A New MIMU/GNSS Ultra-Tightly Coupled Integration Architecture for Mitigating Abrupt Changes of Frequency Tracking Errors.

We present a new ultra-tightly coupled (UTC) integration architecture of a micro-electromechanical inertial measurement unit (MIMU) and global navigation satellite system (GNSS) to reduce the performance degradation caused by abrupt changes of frequency tracking errors. A large frequency error will lead to a decrease in the carrier-to-noise ratio (C/N0) estimate and an increase in the code discriminator estimation error. The disruptive effects of frequency errors on the estimation of C/N0 and on the code discriminator are quantitatively evaluated via theoretical analyses and Monte Carlo simulations. The new MIMU/GNSS UTC architecture introduces a large frequency error detector and a refined frequency processor based on a retuned frequency in each tracking channel. In addition, an adaptive channel prefilter with multiple fading factors is introduced as an alternate to the conventional prefilter. Numerical simulations based on a highly dynamic trajectory are used to assess performance. The simulation results show that when there is an abrupt step change in the frequency tracking error, the new UTC architecture can effectively suppress the divergence of navigation solutions and the loss of tracking lock, and can significantly reduce the deviation of the C/N0 estimation.

Adaptive disturbance cancellation for a class of MIMO nonlinear Euler-Lagrange systems under input saturation.

In this paper, we develop an adaptive disturbance cancellation tracking control strategy with the asymptotically converging output tracking errors for a class of multi-input and multi-output (MIMO) nonlinear Euler-Lagrange systems with unknown time-varying disturbances under input saturation. The unknown time-varying disturbances are firstly described as the outputs of a multivariate linear unknown exosystem with unavailable regressor and regression parameter. To estimate the unavailable regressor, an observer is constructed, such that the disturbance cancellation is converted to an adaptive control problem. An auxiliary dynamic system (ADS) is employed to mitigate the effect of the input saturation. Then, a robust adaptive tracking control law is proposed incorporating the above and a robustifying term into the adaptive vectorial backstepping design tool. It is proven theoretically that the asymptotic convergence of the output tracking errors of Euler-Lagrange systems is achieved, while the uniform ultimate boundedness of the remaining signals of the resulting closed-loop control system is guaranteed. Simulation and comparison results on a two-link rigid manipulator and a scale model ship are presented to illustrate the effectiveness and the superiority of the proposed control scheme.

Gesture-based registration correction using a mobile augmented reality image-guided neurosurgery system.

In image-guided neurosurgery, a registration between the patient and their pre-operative images and the tracking of surgical tools enables GPS-like guidance to the surgeon. However, factors such as brainshift, image distortion, and registration error cause the patient-to-image alignment accuracy to degrade throughout the surgical procedure no longer providing accurate guidance. The authors present a gesture-based method for manual registration correction to extend the usage of augmented reality (AR) neuronavigation systems. The authors' method, which makes use of the touchscreen capabilities of a tablet on which the AR navigation view is presented, enables surgeons to compensate for the effects of brainshift, misregistration, or tracking errors. They tested their system in a laboratory user study with ten subjects and found that they were able to achieve a median registration RMS error of 3.51 mm on landmarks around the craniotomy of interest. This is comparable to the level of accuracy attainable with previously proposed methods and currently available commercial systems while being simpler and quicker to use. The method could enable surgeons to quickly and easily compensate for most of the observed shift. Further advantages of their method include its ease of use, its small impact on the surgical workflow and its small-time requirement.

Reverse retrospective motion correction.

PURPOSE: One potential barrier for using prospective motion correction (PMC) in the clinic is the unpredictable nature of a scan because of the direct interference with the imaging sequence. We demonstrate that a second set of "de-corrected" images can be reconstructed from a scan with PMC that show how images would have appeared without PMC enabled. THEORY AND METHODS: For three-dimensional scans, the effects of PMC can be undone by performing a retrospective reconstruction based on the inverse of the transformation matrix used for real time gradient feedback. Retrospective reconstruction is performed using a generalized SENSE approach with continuous head motion monitored using a single-marker optical camera system. RESULTS: Reverse retrospective reconstruction is demonstrated for phantom and in vivo scans using an magnetization-prepared rapid gradient echo (MPRAGE) sequence including parallel and Partial Fourier acceleration. CONCLUSION: Reverse retrospective reconstruction can almost perfectly undo the effects of prospective feedback, and thereby provide a second image data set with the effects of motion correction removed. In case of correct feedback, one can directly compare the quality of the corrected with that of the uncorrected scan. Additionally, because erroneous feedback during PMC may introduce artifacts, it is possible to eliminate artifacts in a corrupted scan by reversing the false gradient updates. Magn Reson Med 75:2341-2349, 2016. © 2015 Wiley Periodicals, Inc.