Previous works produced the robust square-root PKF algorithm for improving a numerical stability of the estimator with respect to roundoff errors.
To solve the problem that when the model uncertainties exists or the prior noise statistics are unknown, the accuracy of the cubature Kalman filter (CKF) will decline or diverge, an adaptive robust square-root CKF ARSCKFF) algorithm with the noise statistic estimator is proposed.
By using the Lyapunov functional method and the linear matrix inequality technology, sufficient conditions for the robust mean square stable (RMSS) of the NCSs with an H∞ norm bound γ are obtained.
In this work we derive necessary and sufficient conditions for robust mean square stability of discrete-time time-inhomogeneous Markov jump linear systems (MJLSs) affected by polytopic uncertainties on transition probabilities and bounded disturbances.
More recently, in [8], a channel estimator is proposed based on a robust least-squares approach.
This paper presents a weighted differencing method using robust least-squares regression for estimating many important error characteristics.
By linear matrix inequality (LMI) approach, sufficient conditions are obtained, which guarantee the robust mean-square exponential stability of the system with an H∞ performance.
New delay-dependent conditions of robust mean-square exponential stability are developed by applying the Lyapunov Krasovskii functional method and introducing free-weighting matrices.
We estimate (epsilon _n^m) by using a robust least-squares fitting of the observed magnetic fields (corrected for core and crustal contributions with the CHAOS-6 model Finlay et al. 2016) from a number of globally distributed magnetic observatories.
In this work, a constrained robust least squares (CRLS) estimator is combined with a branch-and-bound global optimization solver for iteratively identifying source release histories and source locations.
