# Distributed State Estimation for Flapping-Wing Micro Air Vehicles with Information Fusion Correction

## TL;DR

This paper proposes a new method for estimating the state of flapping-wing micro air vehicles using a networked system with improved accuracy.

## Contribution

The novel contribution is an information-fusion-based estimator model that improves estimation accuracy and topological information completeness in FMAVs.

## Key findings

- An information fusion function was introduced to enhance the FMAV state estimation model.
- A Lyapunov–Krasovskii functional and refined inequalities were used to derive less conservative estimator design conditions.
- Simulation results confirmed the effectiveness and superiority of the proposed estimator.

## Abstract

In this paper, we explore a nonlinear interactive network system comprising nodalized flapping-wing micro air vehicles (FMAVs) to address the distributed H∞ state estimation problem associated with FMAVs. We enhance the model by introducing an information fusion function, leading to an information-fusionized estimator model. This model ensures both estimation accuracy and the completeness of FMAV topological information within a unified framework. To facilitate the analysis, each FMAV’s received signal is individually sampled using independent and time-varying samplers. Transforming the received signals into equivalent bounded time-varying delays through the input delay method yields a more manageable and analyzable time-varying nonlinear network error system. Subsequently, we construct a Lyapunov–Krasovskii functional (LKF) and integrate it with the refined Wirtinger and relaxed integral inequalities to derive design conditions for the FMAVs’ distributed H∞ state estimator, minimizing conservatism. Finally, we validate the effectiveness and superiority of the designed estimator through simulations.