An Improved Particle Filter Map Matching Algorithm for Personal Inertial Positioning

The current particle filtering map matching algorithm has problems such as low map utilization and poor accuracy of turnoff positioning, etc. This paper proposed an improved particle filtering-based map-matching algorithm for the inertial positioning of personnel. The historical moment position constraint and feasible region constraint of particles were introduced in this paper. A resampling method based on multi-stage backtracking of particles was proposed. Therefore, the effectiveness of newly generated particles could be guaranteed. The utilization rate of map information could be improved, thus enhancing the accuracy of personnel localization. The walking experiment results showed that, compared with the traditional PDR algorithm, the proposed method had higher localization accuracy and better repeatability of the localization trajectory for multi-turn paths. Under the total travel of 480 meters, the deviation of the starting end point was less than 2 meters, which was about 0.4% of the total travel.

GPS probe map matching algorithm based on spatial data model

To improve the performance of the traditional map matching algorithms in freeway traffic state monitoring systems using the low logging frequency GPS （global positioning system） probe data, a map matching algorithm based on the Oracle spatial data model is proposed. The algorithm uses the Oracle road network data model to analyze the spatial relationships between massive GPS positioning points and freeway networks, builds an N-shortest path algorithm to find reasonable candidate routes between GPS positioning points efficiently, and uses the fuzzy logic inference system to determine the final matched traveling route. According to the implementation with field data from Los Angeles, the computation speed of the algorithm is about 135 GPS positioning points per second and the accuracy is 98.9%. The results demonstrate the effectiveness and accuracy of the proposed algorithm for mapping massive GPS positioning data onto freeway networks with complex geometric characteristics.

A novel vehicle navigation map matching algorithm based on fuzzy logic and its application

A new real-time map matching algorithm based on fuzzy logic is proposed. 3 main factors affecting the reliability of map matching, including the distance between the vehicle location and the matching road segment, the angle between the vehicle direction and the road segment direction and the road connectivity are discussed. Fuzzy rules for the distance, angle and connectivity are presented to calculate the matching reliability. 2 indicators for estimating the matching reliability are then derived, one is the lower limit of the reliability, and the other is the limit error of the difference between the maximal value and the second-maximal value of the reliability. A real-time map-matching system based on fuzzy logic is therefore developed. Using the real data of global positioning system(GIS) based navigation and geographic information system(GPS) based road map, the method is verified and the (results) prove the effectiveness of the proposed method.

Low-complexity online correction and calibration of pedestrian dead reckoning using map matching and GPS

Dead Reckoning is a relative positioning scheme that is used to infer the change of position relative to a point of origin by measuring the traveled distance and orientation change.Pedestrian Dead Reckoning(PDR)applies this concept to walking persons.The method can be used to track someone's movement in a building after a known landmark like the building's entrance is registered.Here,the movement of a foot and the corresponding direction change is measured and summed up,to infer the current position.Measuring and integrating the corresponding physical parameters,e.g.using inertial sensors,introduces small errors that accumulate quickly into large distance errors.Knowledge of a buildings geography may reduce these errors as it can be used to keep the estimated position from moving through walls and onto likely paths.In this paper,we use building maps to improve localization based on a single foot-mounted inertial sensor.We describe our localization method using zero velocity updates to accurately compute the length of individual steps and a Madgwick filter to determine the step orientation.Even though the computation of individual steps is quite accurate,small errors still accumulate in the long term.We show how correction algorithms using likely and unlikely paths can rectify errors intrinsic to pedestrian dead reckoning tasks,such as orientation and displacement drift,and discuss restrictions and disadvantages of these algorithms.We also present a method of deriving the initial position and orientation from GPS measurements.We verify our PDR correction methods analyzing the corrected and raw trajectories of six participants walking four routes of varying length and complexity through an office building,walking each route three times.Our quantitative results show an endpoint accuracy improvement of up to 60%when using likely paths and 23%when using unlikely paths.However,both approaches can also decrease accuracy in certain scenarios.We identify those scenarios and offer further ideas for improving Pedestrian Dead Reckoning methods.

SEVERAL PROBLEMS OF DIGITAL MAP TECHNIQUE IN LOW ALTITUDE PENETRATION

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On palaeogeographic map

The palaeogeographic map is a graphic representation of physical geographical character- istics in geological history periods and human history periods. It is the most important result of palaeogeographic study. The author, as the Editor-in-Chief oflournal of Palaeogeography, Chinese Edition and English Edition, aimed at the problems of the articles submitted to and published in the]ournal of Palaeogeography in recent years and the relevant papers and books of others, and integrated with his practice of palaeogeographic study and mapping, wrote this paper. The content mainly includes the data of palaeogeographic mapping, the prob- lems of palaeogeographic mapping method, the ＂Single factor analysis and multifactor comprehensive mapping method ---- Methodology of quantitative lithofacies palae- ogeography＇, i.e., the ＂4 steps mapping method＂, the nomenclature of each palaeogeographic unit in palaeogeographic map, the explanation of each palaeogeographic unit in palae- ogeographic map, the explanation of significance of palaeogeographic map and palae- ogeographic article, the evaluative standards of palaeogeographic map and palaeogeographic article, and the self-evaluation. Criticisms and corrections are welcome. 2016 China University of Petroleum {Beijing）. Production and hosting by Elsevier B.V. on behalf of China University of Petroleum （Beijing）. This is an open access article under

Traffic services for vehicles: the process from receiving raw probe data to space-time diagrams and the resulting traffic service

Today, large quantities of vehicle data(FCD:floating car data) are widely used by traffic service providers to create and broadcast traffic states in road networks. As a first processing step, all raw position data received from Global Positioning Systems(GPS) have to be map matched in a digital road map. The technical aspects of such a matching process for GPS data are described in this report. After the matching process, spacetime-diagrams are created of the probe data showing traffic situation details over space and time. Various examples illustrate how traffic service quality depends on the number of matched GPS raw data; it will be stated that when 2% of connected vehicles in the total traffic flow are sending their GPS data in shorter time intervals, a high quality and precise reconstruction of the current traffic phases is achieved. Traffic reconstruction is followed by a translation into traffic information messages, which can be sent and used in vehicle navigation systems for driver information and dynamic route guidance.