This paper presents an embedded implementation approach of land vehicle navigation involving a Multi Sensor System (MSS) consisting of a single-axis gyroscope and an odometer integrated with GPS receiver. With the assumption that the vehicle stays mostly in the horizontal plane, the vehicle speed obtained from the odometer measurements is decomposed into east and north velocities by using heading information from the gyroscope. Subsequently, the vehicle's position in latitude and longitude are determined. MSS errors are estimated by an integrated MSS/GPS Kalman filter (KF) which relies on a dynamic error model of position, velocity and heading as well as stochastic models for gyroscope and odometer errors. In case of a GPS outage, the designed KF module provides positioning information. The decentralized KF algorithm is described in software and is executed on an embedded soft core processor containing a single precision floating point unit. Results were validated imposing numerous simulated GPS outages of varied lengths on road test trajectory data of GPS receiver, car chip odometer and single axis MEMS based gyro. The length of the simulated GPS outages varied from 36 s to 425 s on three different road trajectories. Results show a maximum positional error of 110 m for an outage of 120s duration and a minimum positional error of 14 m for an outage of 60 s duration with respect to the reference trajectory.