TY - GEN
T1 - Distributed Noise Covariance Matrices Estimation in Sensor Networks
AU - Li, Jiahong
AU - Ma, Nan
AU - Deng, Fang
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/14
Y1 - 2020/12/14
N2 - Adaptive algorithms based on in-network processing over networks are useful for online parameter estimation of historical data (e.g., noise covariance) in predictive control and machine learning areas. This paper focuses on the distributed noise covariance matrices estimation problem for multi-sensor linear time-invariant (LTI) systems. Conventional noise covariance estimation approaches, e.g., auto-covariance least squares (ALS) method, suffers from the lack of the sensor's historical measurements and thus produces high variance of the ALS estimate. To solve the problem, the distributed auto-covariance least squares (D-ALS) algorithm is proposed based on the batch covariance intersection (BCI) method by enlarging the innovations from the neighbors. The accuracy analysis of DALS algorithm is given to show the decrease of the variance of the D-ALS estimate. The numerical results of cooperative target tracking tasks in static and mobile sensor networks are demonstrated to show the feasibility and superiority of the proposed D-ALS algorithm.
AB - Adaptive algorithms based on in-network processing over networks are useful for online parameter estimation of historical data (e.g., noise covariance) in predictive control and machine learning areas. This paper focuses on the distributed noise covariance matrices estimation problem for multi-sensor linear time-invariant (LTI) systems. Conventional noise covariance estimation approaches, e.g., auto-covariance least squares (ALS) method, suffers from the lack of the sensor's historical measurements and thus produces high variance of the ALS estimate. To solve the problem, the distributed auto-covariance least squares (D-ALS) algorithm is proposed based on the batch covariance intersection (BCI) method by enlarging the innovations from the neighbors. The accuracy analysis of DALS algorithm is given to show the decrease of the variance of the D-ALS estimate. The numerical results of cooperative target tracking tasks in static and mobile sensor networks are demonstrated to show the feasibility and superiority of the proposed D-ALS algorithm.
UR - http://www.scopus.com/inward/record.url?scp=85099880505&partnerID=8YFLogxK
U2 - 10.1109/CDC42340.2020.9303944
DO - 10.1109/CDC42340.2020.9303944
M3 - Conference contribution
AN - SCOPUS:85099880505
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 1158
EP - 1163
BT - 2020 59th IEEE Conference on Decision and Control, CDC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 59th IEEE Conference on Decision and Control, CDC 2020
Y2 - 14 December 2020 through 18 December 2020
ER -