Kalman filters comparison for vehicle localization data alignment

The aim of this paper is to carry out a comparison between several algorithms of the Kalman filters family for nonlinear systems. Alter having presented the most popular of them and showed its limitations, we introduce some new Kalman filters and compare them for the vehicle localization problem. This comparison is based on the predictive step what corresponds to the worst case that it can occur in vehicle localization. Typically, when we achieve a vehicle tracking, if the tracked vehicle is hidden, corrective data are unavailable and therefore the corrective step is disable (time data alignment)

[1]  Nadine Le Fort-Piat,et al.  Safe Task Planning Integrating Uncertainties and Local Maps Federations , 2000, Int. J. Robotics Res..

[2]  Sebastien Glaser,et al.  LATERAL DRIVING ASSISTANCE USING ROBUST CONTROL AND EMBEDDED DRIVER-VEHICLE-ROAD MODEL , 2004 .

[3]  Stergios I. Roumeliotis,et al.  Extended Kalman filter for frequent local and infrequent global sensor data fusion , 1997, Other Conferences.

[4]  James L. Crowley,et al.  World modeling and position estimation for a mobile robot using ultrasonic ranging , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[5]  Peter Cheeseman,et al.  On the Representation and Estimation of Spatial Uncertainty , 1986 .

[6]  M. Nørgaard,et al.  Advances in Derivative-Free State Estimation for Nonlinear Systems , 1998 .

[7]  S. Julier,et al.  A General Method for Approximating Nonlinear Transformations of Probability Distributions , 1996 .

[8]  Nando de Freitas,et al.  The Unscented Particle Filter , 2000, NIPS.

[9]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[10]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[11]  Hans B. Pacejka,et al.  A New Tire Model with an Application in Vehicle Dynamics Studies , 1989 .

[12]  Greg Welch,et al.  Welch & Bishop , An Introduction to the Kalman Filter 2 1 The Discrete Kalman Filter In 1960 , 1994 .

[13]  K. Ito.  Gaussian filter for nonlinear filtering problems , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[14]  Kazufumi Ito,et al.  Gaussian filters for nonlinear filtering problems , 2000, IEEE Trans. Autom. Control..

[15]  F. Gustafsson,et al.  Model-based statistical tracking and decision making for collision avoidance application , 2004, Proceedings of the 2004 American Control Conference.