Versatile visual servoing without knowledge of true jacobian.pdf cobian matrix estimator.
The Jacobian matrix estimator does not need a priori
knowledge of the kinematic structure and parameters
of the robot system, such as camera and link parameters.
The proposed visual servoing control scheme ensures
the convergence of the image-features to desired
trajectories, by using the estimated Jacobian matrix,
which is proved by the Lyapunov stability theory. To
show the effectiveness of the proposed scheme, simulation
and experimental results are presented.
A method is presented for augmenting an extended
Kalman filter with an adaptive element. The resulting estimator
provides robustness to parameter uncertainty and unmodeled
dynamics.
This paper examines the asymptotic (large sample) performance
of a family of non-data aided feedforward (NDA FF) nonlinear
least-squares (NLS) type carrier frequency estimators for burst-mode
phase shift keying (PSK) modulations transmitted through AWGN and
flat Ricean-fading channels. The asymptotic performance of these estimators
is established in closed-form expression and compared with the
modified Cram`er-Rao bound (MCRB). A best linear unbiased estimator
(BLUE), which exhibits the lowest asymptotic variance within the family
of NDA FF NLS-type estimators, is also proposed.
In recent years large scientific interest has been
devoted to joint data decoding and parameter estimation
techniques. In this paper, iterative turbo decoding joint
to channel frequency and phase estimation is proposed.
The phase and frequency estimator is embedded into the
structure of the turbo decoder itself, taking into consideration
both turbo interleaving and puncturing. Results
show that the proposed technique outperforms conventional
approaches both in terms of detection capabilities and
implementation complexity.
