Simultaneous Adaptive Estimation of the Attitude and Bias of Gyroscopes in Motion Using Accelerometer and GPS Data with Hardware Implementation

This paper presents a method for determining the attitude (Euler angles) of a moving object.One of the main challenges in attitude determination is the presence of inherent errors in inertial sensors and motion disturbances, including accelerations acting on the object during its movement, which lead to reduced accuracy in estimating Euler angles.In this paper, using an adaptive cascading fusion structure where estimation filters are used sequentially, information from various sensors including gyroscopes, accelerometers, and data received from Global Navigation Satellite System (GNSS receiver) are combined.This approach allows for accurate attitude determination and sensor bias estimation.Since solving the challenges of attitude determination (sensor biases and compensation for object motion disturbances) requires precise tuning of filter parameters, the use of adaptive filters in this structure leads to easier optimization of each filter's performance and error reduction.The evaluation of the proposed structure is carried out using two methods: utilizing synthetic data generated in a simulation scenario and also with real data collected in an experimental test.The results show that the proposed structure significantly reduces the error in estimation of angles and gyroscope biases compared to other approaches.