2018/01/10 by Juri Sidorenko, Sidorenko, Juri, Norbert Scherer‐Negenborn +5
Engineering · #FOS: Electrical engineering #GNSS positioning and interference #Indoor and Outdoor Localization Technologies #Radio Wave Propagation Studies #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1801.03358
openalex publication_date 2018/01/10 · openalex created_date 2022/08/09 · openalex updated_date 2026/07/28
In the field of localization the linear least square solution is frequently\nused. This solution is compared to nonlinear solvers more effected by noise,\nbut able to provide a position estimation without the knowledge of any starting\ncondition. The linear least square solution is able to minimize Gaussian noise\nby solving an overdetermined equation with the MoorePenrose pseudoinverse.\nUnfortunately this solution fails if it comes to non Gaussian noise. This\npublication presents a direct solution which is able to use prefiltered data\nfor the LPM (RNL) equation. The used input for the linear position estimation\nwill not be the raw data but over the time filtered data, for this reason this\nsolution will be called direct solution. It will be shown that the presented\nsymmetrical direct solution is superior to non symmetrical direct solution and\nespecially to the not prefiltered linear least square solution.\n