Kalman Filter-Based Integration of DGPS and Vehicle Sensors for Localization

Abstract

Abstract-We present a position estimation scheme for cars based on the integration of Global Positioning System (GPS) with vehicle sensors. The aim is to achieve enough accuracy to enable in vehicle cooperative collision warning, i.e., systems that provides warnings to drivers based on information about the motions of neighboring vehicles obtained by wireless communications from those vehicles, without use of ranging sensors. The vehicle sensors consist of wheel speed sensors, steering angle encoder, and a fiber optic gyro. We fuse these in an extended Kalman filter. The process model is a dynamic bicycle model. We present data from about 60 km of driving in urban environments including stops, intersection turns, U-turns, and lane changes, at both low and high speeds. The data show the filter estimates position, speed, and heading with the accuracies required by cooperative collision warning in all except two kinds of settings. The data also shows GPS and vehicle sensor integration through a bicycle model compares favorably with position estimation by fusing GPS and inertial navigation system (INS) through a kinematic model.

An Algorithm of Mobile Sensors Data Fusion Tracking for Wireless Sensor Networks

“…The first component in (4) indicates the distance between the target and one of the mobile sensors, the second and the third term denotes the measured velocity and the angle between the target and one of the mobile sensors, respectively.…”

“…In other words, each local system performs a data association to determine the H i 's, which is the Jacobian matrix of partial derivatives of h(·) shown in (4), and then transmits its own estimate ofx i (t|t) to the central processor. In addition, an index matrix M i (t), which is used to associate the local processors with the central processor and for track-to-track correlation, is defined in our fusion algorithm.…”

“…A piece of m-file for the purpose of tutoring to calculate the measurement state related to (4) is illustrated in the appendix in which two targets tracking is assumed to simulate, …”

Positioning performance analysis using RWLS algorithm based on variance estimation methods

“…To achieve a higher accuracy, some researches have been done on differential GPS [7,19,20,28]. In some other research efforts, it has been tried to combine GPS with other navigation systems to attain a higher accuracy [29,5,31].…”

DGPS/IMU integration-based geolocation system: Airborne experimental test results

“…The objective of DGPS/IMU integration is to provide precise navigation information for the localization of ground/airborne/ underwater mobile vehicles [8,14,17,18] or pedestrian tracking [6]. However relatively few research efforts have been devoted to the geolocation of DGPS/IMU integration-based airborne image systems even though some previous works can be found as [13,19].…”