GPS World, February 2019

MAPPING FEBRUARY 2019 WWW GPSWORLD COM GPS WORLD 47 is growing and so is the volume of the streamed data A recent advance in cloud based data stream processing a data flow model treats an input data stream as something that will never become complete A derivative of that model is Flink an open source framework capable of both unbounded data stream and bounded data batch processing while treating bounded data as a special case of the streaming applications We use Flink as a core library for the environment mapping architecture as it fits the needs of eventtime processing while being a highly scalable solution The processing enables calculating necessary statistics based on a moment of time a reading occurred rather than based on a moment of time the reading arrived at the cluster The proposed system architecture is presented in FIGURE 1 The connected vehicle mapping fleet transmits packets of the GNSS receiver readings via cellular Internet connection to the server at 1 Hz Each packet contains a timestamp in the UTC time system the geographic coordinates determined by the proprietary positioning algorithms of a connected vehicle and the C N 0 measurements per each tracked satellite The geospatial processing block calculates the average C N 0 metric among the readings of a given space time cube Computed statistics are sent to Elasticsearch updating the map in real time Elasticsearch is an open source distributed search and analytics engine integrated with Kibana an open source data visualization tool User platforms request the average C N 0 metric from the search engine with their UTC timestamp and coordinates and apply it in the processing filter PILOT PROJECT The system is currently in prototype Collection of the data populating the map was performed with two positioning boards designed by Parkofon Inc and installed on the dashboard of a vehicle FIGURE 2 Lack of a high number of vehicles for the data collection campaign was compensated with an extensive piloting time 17 hours 43 minutes in a limited area driving the same roads repeatedly Two areas of New York City were the subject of extensive mapping Tests concentrated on two sectors with different GNSS signal strengths sector A a relatively open sky area and sector B exhibiting deep urban canyon conditions The mapped average C N 0 is denoted as The of the less obstructed sector A 393 dB Hz while that of the more obstructed sector B is lower 181 dB Hz This tendency is repeatable throughout the surveyed area and allows for further GNSS signal strength map integration into the algorithms at the user side MAP AIDED WEIGHTING FUNCTION It is a challenge to find an optimal set of urban navigation filter parameters as the signal obstruction environment changes significantly with the moving positioning platform Our approach adjusts parameters of the GNSS observation weighting function with respect to the retrieved from the map The algorithm scheme appears in FIGURE 3 When the first position fix is obtained the algorithm sends a request to the server with the timestamp and the coordinates determined at the previous epoch If one is available in the current user area the server response includes the metric retrieved from the GNSS signal strength map Next the GNSS observation weighting function is adjusted according to equations given in the full technical paper see Acknowledgment section FIGURE 1 Continuous GNSS signal strength environment mapping architecture FIGURE 2 Mapping setup Parkofon board is installed on the dashboard of a vehicle FIGURE 3 Map aided automatic weighting adjustment algorithm All figures supplied by the authors