Magnetic Anomaly Detection Using Three-Axis Magnetoelectric Sensors Based on the Hybridization of Particle Swarm Optimization and Simulated Annealing Algorithm

Abstract

The application of high-performance magnetic sensors and the parameter estimation of ferromagnetic target from magnetic anomaly data are two important parts in magnetic anomaly detection (MAD) technique. In this work, we proposed a detection approach using a self-designed high-sensitivity three-axis magnetoelectric (ME) magnetic sensor based on a hybrid algorithm of particle swarm optimization (PSO) and simulated annealed (SA) algorithm. The magnetic dipole model is utilized as a universal model in the parameter estimation. Combining the strong global exploration ability and fast convergence of PSO algorithm with the probabilistic acceptance criterion in SA algorithm, the hybridization entitled PSO-SA algorithm for magnetic anomaly detection is expounded in detail. The ME sensor was applied to construct a test platform with an equivalent magnetic noise (EMN) lower than 26.08pT<inline-formula> <tex-math notation="LaTeX">$/ \surd $ </tex-math></inline-formula> Hz at 1Hz. The proposed method was evaluated by three controlled experiments performed on the test platform using the ME sensor. Based on the observed results for the designed experiments, a high fitting level has been demonstrated by the goodness indexes of the proposed algorithm as high as 0.9182, 0.9287 and 0.9320 for the three experimental data, respectively. Moreover, the characteristics of the estimated parameters are in good agreement with the experimental design, which proves the reliability of the proposed algorithm and reveals its universality in magnetic anomaly detection as well.