An FGO-based Unified Initial Alignment Method of Strapdown Inertial Navigation System

The initial alignment process can provide an accurate initial attitude of strapdown inertial navigation system. The conventional two-procedure method usually includes coarse and fine alignment processes. Coarse alignment converges fast because of its batch estimating characteristics and the initial attitude does not influence the results. But coarse alignment is low accuracy without considering the IMU's bias. The fine alignment is more accurate by applying a recursive Bayesian filter to estimate the IMU's bias, but the attitude converges slowly as the initial value influence the convergence speed of the recursive filter. Researchers have proposed the unified initial alignment to achieve initial alignment in one procedure, existing unified methods make improvements on the basics of recursive Bayesian filter and those methods are still slow to converge. In this paper, a unified method based on batch estimator FGO (factor graph optimization) is raised, which is converge fast like coarse alignment and accurate than the existing method. We redefine the state and rederivation the state dynamic model first. Then, the optimal attitude and the IMU's bias are estimated simultaneously through FGO. The fast convergence and high accuracy of this method are verified by simulation and physical experiments on a rotation SINS.Comment: 9 pages, Journal Paper

Imaging Molecules from Within: Ultra-fast, {\AA}ngstr\"om Scale Structure Determination of Molecules via Photoelectron Holography using Free Electron Lasers

A new scheme based on (i) upcoming brilliant X-ray Free Electron Laser (FEL) sources, (ii) novel energy and angular dispersive, large-area electron imagers and (iii) the well-known photoelectron holography is elaborated that provides time-dependent three-dimensional structure determination of small to medium sized molecules with {\AA}ngstr\"om spatial and femtosecond time resolution. Inducing molecular dynamics, wave-packet motion, dissociation, passage through conical intersections or isomerization by a pump pulse this motion is visualized by the X-ray FEL probe pulse launching keV photoelectrons within few femtoseconds from specific and well-defined sites, deep core levels of individual atoms, inside the molecule. On their way out the photoelectrons are diffracted generating a hologram on the detector that encodes the molecular structure at the instant of photoionization, thus providing 'femtosecond snapshot images of the molecule from within'. Detailed calculations in various approximations of increasing sophistication are presented and three-dimensional retrieval of the spatial structure of the molecule with {\AA}ngstr\"om spatial resolution is demonstrated. Due to the large photo-absorption cross sections the method extends X-ray diffraction based, time-dependent structure investigations envisioned at FELs to new classes of samples that are not accessible by any other method. Among them are dilute samples in the gas phase such as aligned, oriented or conformer selected molecules, ultra-cold ensembles and/or molecular or cluster objects containing mainly light atoms that do not scatter X-rays efficiently.Comment: 18 pages, 11 figure

Laser diagnostics and minor species detection in combustion using resonant four-wave mixing

Peer reviewedPostprin

Machine-learning nonstationary noise out of gravitational-wave detectors

Signal extraction out of background noise is a common challenge in high-precision physics experiments, where the measurement output is often a continuous data stream. To improve the signal-to-noise ratio of the detection, witness sensors are often used to independently measure background noises and subtract them from the main signal. If the noise coupling is linear and stationary, optimal techniques already exist and are routinely implemented in many experiments. However, when the noise coupling is nonstationary, linear techniques often fail or are suboptimal. Inspired by the properties of the background noise in gravitational wave detectors, this work develops a novel algorithm to efficiently characterize and remove nonstationary noise couplings, provided there exist witnesses of the noise source and of the modulation. In this work, the algorithm is described in its most general formulation, and its efficiency is demonstrated with examples from the data of the Advanced LIGO gravitational-wave observatory, where we could obtain an improvement of the detector gravitational-wave reach without introducing any bias on the source parameter estimation

Adaptive feedback analysis and control of programmable stimuli for assessment of cerebrovascular function

The assessment of cerebrovascular regulatory mechanisms often requires flexibly controlled and precisely timed changes in arterial blood pressure (ABP) and/or inspired CO2. In this study, a new system for inducing variations in mean ABP was designed, implemented and tested using programmable sequences and programmable controls to induce pressure changes through bilateral thigh cuffs. The system is also integrated with a computer-controlled switch to select air or a CO2/air mixture to be provided via a face mask. Adaptive feedback control of a pressure generator was required to meet stringent specifications for fast changes, and accuracy in timing and pressure levels applied by the thigh cuffs. The implemented system consists of a PC-based signal analysis/control unit, a pressure control unit and a CO2/air control unit. Initial evaluations were carried out to compare the cuff pressure control performances between adaptive and non-adaptive control configurations. Results show that the adaptive control method can reduce the mean error in sustaining target pressure by 99.57 % and reduce the transient time in pressure increases by 45.21 %. The system has proven a highly effective tool in ongoing research on brain blood flow control

A minimalistic approach to appearance-based visual SLAM

This paper presents a vision-based approach to SLAM in indoor / outdoor environments with minimalistic sensing and computational requirements. The approach is based on a graph representation of robot poses, using a relaxation algorithm to obtain a globally consistent map. Each link corresponds to a relative measurement of the spatial relation between the two nodes it connects. The links describe the likelihood distribution of the relative pose as a Gaussian distribution. To estimate the covariance matrix for links obtained from an omni-directional vision sensor, a novel method is introduced based on the relative similarity of neighbouring images. This new method does not require determining distances to image features using multiple view geometry, for example. Combined indoor and outdoor experiments demonstrate that the approach can handle qualitatively different environments (without modification of the parameters), that it can cope with violations of the “flat floor assumption” to some degree, and that it scales well with increasing size of the environment, producing topologically correct and geometrically accurate maps at low computational cost. Further experiments demonstrate that the approach is also suitable for combining multiple overlapping maps, e.g. for solving the multi-robot SLAM problem with unknown initial poses