Adaptive coherence estimator (ACE) for explosive hazard detection using wideband electromagnetic induction (WEMI)

TL;DR

This paper presents an adaptive coherence estimator (ACE) method for detecting buried explosive hazards using wideband electromagnetic induction data, leveraging background statistics to improve detection accuracy.

Contribution

It introduces an ACE-based detection approach utilizing a DSRF model for target signatures in WEMI data, enhancing explosive hazard detection performance.

Findings

ACE detection improves hazard identification accuracy.

Results show superior ROC performance compared to other methods.

Background whitening significantly impacts detection success.

Abstract

The adaptive coherence estimator (ACE) estimates the squared cosine of the angle between a known target vector and a sample vector in a whitened coordinate space. The space is whitened according to an estimation of the background statistics, which directly effects the performance of the statistic as a target detector. In this paper, the ACE detection statistic is used to detect buried explosive hazards with data from a Wideband Electromagnetic Induction (WEMI) sensor. Target signatures are based on a dictionary defined using a Discrete Spectrum of Relaxation Frequencies (DSRF) model. Results are summarized as a receiver operator curve (ROC) and compared to other leading methods.