Fusion
KW - Out of Sync Data
UR - http://www.scopus.com/inward/record.url?scp=85092555246&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85092555246&partnerID=8YFLogxK
U2 - 10.1109/AERO47225.2020.9172681
DO - 10.1109/AERO47225.2020.9172681
M3 - Conference contribution
AN - SCOPUS:85092555246
T3 - IEEE Aerospace Conference Proceedings
BT - 2020 IEEE Aerospace Conference, AERO 2020
PB - IEEE Computer Society
T2 - 2020 IEEE Aerospace Conference, AERO 2020
Y2 - 7 March 2020 through 14 March 2020
ER -