Quantized Kalman Filter for Sensor Networks with Random Packet Dropouts

Abstract:

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Based on the projection theory and the uniform quantization method, a quantized Kalman filter is presented for sensor networks with random packet dropouts from sensors to the fusion center (filter). The bandwidths are scheduled by the optimality index that energy consumption is minimized under a given performance constraint. Compared with the filter without quantization, the quantized filter given can reduce the energy consumption and has an effective tracking performance. A simulation example demonstrates the effectiveness of the proposed algorithm.

Info:

Periodical:

Advanced Materials Research (Volumes 219-220)

Edited by:

Helen Zhang, Gang Shen and David Jin

Pages:

1040-1044

DOI:

10.4028/www.scientific.net/AMR.219-220.1040

Citation:

N. Liu et al., "Quantized Kalman Filter for Sensor Networks with Random Packet Dropouts", Advanced Materials Research, Vols. 219-220, pp. 1040-1044, 2011

Online since:

March 2011

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Price:

$35.00

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