Multisensor integration using neuron computing for land-vehicle navigation Kai-Wei Chiang Æ Aboelmagd Noureldin Æ Naser El-Sheimy Abstract Most of the present navigation sensor integration techniques are based on Kalman-filtering estimation procedures. Although Kalman filtering represents one of the best solutions for multisensor integration, it still has some drawbacks in terms of stability, computation load, immunity to noise ef- fects and observability. Furthermore, Kalman filters perform adequately only under certain predefined dynamic models. Neuron computing, a technology of artificial neural network (ANN), is a powerful tool for solving nonlinear problems that involve mapping input data to output data without having any prior knowledge about the mathematical process involved. This article suggests a multisensor integration approach for fusing data from an inertial navigation system (INS) and differential global positioning system (DGPS) hardware utilizing multilayer feed- forward neural networks with a back propagation learning algorithm. In addition, it addresses the impact of neural network (NN) parameters and random noise on positioning accuracy. Introduction Today, most vehicle navigation systems mainly rely on global positioning system (GPS) receivers as the primary source of information to provide the position of the ve- hicle (Shin and El-Sheimy 2002). GPS is a satellite-based all-weather radio navigation system, developed by the United States Department of Defense (DoD), which became fully operational in 1994. The system can provide precise positioning information to an unlimited number of users anywhere on the planet. Since its advent, the number of applications using GPS has increased dramatically, in- cluding tracking people, a fleets of trucks, trains, ships or planes and how fast they are moving; directing emergency vehicles to the scene of an accident; mapping where a city’s assets are located; and providing precise timing for en- deavors that require large-scale coordination. However, GPS can provide this type of information only when there is direct line of sight to four or more satellites. In other words, the system does not work well in urban areas due to signal blockage and attenuation, which may deteriorate the positioning accuracy. For the moment, any sophisticated urban application, which essentially demands continuous position determination, cannot depend on GPS as a stand- alone system. More recently, and accepting that these techniques must inevitably cost more than GPS as a stand- alone system, the concept of combining complimentary navigation systems, such as a dead reckoning (DR), iner- tial navigation system (INS), or a navigation aid, such as digital map database, have been integrated in commercial applications. Certainly it has always been a maxim in safety-related applications that it is imprudent to depend on a single navigation technique. The complimentary navigation systems should have error mechanisms that are disjoint. The resultant system design is then driven by a trade-off between cost and performance (El-Sheimy and Naser 2000). This article focuses on the implementation of a multisensor integration based on utilizing multilayer feed-forward neural networks with a back propagation learning algorithm. The basis of multisensor integration is to fuse all available data from various sensors in order to obtain an optimal navigation solution (Ashkenazi et al. 1995). Traditionally, a Kalman filter is used to combine data from various sensors, which may contain different sources of errors. Figure 1 shows a simplified scheme of the Kalman filter process. Before the estimation process starts, values for the initial error state ^ x þ 0 and the corresponding error covari- ance P þ 0 are assumed. Consequently, the filter projects the state and error covariance ahead to estimate ^ x  k andP  k , this is called the prediction mode. If new measurements at time epoch k are available, the filter starts the updating mode by computing the Kalman gainK k and updating the error state and error covariance to estimate ^ x þ k and P þ k . The Kalman filter incorporates all of this information together to provide an optimal estimate of the error states at time k (Brown and Hwang 1992). Received: 17 May 2002 / Accepted: 27 July 2002 Published online: 5 November 2002 ª Springer-Verlag 2002 K.-W. Chiang (&) Æ A. Noureldin Æ N. El-Sheimy Department of Geomatics Engineering, The University of Calgary, 2500 University Dr. NW, Calgary, Alberta T2N 1N4, Canada E-mail: kwchiang@ucalgary.ca Tel.: +1-403-2208794 or +1-403-2106263 Original article DOI 10.1007/s10291-002-0024-4 GPS Solutions (2003) 6:209–218 209