# Sequential Fusion Estimation for Clustered Sensor Networks

## TL;DR

This paper introduces sequential fusion estimation methods for clustered sensor networks that match batch performance but are more efficient and better suited for asynchronous data, demonstrated through target tracking simulations.

## Contribution

It proposes novel sequential fusion estimation techniques that are computationally more efficient and handle asynchronous data better than traditional batch methods.

## Key findings

- Sequential methods match batch fusion performance.
- Sequential measurement fusion has lower computational complexity.
- Simulations confirm effectiveness in target tracking.

## Abstract

We consider multi-sensor fusion estimation for clustered sensor networks. Both sequential measurement fusion and state fusion estimation methods are presented. It is shown that the proposed sequential fusion estimation methods achieve the same performance as the batch fusion one, but are more convenient to deal with asynchronous or delayed data since they are able to handle the data that is available sequentially. Moreover, the sequential measurement fusion method has lower computational complexity than the conventional sequential Kalman estimation and the measurement augmentation methods, while the sequential state fusion method is shown to have lower computational complexity than the batch state fusion one. Simulations of a target tracking system are presented to demonstrate the effectiveness of the proposed results.