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The proposed controller comprises of an independent joint controller, designed in the configuration space, and a sliding mode controller that enforces desired dynamics for the tracking error projections to the Frenet-Serret frame.
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They are calculated based on the error projection matrix in the back projection process.
Therefore, we can also apply error projection (6) on the unconstrained estimate of each Kalman filter.
The input (innovation vectors) to mode probability computation step is a modified version after error projection.
Error projection was proposed after both predict and update steps have been implemented.
The proposed radial basis function least square error projection cascade network for sparse signal Recovery (RASR) utilizes the smoothed L0 norm optimization, L2 least square error projection and feedback network model to improve the signal recovery performance over the existing CSIANN algorithm.
In single-model Kalman filtering, error projection method can be applied on the unconstrained Kalman filter estimate to meet the constraints.
Note, however, the update step in each Kalman filter still use the unchanged predicted state vector because there will be another error projection step after update.
To make the mode probabilities to better reflect the influence of the black-border constraints, we propose to insert an additional error projection step between the predict and update steps in each Kalman filters in the IMM algorithm.
A 2D Butterworth pre-reconstruction filter was used with cut-off frequency of 0.45, order 5. Attenuation corrected images were reconstructed with an iterative algorithm, 6 iterations where a ramp filter was applied on the error projection prior to backprojection.
A 2D Butterworth prereconstruction filter was used with a cutoff frequency of 0.45, order 5. Attenuation-corrected images were reconstructed with an iterative algorithm, with six iterations [8] where a ramp filter was applied on the error projection prior to back projection.
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