this paper presents an accurate differential global positioning system (dgps) using multi-layered neural networks (nns) based on the back propagation (bp) and imperialistic competition algorithm (ica) in order to predict the dgps corrections for accurate positioning. simulation results allowed us to optimize the nn performance in term of residual mean square error. we compare results obtained b...

Differential Global Positioning System (DGPS) provides differential corrections for a GPS receiver in order to improve the navigation solution accuracy. DGPS position signals are accurate, but very slow updates. Improving DGPS corrections prediction accuracy has received considerable attention in past decades. In this research work, the Neural Network (NN) based on the Gaussian Radial Basis Fun...

This paper focuses on applying a neural network model to predict pseudorange corrections (PRC) for differential Global Positioning System (DGPS). The class of nonlinear autoregressive recurrent neural networks is chosen as the basic architecture. The neural networks are trained by an unscented Kalman filter due to its powerful capabilities for online parameters estimation. The paper first brief...

In this paper we propose an efficient authentication and integrity scheme to support DGPS corrections using the RTCM protocol, such that the identified vulnerabilities in DGPS are mitigated. The proposed scheme is based on the TESLA broadcast protocol with modifications that make it suitable for the bandwidth and processor constrained environment of marine DGPS.

The global positioning system (GPS) allows properly equipped users to determine their position based on the measured pseudoranges to at least four satellites. Differential GPS operation (DGPS) uses a reference station at a known location to calculate and broadcast pseudorange corrections to local users, resulting in improved user position accuracy. DGPS accuracy is limited by the ability of the...

Position information obtained from standard GPS receivers is known to be corrupted with noise. To make effective use of GPS information in a navigation system it is essential to model this noise and to eliminate its effect. This paper present Parallel Structure Wavelet Based Neural Network (PSWNN) for predicting the Differential GPS (DGPS) corrections. The PSWNN consists of multiple numbers of ...

Back-Propagation (BP), Extended Kalman Filter (EKF) and Particle Swarm Optimization (PSO) are three of the most widely used algorithms for training feed forward Neural Networks (NNs). This paper presents an accurate DGPS land vehicle navigation system using multi-layered NNs based on the BP, EKF and PSO learning algorithms. The network setup is developed based on mathematical models to avoid ex...

Neural Networks (NNs) are capable of learning high complex, nonlinear input-output mappings. This characteristic of NNs enables them to be used in nonlinear system modeling and prediction applications. On the other hand, the wavelet decomposition provides a powerful tool for functional approximation. In this paper, a kind of Wavelet Neural Networks (WNNs) is proposed for Differential GPS (DGPS)...

Differential Global Positioning System (DGPS) provides differential corrections for a GPS receiver in order to improve the navigation solution accuracy. DGPS position signals are accurate, but very slow updates. Improving DGPS corrections prediction accuracy has received considerable attention in past decades. In this research work, the Neural Network (NN) based on the Gaussian Radial Basis Fun...

As part of a project for the Defense Advanced Research Projects Agency, Sandia National Laboratones’ Intelligent Systems & Robotics Center is developing and testing the feasibility of using a cooperative team of robotic sentry vehicles to guard a perimeter, perform a surround task, and travel extended distances. This paper describes our most recent activities. In particular, this paper highligh...