The position accuracy based on Decawave Ultra-Wideband (UWB) is affected mainly by three factors: hardware delays, clock drift, and signal power. This article discusses the last two factors. The general approach to clock drift correction uses the phase-locked loop (PLL) integrator, which we show is subject to signal power variations, and therefore, is less suitable for clock drift correction. The general approach to the estimation of signal power correction curves requires additional measurement equipment. This article presents a new method for obtaining the curve without additional hardware and clock drift correction without the PLL integrator. Both correction methods were fused together to improve two-way ranging (TWR).

Precise indoor localization is a major challenge in the field of localization. In this work we investigate multiple error corrections for the ultra-wideband (UWB) technology, in particular the DecaWave DW1000 transceiver. Both the time-of-arrival (TOA) and the time-difference-of-arrival (TDOA) methods are considered. Various clock-drift correction methods for TOA from the literature are reviewed and compared experimentally. The best performing method is extended to TDOA, corrections for the signal power dependence and the hardware delay are added, and two additional enhancements suggested. These are compared to each other and to TOA in positioning experiments. Index Terms-time-of-arrival (TOA), time-difference-of-arrival (TDOA), two-way-ranging (TWR), DecaWave, ultra-wideband (UWB)

In the field of localization the linear least square solution is frequently used. This solution is compared to nonlinear solvers more effected by noise, but able to provide a position estimation without the knowledge of any starting condition. The linear least square solution is able to minimize Gaussian noise by solving an overdetermined equation with the Moore-Penrose pseudoinverse. Unfortunately this solution fails if it comes to non Gaussian noise. This publication presents a direct solution which is able to use pre-filtered data for the LPM (RNL) equation. The used input for the linear position estimation will not be the raw data but over the time filtered data, for this reason this solution will be called direct solution. It will be shown that the presented symmetrical direct solution is superior to non symmetrical direct solution and especially to the not pre-filtered linear least square solution.

The quadratic system provided by the Time of Arrival technique can be solved analytically or by nonlinear least squares minimization. An important problem in quadratic optimization is the possible convergence to a local minimum, instead of the global minimum. This problem does not occur for Global Navigation Satellite Systems (GNSS), due to the known satellite positions. In applications with unknown positions of the reference stations, such as indoor localization with self-calibration, local minima are an important issue. This article presents an approach showing how this risk can be significantly reduced. The

The Local Position Measurement system (LPM) is one of the most precise systems for 3D position estimation. It is able to operate in-and outdoor and updates at a rate up to 1000 measurements per second. Previous scientific publications focused on the time of arrival equation (TOA) provided by the LPM and filtering after the numerical position estimation. This paper investigates the advantages of the TOA over the time difference of arrival equation transformation (TDOA) and the signal smoothing prior to its fitting. The LPM was designed under the general assumption that the position of the base station and position of the reference station are known. The information resulting from this research can prove vital for the system's selfcalibration, providing data aiding in locating the relative position of the base station without prior knowledge of the transponder and reference station positions.