A test-bed simulator for gps and gis integrated navigation and positioning research: - bus positioning, using gps observations, odometer readings and map matching

A test-bed application, called Map Matched GPS (MMGPS) processes raw GPS output data, from RINEX files, or GPS derived coordi nates. This developed method uses absolute GPS positioning, map matched, to locate the vehicle on a road centre-line, when GPS is known to be sufficiently accurate. MMGPS software has now been adapted to incorporate positioning based on odometer derived dist ances (OMMGPS), when GPS positions are not available. Relative GPS positions are used to calibrate the odometer observations. In OMMGPS, GPS pseudorange observations are combined with DTM height information and odometer positions to provide a vehicle position at one second epochs. Generally, odometer positioning is used much more often, to position the vehicle, than GPS. Typically the ratio is 7:3 odometer positions to GPS pos itions. In total, over 15,000 vehicle positions were computed using OMMGPS. The described experiment used GPS observations taken on a bus on a predefined route, hence the correct road is always known. Ther efore, map matching techniques are used to improve the GPS positioning accuracy, and to identify grossly inaccurate GPS positions. Calibrated odometer corrections are made using odometer count at the current epoch and relative GPS distance travelled. If a GPS position is detected to be inaccurate, it is not used for positioning the bus, or for calibrating the odometer correction factor. In general the position quality provided by GPS alone was extremely poor, due to multipath effects caused by the urban canyons of central London. In the case of one particular trip, OMMGPS provides a mean error of position of 8.8 metres compared with 53.7 metres for raw GPS alone

A Map-matching Algorithm to Improve Vehicle Tracking Systems Accuracy

The satellite-based vehicle tracking systems accuracy can be improved by augmenting the positional information using road network data, in a process known as map-niatcliing. Map-matching algorithms attempt to estimate vehicle route and location in it particular road map (or any restricting track such as rails, etc), in spite of the digital map errors and GPS inaccuracies. Point-to-curve map-matching is not fully suitable to the problems since it ignores any historical data and often gives inaccurate, unstable, jumping results. The better curve-to-curve matching approach consider the road connectivity and measure the curve similarity between the track and the possible road path (hypotheses), but mostly does not have any way to manage multiple route hypotheses which have varying degree of similarity over time. The thesis presents a new distance metric for curve-to-curve mapmatching technique, integrated with a framework algorithm which is able to maintain many possible route hypotheses and pick the most likely hypothesis at a time, enabling future corrections if necessary, therefore providing intelligent guesses with considerable accuracy. A simulator is developed as a test bed for the proposed algorithm for various scenarios, including the field experiment using Garmin e-Trex GPS Receiver. The results showed that the proposed algoritlimi is able to improve the neap-matching accuracy as compared to the point-to-curve algorithm. Keywords: map-matching, vehicle tracking systems, Multiple Hypotheses Technique, Global Positioning System

A Map-matching Algorithm to Improve Vehicle Tracking Systems Accuracy

The satellite-based vehicle tracking systems accuracy can be improved by augmenting the positional information using road network data, in a process known as map-matching. Map-matching algorithms attempt to estimate vehicle route and location in a particular road map (or any restricting track such as rails, etc), in spite of the digital map errors and GPS inaccuracies. Point-to-curve map-matching is not fully suitable to the problem since it ignores any historical data and often gives inaccurate, unstable, jumping results. The better curve-to-curve matching approach consider the road connectivity and measure the curve similarity between the track and the possible road path (hypotheses), but mostly does not have any way to manage multiple route hypotheses which have varying degree of similarity over time. The thesis presents a new distance metric for curve-to-curve mapmatching technique, integrated with a framework algorithm which is able to maintain many possible route hypotheses and pick the most likely hypothesis at a time, enabling future corrections if necessary, therefore providing intelligent guesses with considerable accuracy. A simulator is developed as a test bed for the proposed algorithm for various scenarios, including the field experiment using Garmin e-Trex GPS Receiver. The results showed that the proposed algorithm is able to improve the map-matching accuracy as compared to the point-to-curve algorithm. Keywords: map-matching, vehicle tracking systems, Multiple Hypotheses Technique, Global Positioning System

A Map-matching Algorithm to Improve Vehicle Tracking Systems Accuracy

The satellite-based vehicle tracking systems accuracy can be improved by augmenting the positional information using road network data, in a process known as map-niatcliing. Map-matching algorithms attempt to estimate vehicle route and location in it particular road map (or any restricting track such as rails, etc), in spite of the digital map errors and GPS inaccuracies. Point-to-curve map-matching is not fully suitable to the problems since it ignores any historical data and often gives inaccurate, unstable, jumping results. The better curve-to-curve matching approach consider the road connectivity and measure the curve similarity between the track and the possible road path (hypotheses), but mostly does not have any way to manage multiple route hypotheses which have varying degree of similarity over time. The thesis presents a new distance metric for curve-to-curve mapmatching technique, integrated with a framework algorithm which is able to maintain many possible route hypotheses and pick the most likely hypothesis at a time, enabling future corrections if necessary, therefore providing intelligent guesses with considerable accuracy. A simulator is developed as a test bed for the proposed algorithm for various scenarios, including the field experiment using Garmin e-Trex GPS Receiver. The results showed that the proposed algoritlimi is able to improve the neap-matching accuracy as compared to the point-to-curve algorithm. Keywords: map-matching, vehicle tracking systems, Multiple Hypotheses Technique, Global Positioning System