# An Energy Detection Algorithm with Clustering-Based False Alarm Suppression for Magnetic Anomaly Detection

## TL;DR

This paper introduces a new magnetic anomaly detection algorithm that reduces false alarms using clustering and a physical model of magnetic fields.

## Contribution

The novel contribution is a clustering-based algorithm with a theoretically derived optimal cut height for suppressing false alarms in magnetic anomaly detection.

## Key findings

- The proposed algorithm significantly reduces false alarms caused by background noise.
- Simulation and field experiments confirm the algorithm's superior performance over traditional CFAR methods.
- The method preserves spatially coherent alarm clusters while eliminating isolated false alarms.

## Abstract

To overcome the limitations of Orthonormal Basis Function (OBF) methods in magnetic anomaly detection, including high false alarm rates and ambiguous target localization due to background noise, this paper introduces a high-confidence detection algorithm based on hierarchical clustering with an optimal cut height. The core of our approach is a theoretically derived optimal cut height, which is calculated from a physical model of the magnetic dipole’s vertical gradient field. This model establishes the implicit functional relationship between the effective detection range and key parameters, including magnetic moment orientation, geomagnetic inclination, and sensor height. The calculated optimal cut height serves as the critical criterion in a complete-linkage hierarchical clustering algorithm, which processes the alarm point clouds generated by a preliminary Greatest-of Cell-Averaging Constant False Alarm Rate (GOCA-CFAR) detector. This effectively suppresses isolated false alarms caused by background fluctuations while preserving spatially coherent alarm clusters within the target’s effective detection range, thereby significantly enhancing detection confidence. Results from both simulations and field experiments validate the efficacy of the proposed algorithm, demonstrating its superior capability to reliably discriminate genuine targets from false alarms compared to traditional one-dimensional CFAR detection.