Radial Basis Function Interpolation for Signal-Model-Independent Localization

In this paper, we propose a novel localization algorithm to be used in applications where the measurement model is neither accurate nor complete. In our algorithm, we apply radial basis function (RBF) interpolation to evaluate the measurement function on the entire surveillance area and, then, estimate the target position. Since the signal function is sparse in the spatial domain, we also propose to use sparse optimization techniques (LASSO) both to efficiently compute the weights for the RBF and to improve the interpolated function quality. Simulation results show good performance in the localization of single and multiple targets.

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