Leong, Alex S. and Dey, Subhrakanti and Nair, Girish N. (2013) Quantized filtering schemes for multi-sensor linear state estimation: Stability and performance under high rate quantization. IEEE Transactions on Signal Processing, 61 (15). pp. 3852-3865. ISSN 1053-587X

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Abstract

In this paper we consider state estimation of a discrete time linear system using multiple sensors, where the sensors quantize their individual innovations, which are then combined at the fusion center to form a global state estimate. We prove the stability of the estimation scheme under sufficiently high bit rates. We obtain asymptotic approximations for the error covariance matrix that relates the system parameters and quantization levels used by the different sensors. Numerical results show close agreement with the true error covariance for quantization at high rates. An optimal rate allocation problem amongst the different sensors is also considered.

Item Type:	Article
Additional Information:	Cite as: A. S. Leong, S. Dey and G. N. Nair, "Quantized Filtering Schemes for Multi-Sensor Linear State Estimation: Stability and Performance Under High Rate Quantization," in IEEE Transactions on Signal Processing, vol. 61, no. 15, pp. 3852-3865, Aug.1, 2013, doi: 10.1109/TSP.2013.2264465.
Keywords:	Kalman filtering; quantization; sensor networks; stability; state estimation;
Academic Unit:	Faculty of Science and Engineering > Electronic Engineering Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	14314
Identification Number:	https://doi.org/10.1109/TSP.2013.2264465
Depositing User:	Subhrakanti Dey
Date Deposited:	08 Apr 2021 16:30
Journal or Publication Title:	IEEE Transactions on Signal Processing
Publisher:	Institute of Electrical and Electronics Engineers
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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