# A Recursive Non-Uniform Sampling Estimator for Asynchronous Nonlinear Systems

## TL;DR

This paper introduces a new estimator for asynchronous nonlinear systems with packet losses, using interpolation and synchronization techniques to improve state estimation.

## Contribution

A novel recursive non-uniform sampling estimator is proposed for asynchronous nonlinear systems with packet losses.

## Key findings

- The proposed estimator effectively synchronizes asynchronous sampling systems using weighted state updates.
- Interpolation-based observation inference improves estimation accuracy despite modeling errors.
- The algorithm successfully extends to multi-sensor systems with distributed fusion estimation.

## Abstract

In this paper, we consider the problem of asynchronous estimation in the presence of packet losses for the randomly sampling nonlinear system. Packet losses occur at the control input and at the measurement side. Firstly, the synchronization of the asynchronous sampling system is realized by weighting the state of the adjacent state update points. Secondly, the projection theorem is used to estimate the system state at the sampling time. Due to modeling errors and unmodeled dynamics, obtaining an accurate dynamic model is challenging. Therefore, observation inference based on interpolation techniques is proposed to solve the asynchronous estimation problem. Furthermore, the algorithm is extended to multi-sensor systems to obtain a distributed fusion estimator. Finally, simulation experiments are conducted to validate the effectiveness of the algorithm.