Window-updated method for strap down inertial navigation systems based on adaptive neuron-fuzzy inference system

Abstract EN

The last two decades have shown an increasing trend in the use of navigation technologies such as Strap down Inertial Navigation Systems (SDINS) in several applications including land vehicles and automated car navigation.

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 INSs.

Although the huge efforts to improve SD1NS 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 paper proposed an intelligent navigator to overcome the limitations of existing INS algorithms.

The intelligent navigator is based on Adaptive Neuro-Fuzzy Inference System (ANFIS).

The proposed conceptual intelligent navigator consisted of SDINS architecture that was developed using adaptive fuzzy system networks to acquire the navigation knowledge.

In addition, a navigation information DataBase, and a window-based learned parameters updating method were implemented to store and accumulate navigation knowledge.