Fuzzy Logic Based-Map Matching Algorithm for Vehicle Navigation System in Urban Canyons

With the rapid progress in the development of wireless technology, Global Positioning System (GPS) based vehicle navigation systems are being widely deployed in automobiles to serve the location-based needs of users and for efficient traffic management. An essential process in vehicle navigation is to map match the position obtained from GPS (or/and other sensors) on a road network map. This process of map matching in turn helps in mitigating errors from navigation solution. GPS based vehicle navigation systems have difficulties in tracking vehicles in urban canyons due to poor satellite availability. High Sensitivity GPS (HS GPS) receivers can alleviate this problem by acquiring and tracking weak signals (and increasing the availability), but at the cost of high measurement noise and errors due to multipath and cross correlation. Position and velocity results in such conditions are typically biased and have unknown distributions. Thus filtering and other statistical methods are difficult to implement. Soft computing has replaced classical computing on many fronts where uncertainties are difficult to model. Fuzzy logic, based on fuzzy reasoning concepts, is one of the most widely used soft computational methods. In many circumstances, it can take noisy, imprecise input, to yield crisp (i.e. numerically accurate) output. Fuzzy logic can be applied effectively to map match the output from a HS GPS receiver in urban canyons because of its inherent tolerance to imprecise inputs. This paper describes a map matching algorithm based on fuzzy logic. The input of the system comes from a SiRF HS XTrac GPS receiver and a low cost gyro (Murata ENV-05G). The results show an improvement in tracking the vehicle in urban canyon conditions.

[1]  H. Zimmermann,et al.  Fuzzy Set Theory and Its Applications , 1993 .

[2]  Craig A. Scott.  Improved GPS Positioning for Motor Vehicles Through Map Matching , 1994 .

[3]  Benjamin Peterson,et al.  Measuring GPS Signals Indoors , 1997 .

[4]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[5]  Laurence W. Carstensen,et al.  GPS and GIS: Enhanced Accuracy in Map Matching through Effective Filtering of Autonomous GPS Points , 1998 .

[6]  Jong-Hwan Kim,et al.  Q-factor map matching method using adaptive fuzzy network , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[7]  Gérard Lachapelle,et al.  Low Cost INS/GPS Integration: Concepts and Testing , 2000 .

[8]  John L. Shewfelt,et al.  Enhanced Sensitivity for Acquisition in Weak Signal Environments Through the Use of Extended Dwell Times , 2001 .

[9]  Gérard Lachapelle,et al.  Vehicular Navigation in Urban Canyons Using a High Sensitivity GPS Receiver Augmented with a Low Cost Rate Gyro , 2002 .

[10]  Glenn D. MacGougan,et al.  High sensitivity GPS performance analysis in degraded signal environments , 2003 .

[11]  G. Lachapelle,et al.  Accuracy and Reliability Improvement of Standalone High Sensitivity GPS Using Map Matching Techniques , 2003 .

[12]  Gérard Lachapelle,et al.  VEHICULAR NAVIGATION IN URBAN CANYONS USING A HIGH SENSITIVITY GPS RECEIVER AUGMENTED WITH A MEDIUM-GRADE IMU , 2003 .

[13]  W. Ochieng,et al.  An Extended Kalman Filter Algorithm for Integrating GPS and Low Cost Dead Reckoning System Data for Vehicle Performance and Emissions Monitoring , 2003 .

[14]  Andreas Wieser.  Fuzzy Logic and GPS Benefitting from Uncertainty , 2003 .