The Global Positioning System (GPS) aided low cost Dead Reckoning (DR) system can provide without interruption the vehicle position for efficient fleet management solutions in smart cities. The Extended Kalman Filter (EKF) is generally applied for data fusion using the sensor’s measures and the GPS position as a helper.

An Improved Robust Low Cost Approach for Real Time Vehicle Positioning in a Smart City

In this work an intelligent navigator developed to overcome the limitations of existing Strapdown Inertial Navigation Systems (SINS) algorithm. This system is based on Adaptive Neuro-Fuzzy Inference System (ANFIS). As in previous work, which is based on Artificial Neural Network, the window based weight updating strategy was used, and the intelligent navigator evaluated using several SINS hypothetical field tests data. And the results show that the intelligent navigator based on ANFIS more powerful compared with other (traditional and intelligent navigator based on ANN).

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Implementation of an intelligent SINS navigator based on ANFIS

Global Positioning System (GPS) and Strapdown Inertial Navigation System (SDINS) can be integrated together to provide a reliable navigation system. This paper offers a new method for error estimation in a GPS/INS augmented system based on Artificial Neural Network (ANN) and Wavelet Transform (WT). An ANN was adopted in this paper to model the GPS/INS position and velocity errors in real time to predict the error in the integrated system and provide accurate navigation information for a moving vehicle. It was found that the proposed technique reduces the standard deviation error in the position by about 91% for X, Y, and Z axes, while in velocity it was reduced by about 94% for North, East, and Down directions.

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Salam A. Ismaeel

Papers

GPS/INS System Integratin Based on Neuro-Wavelet Techniques.