Abstract
Abstract A three-dimensional distance network is composed of a system of points or nodes located on earth’s surface or in space, in a building or in a construction site. Localization of these nodes is a fundamental operation in geodetic and sensor networks. This paper shows a new method for calculating n-dimensional dynamic distance networks, whereas a spectral decomposition of a symmetric matrix of squared distances is used. Thereby neither approximation, nor iterative solutions are used. By using this method, fixed reference points can be selected as well as noisy distances can be denoised and checked for consistency. Given a realistic scenario, Monte-Carlo simulations show that the proposed method always converge to an optimal solution with less computation time than numerically optimized Levenberg-Marquandt method.
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