Abstract
An algorithm for the detection and localization of magnetic targets is described. The algorithm uses overlapping segments of magnetic gradient data (which contain more information than more common single-channel magnetometer data), collected from a moving platform, to estimate the positions and moments of stationary dipole targets. Effects of platform motion are removed prior to localization. This motion compensation can be performed with attitude information from inertial measurements, if available. Subtracting a least-square fit to measurements from a vector magnetometer that is used as a reference can also perform the compensation. This general reference-subtraction method can be used to remove other forms of noise. In particular, it can be used to remove interference from active noise sources during operation on UUVs. The dipole parameters are estimated with a combination of linear (to find the moments) and nonlinear (to find the positions) least-squares fits. Multiple targets are handled with an iterative scheme, with new targets being successively added until there is no improvement in the fit. The implementation of this algorithm runs in real time, with only a short lag for processing.
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