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We consider the impact of noise on a modification of a recently developed attitude estimation algorithm which is formulated directly as a rotation matrix tracking problem on SO(3). By means of a stochastic Lyapunov analysis we derive several results. Firstly in the presence of noise the estimation algorithm no longer converges. Rather it fluctuates in the vicinity of the true rotation matrix and angular...
This study addresses a robust filtering design problem for linear stochastic partial differential systems (LSPDSs) with external disturbance and measurement noise in the spatio-temporal domain. For LSPDSs, the robust filter design via a set of sensor measurements needs to solve a complex Hamilton Jacobi integral inequality (HJII) for robust state estimation despite external...
A hierarchical state bounding estimation method is presented for nonlinear dynamic systems where different sensors offer several measurements of the same state vector, each of which is subject to unknown but bounded disturbances and is equipped with a local processor. For each sampling time, the proposed algorithm proceeds in two stages. At the prediction stage, an approximating outer-bounding ellipsoid...
In this paper, we present a joint state and adaptive parameter identification scheme for the cases when all the states of the system are measured and when only some states of the system are measured. When all the states are measured, we show that, in the presence of process and measurement noise, the state and parameter estimation errors are bounded. To this end, we show that this is possible only...
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