Accurate Events Synchronization in a System-on-Chip Navigation Receiver

International audienceA System-On-Chip design and synchronization details of a navigation receiver are presented. The architecture of the GNSS receiver is easily modifiable and offers the capability of accurate time management, thanks to the use of a co-design approach. The purpose of such a platform is to allow real time validation of research algorithms. A secondary application is education, as this platform can be used to study signal demodulation and navigation. The receiver is fully functional, but further developments are still undergoing. Results demonstrate accuracy, flexibility and ease of use of the system

1981 : Châteaux forts et villes fortifiées d’Alsace

Association régionale fondée en 1981, membre de la Fédération des Sociétés d’Histoire et d’Archéologie d’Alsace, l’ACF s’efforce de médiatiser au mieux les trouvailles divulguées par les chercheurs. Elle regroupe, dans un esprit convivial, environ 250 membres qui souhaitent apprendre à connaître ou mieux appréhender ce patrimoine si riche, témoin des grandes heures de l’Alsace du Moyen Age. L’association est inscrite au registre des associations du Tribunal d’Instance de Mulhouse Vol. XXXVIII..

Vergence tracking: a tool to assess oculomotor performance in stereoscopic displays

Oculomotor conflict induced between the accommodative and vergence components in stereoscopic displays represents an unnatural viewing condition. There is now some evidence that stereoscopic viewing may induce discomfort and changes in oculomotor parameters. The present study sought to measure oculomotor performance during stereoscopic viewing. Using a 3D stereo setup and an eye-tracker, vergence responses were measured during 20-min exposure to a virtual visual target oscillating in depth, which participants had to track. The results showed a significant decline in the amplitude of the in-depth oscillatory vergence response over time. We propose that eye-tracking provides a useful tool to objectively assess the timevarying alterations of the vergence system when using stereoscopic displays

(Disparity-Driven) Accommodation Response Contributes to Perceived Depth

When looking at objects at various distances in the physical space, the accommodation and vergence systems adjust their parameters to provide a single and clear vision of the world. Subtended muscle activity provides oculomotor cues that can contribute to the perception of depth and distance. While several studies have outlined the role of vergence in distance perception, little is known about the contribution of its concommitant accommodation component. It is possible to unravel the role of each of these physiological systems by placing observers in a situation where there is a conflict between accommodation and vergence distances. We thus sought to determine the contribution of each response system to perceived depth by simultaneously measuring vergence and accommodation while participants judged the depth of 3D stimuli. The distance conflict decreased depth constancy for stimulus displayed with negative disparity steps (divergence). Although vergence was unaffected by the stimulus distance, accommodation responses were significantly reduced when the stimulus was displayed with negative disparities. Our results show that biases in perceived depth follow undershoots in the disparity-driven accommodation response. These findings suggest that accommodation responses (i.e., from oculomotor information) can contribute to perceived depth

Accurate Events Synchronization in a System-on-Chip Navigation Receiver

A System-On-Chip design and synchronization details of a navigation receiver are presented. The architecture of the GNSS receiver is easily modifiable and offers the capability of accurate time management, thanks to the use of a co-design approach. The purpose of such a platform is to allow real time validation of research algorithms. A secondary application is education, as this platform can be used to study signal demodulation and navigation. The receiver is fully functional, but further developments are still undergoing. Results demonstrate accuracy, flexibility and ease of use of the system

Robust GNSS phase tracking in case of slow dynamics using variational Bayes inference

For a precise GNSS (Global Navigation Satellite System) positioning, carrier phase measurements are required. However, cycle slipping in classical phase locked loop (PLL) can lead to a local or permanent loss of lock. To address this problem, we propose a robust nonlinear filter for carrier phase tracking based on Variational Bayes (VB) inference. So far, the algorithm is designed only for slow phase dynamics (i.e., first order loop). Interestingly, the estimator update equation can be expressed in closed form. Performance of our algorithm is assessed on synthetic and experimental GNSS data and compared to that of conventional PLL-based techniques. Results show that the proposed method brings significant improvement in terms of cycle slipping

On LMVDR Estimators for LDSS Models: Conditions for Existence and Further Applications

For linear discrete state-space models, under certain conditions, the linear least mean squares (LLMS) filter estimate has a recursive format, a.k.a. the Kalman filter (KF). Interestingly, the linear minimum variance distortionless response (LMVDR) filter, when it exists, shares exactly the same recursion as the KF, except for the initialization. If LMVDR estimators are suboptimal in mean-squared error sense, they do not depend on the prior knowledge on the initial state. Thus, the LMVDR estimators may outperform the usual LLMS estimators in case of misspecification of the prior knowledge on the initial state. In this perspective, we establish the general conditions under which existence of the LMVDRF is guaranteed. An immediate benefit is the introduction of LMVDR fixed-point and fixed-lag smoothers (and possibly other smoothers or predictors), which has not been possible so far. Indeed, the LMVDR fixed-point smoother can be used to compute recursively the solution of a generalization of the deterministic least-squares problem

New Results on LMVDR Estimators for LDSS Models

In the context of linear discrete state-space (LDSS) models, we generalize a result lately introduced in the restricted case of invertible state matrices, namely that the linear minimum variance distortionless response (LMVDR) filter shares exactly the same recursion as the linear least mean squares (LLMS) filter, aka the Kalman filter (KF), except for the initialization. An immediate benefit is the introduction of LMVDR fixed-point and fixed-lag smoothers (and possibly other smoothers or predictors), which has not been possible so far. This result is particularly noteworthy given the fact that, although LMVDR estimators are sub-optimal in mean-squared error sense, they are infinite impulse response distortionless estimators which do not depend on the prior knowledge on the mean and covariance matrix of the initial state. Thus the LMVDR estimators may outperform the usual LLMS estimators in case of misspecification of the prior knowledge on the initial state. Seen from this perspective, we also show that the LMVDR filter can be regarded as a generalization of the information filter form of the KF. On another note, LMVDR estimators may also allow to derive unexpected results, as highlighted with the LMVDR fixed-point smoother

Minimum Variance Distortionless Response Estimators for Linear Discrete State-Space Models

For linear discrete state-space models, under certain conditions, the linear least-mean-squares filter estimate has a convenient recursive predictor/corrector format, aka the Kalman filter. The purpose of this paper is to show that the linear minimum variance distortionless response (MVDR) filter shares exactly the same recursion, except for the initialization which is based on a weighted least-squares estimator. If the MVDR filter is suboptimal in mean-squared error sense, it is an infinite impulse response distortionless filter (a deconvolver) which does not depend on the prior knowledge (first- and second-order statistics) on the initial state. In other words, the MVDR filter can be pre-computed and its behaviour can be assessed in advance independently of the prior knowledge on the initial state