Real-Time Detection of Moving Magnetic Target Using Distributed Scalar Sensor Based on Hybrid Algorithm of Particle Swarm Optimization and Gauss–Newton Method

Using a prior data to solve for the characteristic parameters of a moving magnetic target is crucial in its detection. For this purpose, we developed a real-time detection method using distributed scalar sensor networks based on a hybrid algorithm combining particle swarm optimization and the Gauss-Newton method. The magnetic anomaly fitting model of the target is established using the magnetic-dipole-moment principle. The PSO’s insensitivity to the initial solution is used to obtain a rough solution of the coefficients of the fitting, and then a more accurate solution is developed using the Gauss-Newton method, exploiting its advantage in high precision. Furthermore, to evaluate the proposed method, we built a test platform. The detection results show that the goodness-of-fit reaches 0.9565 and only requires 3.4 s; in the 16 m horizontal distance, the calculated trajectory overlaps significantly the actual trajectory, and errors in position and speed are around 10% and 4.25%, respectively.