Quantized Filtering Schemes for Multi-Sensor Linear State Estimation: Stability and Performance Under High Rate Quantization
2013 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 61, no 15, 3852-3865 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2013. Vol. 61, no 15, 3852-3865 p.
Kalman filtering, quantization, sensor networks, stability, state estimation
Natural Sciences Engineering and Technology
Research subject Electrical Engineering with specialization in Signal Processing
IdentifiersURN: urn:nbn:se:uu:diva-204842DOI: 10.1109/TSP.2013.2264465ISI: 000321669200012OAI: oai:DiVA.org:uu-204842DiVA: diva2:640309