Title :
Quantized Kalman Filtering
Author :
Sun, Shuli ; Lin, Jianyong ; Xie, Lihua ; Xiao, Wendong
Author_Institution :
Heilongjiang Univ., Harbin
Abstract :
This paper is concerned with the estimation problem for a dynamic stochastic estimation in a sensor network. Firstly, the quantized Kalman filter based on the quantized observations (QKFQO) is presented. Approximate solutions for two optimal bandwidth scheduling problems are given, where the tradeoff between the number of quantization levels or the bandwidth constraint and the energy consumption is considered. However, for a large observed output, quantizing observations will result in large information loss under the limited bandwidth. To reduce the information loss, another quantized Kalman filter based on quantized innovations (QKFQI) is developed, which requires that the fusion center broadcast the one-step prediction of state and innovation variances to the tasking sensor nodes. Compared with QKFQO, QKFQI has better accuracy. Simulations show the effectiveness.
Keywords :
Kalman filters; distributed sensors; quantisation (signal); dynamic stochastic estimation; energy consumption; optimal bandwidth scheduling problems; quantized Kalman filtering; quantized observations; sensor network; Bandwidth; Intelligent sensors; Kalman filters; Parameter estimation; Quantization; Sensor fusion; Sensor systems; State estimation; Technological innovation; Wireless sensor networks;
Conference_Titel :
Intelligent Control, 2007. ISIC 2007. IEEE 22nd International Symposium on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-0440-7
Electronic_ISBN :
2158-9860
DOI :
10.1109/ISIC.2007.4450852