TY - GEN
T1 - Linear minimum mean square error estimation for linear systems with stochastic state equality constraints
AU - Yang, Yanbo
AU - Pan, Quan
AU - Liang, Yan
AU - Yang, Feng
PY - 2013/10/18
Y1 - 2013/10/18
N2 - In the practical linear system with the state equality constraint, the constraint may exist stochastically. Considered this phenomenon, the stochastic distribution of the state equality constraint is modeled as the Bernoulli distribution, and regarded it as the perfect measurement drew into the measurement equation in this paper. A tiny noise may be subjoined on it to avoid leading the covariance of the augmented measurement noise singular. At this time, the original system can be generalized to the linear system with a part of its measurement received stochastically. Aimed at this system, the linear minimum mean square error estimator (LMMSE) is derived according to the orthogonality principle. The simulation result shows that the proposed method has a same performance with the traditional method which is used to deal with the linear system with the state equality constraint when the constraint is existed all the time. However, the performance of the proposed method is prior to that of the traditional method when the state equality constraint is existed stochastically.
AB - In the practical linear system with the state equality constraint, the constraint may exist stochastically. Considered this phenomenon, the stochastic distribution of the state equality constraint is modeled as the Bernoulli distribution, and regarded it as the perfect measurement drew into the measurement equation in this paper. A tiny noise may be subjoined on it to avoid leading the covariance of the augmented measurement noise singular. At this time, the original system can be generalized to the linear system with a part of its measurement received stochastically. Aimed at this system, the linear minimum mean square error estimator (LMMSE) is derived according to the orthogonality principle. The simulation result shows that the proposed method has a same performance with the traditional method which is used to deal with the linear system with the state equality constraint when the constraint is existed all the time. However, the performance of the proposed method is prior to that of the traditional method when the state equality constraint is existed stochastically.
KW - Bernoulli distribution
KW - linear minimum mean square error estimator
KW - linear system
KW - state equality constraint
UR - http://www.scopus.com/inward/record.url?scp=84890503387&partnerID=8YFLogxK
M3 - 会议稿件
AN - SCOPUS:84890503387
SN - 9789881563835
T3 - Chinese Control Conference, CCC
SP - 4747
EP - 4752
BT - Proceedings of the 32nd Chinese Control Conference, CCC 2013
PB - IEEE Computer Society
T2 - 32nd Chinese Control Conference, CCC 2013
Y2 - 26 July 2013 through 28 July 2013
ER -