A Logistic Regression Approach to Field Estimation Using Binary Measurements

TL;DR

This paper introduces an online logistic regression method for field estimation using binary measurements, offering a computationally efficient alternative to sequential Monte Carlo techniques with improved reliability.

Contribution

The paper proposes a novel online logistic regression approach for field estimation, reducing computational complexity and enhancing estimation accuracy compared to existing methods.

Findings

The logistic regression method is less computationally intensive.

It provides more reliable estimation performance.

It outperforms sequential Monte Carlo techniques in accuracy.

Abstract

In this letter, we consider the problem of field estimation using binary measurements. Previous work has formulated the problem as a parameter estimation problem, with the parameter estimation carried out in an online manner using sequential Monte Carlo techniques. In the current work, we consider an alternative approach to the parameter estimation based on online logistic regression. The developed algorithm is less computationally intensive than the sequential Monte Carlo approach, while having more reliable estimation performance.