Foot mounted inertial navigation is an effective method for obtaining high quality pedestrian navigation solutions from MEMS sensors. Zero-Velocity information from stationary periods in the step-cycle can be used to regularly correct position drift and update estimates of the inertial sensor biases, hence dramatically improving the navigation solution. However the causes of heading error remain poorly observable and so foot mounted inertial navigation suffers from considerable drift over time. To address this problem the authors previously developed Cardinal Heading Aided Inertial Navigation (CHAIN). CHAIN makes use of the fact that when in a building, obstacles such as corridors and furniture constrain pedestrians to move in one of four directions parallel to the outside walls of the building. This knowledge is then appropriately weighted and used in an Extended Kalman Filter to improve error estimation. Although the CHAIN method is very effective at improving the quality of the heading estimates, position errors still accumulate with time, and threshold tests are required to cope with periods of motion away from the cardinal headings. In this work we investigate the use of a building floor plan to further aid navigation. This is achieved using a particle filter approach whereby particles which cross walls are removed and those which navigate in open spaces are allowed to continue. Previously the particle filter approach has been computationally intensive process requiring many particles to effectively model the navigation errors. In our work we recognise that heading is the primary source of navigation error and therefore incorporate heuristic heading information into the particle filter design. By weighting particles according to their heading we reduce the number of particles required to maintain a small failure rate and improve system performance in more open areas where there are few mapped walls to aid navigation. This paper will describe the design of our particle filter and the heuristic heading approach. Results from a number of representative test walks using a MEMS IMU will be used to demonstrate the system performance. The use of CHAIN is shown to be capable of significantly reducing the filter failure rate from 44% to 14% when a small number of particles is used in the filter (250) and the initial position is poorly known.

Clinical informatics, decision support for health professionals, electronic health records, and ehealth infrastructures.

Indoor localisation has always been a challenging problem due to poor Global Navigation Satellite System (GNSS) availability in such environments. While inertial measurement sensors have become popular solutions for indoor positioning, they suffer large drifts after initialisation. Collaborative positioning enhances positioning robustness by integrating multiple localisation information, especially relative ranging measurements between local users and transmitters. However, not all ranging measurements are useful throughout the whole positioning process and integrating too much data will increase the computation cost. To enable a more reliable positioning system, an adaptive collaborative positioning algorithm is proposed which selects units for the collaborative network and integrates ranging measurement to constrain inertial measurement errors. The algorithm selects the network adaptively from three perspectives: the network geometry, the network size and the accuracy level of the ranging measurements between the units. The collaborative relative constraint is then defined according to the selected network geometry and anticipated measurement quality. In the case of trials with real data, the positioning accuracy is improved by 60% by adjusting the range constraint adaptively according to the selected network situation, while also improving the system robustness.

The dominant navigation system for low-cost, mass-market Unmanned Aerial Vehicles (UAVs) is based on an Inertial Navigation System (INS) coupled with a Global Navigation Satellite System (GNSS). However, problems tend to arise during periods of GNSS outage where the navigation solution degrades rapidly. Therefore, this paper details a model-based integration approach for fixed wing UAVs, using the Vehicle Dynamics Model (VDM) as the main process model aided by low-cost Micro-Electro-Mechanical Systems (MEMS) inertial sensors and GNSS measurements with moment of inertia calibration using an Unscented Kalman Filter (UKF). Results show that the position error does not exceed 14.5 m in all directions after 140 s of GNSS outage. Roll and pitch errors are bounded to 0.06 degrees and the error in yaw grows slowly to 0.65 degrees after 140 s of GNSS outage. The filter is able to estimate model parameters and even the moment of inertia terms even with significant coupling between them. Pitch and yaw moment coefficient terms present significant cross coupling while roll moment terms seem to be decorrelated from all of the other terms, whilst more dynamic manoeuvres could help to improve the overall observability of the parameters.

Vehicle-to-grid services draw power or curtail demand from electric vehicles when they are connected to a compatible charging station. In this paper, we investigated automated machine learning for predicting when vehicles are likely to make such a connection. Using historical data collected from a vehicle tracking service, we assessed the technique’s ability to learn and predict when a fleet of 48 vehicles was parked close to charging stations and compared this with two moving average techniques. We found the ability of all three approaches to predict when individual vehicles could potentially connect to charging stations to be comparable, resulting in the same set of 30 vehicles identified as good candidates to participate in a vehicle-to-grid service. We concluded that this was due to the relatively small feature set and that machine learning techniques were likely to outperform averaging techniques for more complex feature sets. We also explored the ability of the approaches to predict total vehicle availability and found that automated machine learning achieved the best performance with an accuracy of 91.4%. Such technology would be of value to vehicle-to-grid aggregation services.