On the Complexity and Approximability of Optimal Sensor Selection and Attack for Kalman Filtering

Given a linear dynamical system affected by stochastic noise, we consider the problem of selecting an optimal set of sensors (at design time) to minimize the trace of the steady-state a priori or a posteriori error covariance of the Kalman filter, subject to certain selection budget constraints. We...

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Bibliographic Details
Published in:	IEEE transactions on automatic control Vol. 66; no. 5; pp. 2146 - 2161
Main Authors:	Ye, Lintao, Woodford, Nathaniel, Roy, Sandip, Sundaram, Shreyas
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.05.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Approximation Approximation algorithms Budgets Computational complexity Cost function Covariance Covariance matrices Estimation error Greedy algorithms Kalman filters Mathematical analysis Noise measurement optimization Polynomials sensor systems Sensors state estimation Steady state Time constant
ISSN:	0018-9286, 1558-2523
Online Access:	Get full text
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