Optimization of intelligent-based approach for low-cost INS/GPS navigation system

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 measurements integration is optimized based on different Neural Networks (NN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) architectures to achieve high-accuracy vehicle state estimates. The proposed approaches involve the use of NN based architectures as well as ANFIS architectures with 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 accuracy of the proposed AI approaches. The obtained results are presented and discussed. The study concludes that using ANFIS, with both position and velocity as input, provides the best estimates of position and velocity in the navigation system. This Input Delayed ANFIS (IDANFIS) approach is further analyzed at the end of the paper.