# Adaptive State-Separated UFIR Filter for Attitude Estimation Using MARG Sensors

## TL;DR

This paper introduces a new adaptive filter for estimating vehicle orientation using sensor data, which improves accuracy and efficiency.

## Contribution

The novel ASSUFIR algorithm adapts different estimation horizons for separate states using quaternions and QR decomposition.

## Key findings

- The ASSUFIR algorithm reduces computational burden by applying different horizons to different states.
- QR decomposition maintains computational efficiency despite structural changes.
- Experimental results show the method's effectiveness in real-world vehicle attitude estimation.

## Abstract

Unbiased Finite Impulse Response (UFIR) filters are widely used in engineering applications, such as vehicle attitude estimation, due to their advantages, including independence from initial conditions and insensitivity to noise. However, the performance of the UFIR filter heavily relies on the estimation horizon N, and different states within the system may exhibit an inverse correlation with respect to N, affecting the estimation results. To address this issue, this paper proposes an adaptive state-separated UFIR (ASSUFIR) filtering algorithm based on the properties of quaternions. By leveraging the relationship between quaternions and attitude angles, the algorithm reduces the computational burden of the batch UFIR filter estimation system, allowing different horizon lengths to be applied to different states. To mitigate the computational efficiency loss caused by disrupting the original UFIR filter structure, QR decomposition is introduced. The algorithm is first validated using simulated data and then compared with classical methods using real vehicle data. Experimental results demonstrate the practical applicability of the proposed method in engineering applications.