Automatic Search Algorithms for Near-Field Ferromagnetic Targets Based on Magnetic Anomaly Detection

Abstract

For searching and detecting near-field unknown ferromagnetic targets, four automatic search algorithms are proposed based on magnetic anomaly information from any position on planes or in space. Firstly, gradient search algorithms and enhanced gradient search algorithms are deduced using magnetic modulus anomaly information and magnetic vector anomaly information. In each algorithm, there are plane search forms and space search forms considering different practical search situations. Then the magnetic anomaly space data of typical magnetic source of oblique magnetization are forwardly simulated by ANSYS MAXWELL software. The plane distributions of some variables are numerically computed and the search destinations of different algorithms are predicted. Four automatic search algorithms are applied to simulate search paths on three characteristic orthogonal planes and in whole solution space. The factor affecting the performance of algorithms is analyzed. Features of each algorithm in different conditions are analyzed and suitable applications are discussed and verified by the experiment. The results show that proposed search algorithms require few prior information and have real-time performance for searching and tracking magnetic anomaly target.

A brief review of magnetic anomaly detection

“…In fact, because the measurement of the magnetic field is not able to avoid other magnetic fields in an open natural environment (including a relatively large magnetic field), the magnetic anomaly signal is usually buried deep in the magnetic noise and is easily ignored. Therefore, the effective detection algorithm is always required to maximize the detection probability [29]. Currently, there are many methods of dealing with MAD signals; in 2009, Ginzburg and Sheinker were focused on the working mechanisms of two categories of detection methods [30]: target-based methods and noise-based methods.…”