Information Aided Navigation: A Review

The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system current conditions and turning it into an applicable source of information to update the navigation filter. This paper aims to provide an extensive survey of information aided navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, that would otherwise remain unobservable.Comment: 8 figures, 3 table

Data-Driven Denoising of Stationary Accelerometer Signals

Modern navigation solutions are largely dependent on the performances of the standalone inertial sensors, especially at times when no external sources are available. During these outages, the inertial navigation solution is likely to degrade over time due to instrumental noises sources, particularly when using consumer low-cost inertial sensors. Conventionally, model-based estimation algorithms are employed to reduce noise levels and enhance meaningful information, thus improving the navigation solution directly. However, guaranteeing their optimality often proves to be challenging as sensors performance differ in manufacturing quality, process noise modeling, and calibration precision. In the literature, most inertial denoising models are model-based when recently several data-driven approaches were suggested primarily for gyroscope measurements denoising. Data-driven approaches for accelerometer denoising task are more challenging due to the unknown gravity projection on the accelerometer axes. To fill this gap, we propose several learning-based approaches and compare their performances with prominent denoising algorithms, in terms of pure noise removal, followed by stationary coarse alignment procedure. Based on the benchmarking results, obtained in field experiments, we show that: (i) learning-based models perform better than traditional signal processing filtering; (ii) non-parametric kNN algorithm outperforms all state of the art deep learning models examined in this study; (iii) denoising can be fruitful for pure inertial signal reconstruction, but moreover for navigation-related tasks, as both errors are shown to be reduced up to one order of magnitude.Comment: 10 pages, 15 figures, 8 table

Parametric and State Estimation of Stationary MEMS-IMUs: A Tutorial

Inertial navigation systems (INS) are widely used in almost any operational environment, including aviation, marine, and land vehicles. Inertial measurements from accelerometers and gyroscopes allow the INS to estimate position, velocity, and orientation of its host vehicle. However, as inherent sensor measurement errors propagate into the state estimates, accuracy degrades over time. To mitigate the resulting drift in state estimates, different approaches of parametric and state estimation are proposed to compensate for undesirable errors, using frequency-domain filtering or external information fusion. Another approach uses multiple inertial sensors, a field with rapid growth potential and applications. The increased sampling of the observed phenomenon results in the improvement of several key factors such as signal accuracy, frequency resolution, noise rejection, and higher redundancy. This study offers an analysis tutorial of basic multiple inertial operation, with a new perspective on the error relationship to time, and number of sensors. To that end, a stationary and levelled sensors array is taken, and its robustness against the instrumental errors is analyzed. Subsequently, the hypothesized analytical model is compared with the experimental results, and the level of agreement between them is thoroughly discussed. Ultimately, our results showcase the vast potential of employing multiple sensors, as we observe improvements spanning from the signal level to the navigation states. This tutorial is suitable for both newcomers and people experienced with multiple inertial sensors

Accounting for center‐of‐mass target motion using convolution methods in Monte Carlo‐based dose calculations of the lung

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134807/1/mp9083.pd

A Learning-based approach for bias elimination in low cost gyroscopes

Modern sensors play a pivotal role in many operating platforms, as they manage to track the platform dynamics at a relatively low manufacturing costs. Their widespread use can be found starting from autonomous vehicles, through tactical platforms, and ending with household appliances in daily use. Upon leaving the factory, the calibrated sensor starts accumulating different error sources which slowly wear out its precision and reliability. To that end, periodic calibration is needed, to restore intrinsic parameters and realign its readings with the ground truth. While extensive analytic methods exist in the literature, little is proposed using data-driven techniques and their unprecedented approximation capabilities. In this study, we show how bias elimination in low-cost gyroscopes can be performed in considerably shorter operative time, using a unique convolutional neural network structure. The strict constraints of traditional methods are replaced by a learning-based regression which spares the time-consuming averaging time, exhibiting efficient sifting of background noise from the actual bias.Comment: 5 pages, 6 figures, conference pape

Safety and pharmacokinetics of intravenous levetiracetam infusion as add-on in status epilepticus

PURPOSE: To evaluate the feasibility and safety of intravenous (iv) levetiracetam (LEV) added to the standard therapeutic regimen in adults with status epilepticus (SE), and as secondary objective to assess a population pharmacokinetic (PK) model for ivLEV in patients with SE. METHODS: In 12 adults presenting with SE, 2,500 mg ivLEV was added as soon as possible to standardized protocol, consisting of iv clonazepam and/or rectal diazepam, as needed followed by phenytoin or valproic acid. ivLEV was administered over approximately 5 min, in general after administration of clonazepam, regardless the need for further treatment. During 24-h follow-up, patients were observed for any clinically relevant side-effects. Blood samples for PK analysis were available in 10 patients. A population PK model was developed by iterative two-stage Bayesian analysis and compared to PK data of healthy volunteers. RESULTS: Eleven patients with a median age of 60 years were included in the per protocol analysis. Five were diagnosed as generalized-convulsive SE, five as partial-convulsive SE, and one as a nonconvulsive SE. The median time from hospital admission to ivLEV was 36 min. No serious side effects could be related directly to the administration of ivLEV. During PK analysis, four patients showed a clear distribution phase, lacking in the others. The PK of the population was best described by a two-compartment population model. Mean (standard deviation, SD) population parameters included volume of distribution of central compartment: 0.45 (0.084) L/kg; total body clearance: 0.0476 (0.0147) L/h/kg; distribution rate constants, central to peripheral compartment (k(12)): 0.24 (0.12)/h, and peripheral to central (k(21)): 0.70 (0.22)/h. Mean maximal plasma concentration was 85 (19) mg/L. DISCUSSION: The addition of ivLEV to the standard regimen for controlling SE seems feasible and safe. PK data of ivLEV in patients with SE correspond to earlier values derived from healthy volunteers, confirming a two-compartment population model

One and four layer acellular bladder matrix for fascial tissue reconstruction

To determine whether the use of multiple layers of acellular bladder matrix (ABM) is more suitable for the treatment of abdominal wall hernia than a single layered ABM. The feasibility, biocompatibility and mechanical properties of both materials were assessed and compared. Biocompatibility testing was performed on 4 and 1 layered ABM. The matrices were used to repair an abdominal hernia model in 24 rabbits. The animals were followed for up to 3 months. Immediately after euthanasia, the implant site was inspected and samples were retrieved for histology, scanning electron microscopy and biomechanical studies. Both acellular biomaterials demonstrated excellent biocompatibility. At the time of retrieval, there was no evidence of infection. The matrices demonstrated biomechanical properties comparable to native tissue. Three hernias (25%) were found in the single layer ABM group and only 1 hernia (8%) was found in the 4 layer ABM group. Histologically, the matrix structure was intact and the cell density within the matrices decreased with time. The dominant cell type present within the matrices shifted from lymphocytes to fibroblasts over time. Both ABMs maintained adequate strength over time when used for hernia repair, and there was an extremely low incidence of adhesion formation. The single layer ABM showed enhanced cellular integration, while the 4 layer ABM reduced hernia formation. Either of these matrices may be useful as an off-the-shelf biomaterial for patients requiring fascial repair