Most of the positioning technologies for modern navigation systems have been available for the last 25 years that has focused on development of the Strapdown Inertial Navigation Systems (SDINS) because of its low cost. On the other hand it can cause large position errors over short time, due to the low quality of the inertial measurement unit (IMU). These errors determine the performance and the navigation accuracy of the INS. Although the huge efforts to improve SDINS in terms of its mechanization equations, it could not cover the remaining drawbacks of SDINS; such as the impact of INS short term errors, model dependency, prior knowledge dependency, sensor dependency, and computational errors. This work proposed an algorithm to overcome the limitations of existing INS algorithms. The alternative algorithm is based on Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed conceptual intelligent navigator consisted of INS architecture that was developed using adaptive fuzzy system networks to acquire the navigation knowledge. In addition, a navigation information database, and a window based weight updating scheme were implemented to store and accumulate navigation knowledge. The conceptual intelligent navigator was evaluated using several SDINS hypothetical field test data and the results demonstrated superior performance to the traditional navigator in the position and velocity domain. Finally, a low cost INS system (SDINS) was considered to verify the advantages gained by incorporating the conceptual intelligent navigator as an alternative method towards developing the next generation of navigation systems.