Eigen Value Analysis in Lower Bounding Uncertainty of Kalman Filter Estimates

2020/03/12 by Das, Niladri, Bhattacharya, Raktim
#FOS: Electrical engineering #FOS: Mathematics #Optimization and Control (math.OC) #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2003.06029

Abstract

In this paper we are concerned with the error-covariance lower-bounding problem in Kalman filtering: a sensor releases a set of measurements to the data fusion/estimation center, which has a perfect knowledge of the dynamic model, to allow it to estimate the states, while preventing it to estimate the states beyond a given accuracy. We propose a measurement noise manipulation scheme to ensure lower-bound on the estimation accuracy of states. Our proposed method ensures lower-bound on the steady state estimation error of Kalman filter, using mathematical tools from eigen value analysis.

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