DocumentCode :
1602554
Title :
Recursive quantized state estimation of discrete-time linear stochastic systems
Author :
Keyou You ; Yanlong Zhao ; Lihua Xie
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore
fYear :
2009
Firstpage :
170
Lastpage :
175
Abstract :
This paper studies the state estimation problem for linear discrete-time systems based on the minimum mean square error (MMSE) criterion. Under the Gaussian assumption on the predicted density, the quantized MMSE filter is derived which has a similar form as the Kalman filter with the raw measurement simply replaced by its quantized version. The quantization effects are explicitly quantified by adding a nonnegative term to the filtering error covariance derived from the Kalman filter at the measurement update step. Finally, experimental results demonstrate the efficiency of the proposed filtering algorithms.
Keywords :
Gaussian processes; Kalman filters; covariance analysis; discrete time filters; error statistics; filtering theory; least mean squares methods; linear systems; quantisation (signal); recursive estimation; recursive filters; state estimation; stochastic systems; Gaussian assumption; Kalman filter; discrete-time linear stochastic system; error covariance; filtering algorithm; minimum mean square error criterion; nonnegative term; quantization effect; quantized MMSE filter; raw measurement; recursive quantized state estimation problem; Control systems; Energy consumption; Error correction; Filtering algorithms; Filters; Quantization; State estimation; Stochastic systems; Technological innovation; Wireless sensor networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Asian Control Conference, 2009. ASCC 2009. 7th
Conference_Location :
Hong Kong
Print_ISBN :
978-89-956056-2-2
Type :
conf
Filename :
5276239
Link To Document :
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