%0 Journal Article
%T Augmented Kalman Filter and Map Matching for 3D RISS/GPS Integration for Land Vehicles
%A Matthew Cossaboom
%A Jacques Georgy
%A Tashfeen Karamat
%A Aboelmagd Noureldin
%J International Journal of Navigation and Observation
%D 2012
%I Hindawi Publishing Corporation
%R 10.1155/2012/576807
%X Owing to their complimentary characteristics, global positioning system (GPS) and inertial navigation system (INS) are integrated, traditionally through Kalman filter (KF), to obtain improved navigational solution. To reduce the overall cost of the system, microelectromechanical system- (MEMS-) based INS is utilized. One of the approaches is to reduce the number of low-cost inertial sensors, decreasing their error contribution which leads to a reduced inertial sensor system (RISS). This paper uses KF to integrate GPS and 3D RISS in a loosely coupled fashion to enhance navigational solution while further improvement is achieved by augmenting it with map matching (MM). The 3D RISS consists of only one gyroscope and two accelerometers along with the vehicle¡¯s built-in odometer. MM limits the error growth during GPS outages by restricting the predicted positions to the road networks. The performance of proposed method is compared with KF-only 3D RISS/GPS integration to demonstrate the efficacy of the proposed technique. 1. Introduction Low-cost navigation applications are highly dependent on satellite navigation systems, primarily global positioning system (GPS). It is composed of a constellation of 24 (with room to spare for some additional) satellites covering the globe in a manner that ensures continuous worldwide coverage. To obtain accurate positioning data, one must be in direct line of sight with at least four satellites. The main advantage of the GPS is that it can determine one¡¯s location, accurate to within a range of 30£¿m when using a single point positioning technique, and to a few centimeters when using a differential GPS technique [1¨C4]. However, the satellite signal can be blocked in GPS-denied environments such as urban canyons and tunnels. This is a major problem because there will be an interruption in the real-time positioning information. To overcome this navigational data gap, GPS is usually integrated with an inertial navigation system (INS) because it does not rely on any external sources [1¨C3]. The INS is a self-contained system consisting of three accelerometers and three gyroscopes which is mounted on the moving platform to monitor linear accelerations and angular velocities. Given the initial values of navigation parameters, the measurements from INS can be processed to determine current position, velocity, and attitude of the moving platform with respect to a certain frame of reference [4, 5]. Since higher-end INS are very expensive therefore not suitable for low-cost applications, contemporary research is focused on
%U http://www.hindawi.com/journals/ijno/2012/576807/