Neural Network Based Data Fusion for Vehicle Positioning in Land Navigation System

Land navigation systems need a precise and continuous position in order to function properly. The sensors commonly found in those systems are differential odometer, global positioning system and 2 or 3 axis inertial measurement unit respectively. Two or more of these complementary positioning methods must be integrated together to achieve the required performance at low cost. The integration, which implies the fusion of noisy data provided by each sensor, must be performed in some optimal manner. Most positioning system designers choose the Kalman filter as the data fusion method. An interesting alternative to the Kalman filter is the artificial neural network (ANN). This paper describes the research conducted to evaluate the potential of an ANN as a centralized fusion method and as nonlinear filters for land vehicle positioning. INTRODUCTION The principal function of a land navigation system in a vehicle is to guide the driver while minimizing the trip duration and/or the traveled distance. The position must be determined with a precision of 20 meters or less and at a frequency of 1 hertz or more. GPS alone is usually able to estimate the position with the required performance. However, possible occlusions of satellite signals by high buildings or heavy foliages may prevent computation of a solution by a GPS receiver for several seconds. A differential odometer measures the traveled distance and the azimuth variation of a vehicle during a sampling period. A new position can be computed with these measurements and the last known position by dead reckoning. An inertial system measures the vehicle's inertia characterized by it's acceleration and it's angular velocity. By means of integration, the traveled distance and the azimuth variation can be computed and therefore a new position can be reckoned from the last known position. The recursive nature of the positioning computation cause the positioning error to grow proportionally with time. A periodic reset is needed. The Kalman filter is an optimal linear estimator which minimizes the mean square error. Non-linearities can also be dealt with the extended Kalman filter. However, the performance analysis of the extended Kalman filter presents some difficulties due to the recurrence of the measure sequence into the states of the filter [6]. Also, implementation of the extended filter can be quite laborious depending on the number of states required to modelize the system. VIRTUAL ENVIRONMENT SIMULATION The data used for testing the algorithms presented in this paper was generated by a virtual environment simulation. A virtual environment avoid the time, expense and difficulty encountered when conducting test track experiments. Other benefits of working in a simulated environment include the availability of ground truth measurements, the repeatability of experiments, easy modification of testing conditions and rapid turnaround of system modifications[3]. VEHICLE`S DYNAMIC Kinematics of a land vehicle in three dimensions has been simulated with the road and the vehicle's acceleration as inputs. To improve the simulation realism, the road has been taken from real digital topographic information and a real vehicle with an accelerometer has traveled this road to generate the vehicle's acceleration data. Even though the acceleration data are noisy, the simulation represent more accurately a real road situation than if the acceleration data had been generated randomly. SENSORS The simulator emulates a GPS receiver, a 3-axis inertial measurement unit and a differential odometer. GPS The GPS model can be described by equation (1.1). ˆ[ ] [ ] [ ] [ ] [ ] [ ] trop iono white mult t t t t t t ρ ρ δ δ δ δ = + + + + (1.1) where ˆ[ ] t ρ is the pseudorange measured by the emulated receiver, [ ] t ρ is the real pseudorange, [ ] trop t δ is the tropospheric delay, [ ] iono t δ is the ionospheric delay, [ ] white t δ is the white noise generated by the receiver's electronic components and [ ] mult t δ is the multipath problem. The 3-axis inertial measurement unit has a single model for every gyroscope and accelerometer described by equation (1.2). 1 2 ˆ [ ] [ ] ( [ ] [ ]*sin( ) [ ]*sin( )) [0] [ ] [ ]*sin( ) [ ]*sin( ) ( [ ] [ ]*sin( ) [ ]*sin( )) [ ] i i i i j ij t k ik i bi n