# UAV Flight Altitude Measurement Based on AWA–AEIF Dual-Layer Information Fusion Algorithm

## TL;DR

This paper introduces a new method to improve UAV altitude measurement accuracy by combining ground and onboard systems with advanced data fusion techniques.

## Contribution

A dual-layer information fusion algorithm (AWA–AEIF) that significantly improves altitude measurement accuracy in UAVs.

## Key findings

- The proposed method reduces altitude RMSE from 4.05 m to 0.31 m in real flight tests.
- The AWA–AEIF algorithm achieves decimeter-level altitude accuracy by suppressing atmospheric drift and dynamic disturbances.

## Abstract

What are the main findings?
Architecture for reducing atmospheric effects in barometric altimetry.Method for reducing errors in barometric altimetry.

Architecture for reducing atmospheric effects in barometric altimetry.

Method for reducing errors in barometric altimetry.

What is the implication of the main finding?
The architecture is composed of a barometric reference station and a mobile station, enabling real-time differential pressure measurement.The method involves simultaneous barometric measurements using multiple barometer sets and data fusion with the AWA–AEIF algorithm.

The architecture is composed of a barometric reference station and a mobile station, enabling real-time differential pressure measurement.

The method involves simultaneous barometric measurements using multiple barometer sets and data fusion with the AWA–AEIF algorithm.

Accurate altitude estimation is critical for unmanned aerial vehicles (UAVs), yet barometric measurements are susceptible to atmospheric drift and dynamic disturbances. To address these limitations, this paper proposes a dual-layer, real-time differential barometric altimetry framework that integrates a ground reference station with an onboard fusion scheme based on Adaptive Weighted Averaging (AWA) and an Adaptive Extended Information Filter (AEIF). The ground reference station suppresses low-frequency atmospheric variations, while the onboard AEIF incorporates a physical pressure–height model and adaptive noise estimation to maintain a fast dynamic response. The proposed method is validated through numerical simulations, hardware-in-the-loop (HIL) experiments, and real flight tests. In a two-hour outdoor flight test, compared with barometric systems operating without a reference station, the proposed approach reduces the altitude RMSE from 4.05 m to 0.31 m, achieving an approximately order-of-magnitude improvement in representative scenarios and demonstrating decimeter-level altitude measurement accuracy.