Online INS/GPS integration with a radial basis function neural network

Sharaf, R.; Noureldin, Aboelmagd; Osman, Ahmed; El‐Sheimy, Naser

doi:10.1109/maes.2005.1412121

Cited by 122 publications

(49 citation statements)

References 5 publications

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“…The work reported in Sharaf et al (2005) is similar to that reported in Bouvet & Garcia (2000) in that preprocessing operations are performed on the measurements before they are fused. An assumption that is made in this method is that the DGPS data is always either available or unavailable due to an outage in satellite signal.…”

Section: Other Motion Sensors and Dgps Fusionmentioning

confidence: 79%

“…In Sharaf et al (2005) an Artificial Neural Network (ANN) is chosen as a tool for detecting errors and noises in INS measurements using a DGPS as a guide to the true location of the vehicle during a training phase. The work reported in Sharaf et al (2005) is similar to that reported in Bouvet & Garcia (2000) in that preprocessing operations are performed on the measurements before they are fused.…”

Section: Other Motion Sensors and Dgps Fusionmentioning

confidence: 99%

See 1 more Smart Citation

Emerging New Trends in Hybrid Vehicle Localization Systems

Drawil¹,

Basir²

2012

Global Navigation Satellite Systems: Signal, Theory and Applications

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Section: Other Motion Sensors and Dgps Fusionmentioning

confidence: 79%

Section: Other Motion Sensors and Dgps Fusionmentioning

confidence: 99%

Emerging New Trends in Hybrid Vehicle Localization Systems

Drawil¹,

Basir²

2012

Global Navigation Satellite Systems: Signal, Theory and Applications

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“…GPS and INS data integration has been performed using Radial Basis Function Neural Network, Back Propagation Neural Network and Fuzzy system. Radial Basis Function Neural Network (RBF-NN) generally has simpler architecture and faster training procedure than multi-layer perceptron neural networks ( [1], [2], [6], [8], [10]). Though it has simple architecture and faster training procedure, it only has fixed topology, so it lacks dynamicity.…”

Section: Existing Ins/gps Data Fusion Techniquesmentioning

confidence: 99%

Analysis of Different Associative Memory Neural Network for GPS/INS Data Fusion

Deborah

2012

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. Aircraft navigation relies mainly on Global Positioning System (GPS) to provide accurate position values consistently. However, GPS receivers may encounter frequent GPS outages within urban areas where satellite signals are blocked. To overcome this drawback generally GPS is integrated with inertial sensors mounted inside the vehicle to provide a reliable navigation solution. Inertial Navigation System (INS) and GPS are commonly integrated using a Kalman filter (KF) to provide a robust navigation solution, overcoming situations of GPS satellite signals blockage. This work presents New Position Update Architecture (NPUA) for GPS and INS data integration. The NPUA has an Artificial Neural Network (ANN) block that uses Associative memoy Neural

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“…Although information about the accuracy achieved during the navigation mission became available, the internal structure (learning base) of each of the MLP networks has to be changed until the best performance is achieved. The P-dP architecture was improved by using radial basis function (RBF) neural networks instead of MLP networks (Sharaf et al, 2005a). This step was basically considered as RBF neural networks do not require as much changes in the network architecture during the training procedure (i.e.…”

Section: Ai-based Data Fusion Techniquesmentioning

confidence: 99%