High-Precision Depth and Magnetic Moment Inversion of Buried Unexploded Ordnance Based on Particle Swarm Optimization Algorithm

A vertical gradiometer is constructed using two scalar magnetometers, with the vertical scalar gradient derived through the application of the Anderson function. The Particle Swarm Optimization (PSO) algorithm is then employed to estimate the target’s position, magnetic moment, and depth by integrating the detection trajectory, magnetic field, and vertical gradient. By thoroughly investigating the impact of computation time t_c and particle number N on algorithm precision, we propose an optimal configuration framework based on Monte Carlo simulation for achieving optimal precision at t_c=40s and N=100. This algorithm is validated via experiments conducted with a handheld detection system over a 96 m² area, where three types of targets are randomly placed. A comparative analysis of parameter inversion accuracy among the Genetic algorithm (GA), Least Squares Method (LSM), and PSO algorithms reveals that the PSO algorithm achieves the highest accuracy, with maximum spatial error e_{(x, y, z) max} = (0.10 m, 0.13 m, 0.10 m). Specifically, the vetical error is e_zmax ≤ 0.10 m and the average magnetic moment estimation error is e_ave = 5.23%.