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| Content Provider | Springer Nature Link |
|---|---|
| Author | Saadeddin, Kamal Abdel Hafez, Mamoun F. Jaradat, Mohammad A. Jarrah, Mohammad Amin |
| Copyright Year | 2013 |
| Abstract | Due to the inherent highly nonlinear vehicle state error dynamics obtained from low-cost inertial navigation system (INS) and Global Positioning System (GPS) along with the unknown statistical properties of these sensors, the optimality/accuracy of the classical Kalman filter for sensor fusion is not guaranteed. Therefore, in this paper, low-cost INS/GPS measurement integration is optimized based on different artificial intelligence (AI) techniques: Neural Networks (NN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) architectures. The proposed approaches are aimed at achieving high-accuracy vehicle state estimates. The architectures utilize overlapping windows for delayed input signals. Both the NN approaches and the ANFIS approaches are used once with overlapping position windows as the input and once with overlapping position and velocity windows as the input. Experimental tests are conducted to evaluate the performance of the proposed AI approaches. The achieved accuracy is presented and discussed. The study finds that using ANFIS, with both position and velocity as input, provides the best estimates of position and velocity in the navigation system. Therefore, the dynamic input delayed ANFIS approach is further analyzed at the end of the paper. The effect of the input window size on the accuracy of state estimation is also discussed. |
| Starting Page | 325 |
| Ending Page | 348 |
| Page Count | 24 |
| File Format | |
| ISSN | 09210296 |
| Journal | Journal of Intelligent & Robotic Systems |
| Volume Number | 73 |
| Issue Number | 1-4 |
| e-ISSN | 15730409 |
| Language | English |
| Publisher | Springer Netherlands |
| Publisher Date | 2013-10-23 |
| Publisher Place | Dordrecht |
| Access Restriction | Subscribed |
| Subject Keyword | Adaptive Neuro-Fuzzy Inference System (ANFIS) Neural Networks (NN) Global Positioning System (GPS) Inertial Measurement Unit (IMU) Inertial Navigation System (INS) Control, Robotics, Mechatronics Electrical Engineering Artificial Intelligence (incl. Robotics) Mechanical Engineering |
| Content Type | Text |
| Resource Type | Article |
| Subject | Industrial and Manufacturing Engineering Artificial Intelligence Control and Systems Engineering Mechanical Engineering Electrical and Electronic Engineering Software |
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