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the nonlinear observation equations.
where and are the linear state transition and nonlinear observation functions, respectively.
An unlimited number of compromised measurements was supposed, and a more realistic nonlinear observation model was used.
We have used the proposed approach on the problem of multi-target tracking with a nonlinear observation model.
This paper deals with the nonlinear observation theory as it is applied to the class of rigid robot manipulator systems.
In our simulation, a classic two-dimensional (2-D) fusion scenario with the nonlinear observation of one active and two passive radars is considered.
Similar(42)
Both algorithms deal with nonlinear observations using the extended Kalman filtering.
Extended Kalman filtering is employed to process the nonlinear observations and track each target's location using the observations of the corresponding set ({mathcal {S}}_{rho _{ell },t}).
Theorem 4.1 A nonlinear augmented complex predictor of the signal s(t k ) based on the set of nonlinear observations {y t1),..., y(t n ), y* (t1),..., y*(t n )} of the form (25) can be determined through the equation ŝ ( t k | t n ) = a [ 1 ] ( t k ) ϵ ( t n ).
It is shown, that within the proposed nonlinear observer design framework, the observation error generated by neglecting the fast process dynamics is of order O, where ɛ is the perturbation parameter and a measure of the relative speed/time-constant of the fast and the slow component of the process dynamics.
The tracking problem was formulated as a cross-layer optimization withashe aim of maximizing the total utility function in the cluster.
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CEO of Professional Science Editing for Scientists @ prosciediting.com