# Scalar Field Estimation with Mobile Sensor Networks

## TL;DR

This paper presents a novel method for estimating scalar fields using mobile sensors, leveraging adaptive control and Lyapunov techniques to ensure stability and convergence of the estimation process.

## Contribution

It introduces a new estimation algorithm for scalar fields with mobile sensors, incorporating stability analysis and motion planning for guaranteed convergence.

## Key findings

- Proposed an adaptive estimation algorithm with stability guarantees.
- Proved convergence of parameter estimates to true values.
- Demonstrated effectiveness through theoretical analysis.

## Abstract

In this paper, we consider the problem of estimating a scalar field using a network of mobile sensors which can measure the value of the field at their instantaneous location. The scalar field to be estimated is assumed to be represented by positive definite radial basis kernels and we use techniques from adaptive control and Lyapunov analysis to prove the stability of the proposed estimation algorithm. The convergence of the estimated parameter values to the true values is guaranteed by planning the motion of the mobile sensors to satisfy persistence-like conditions.