A robust position estimation algorithm for a local positioning measurement system

Pfeil, Reimar; Schuster, Stefan; Scherz, P.; Stelzer, Andreas; Stelzhammer, G.

doi:10.1109/imws2.2009.5307893

Cited by 9 publications

(7 citation statements)

References 5 publications

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“…The most recent publications on LPM have focused on the numerical solver. Generally, the LPM uses a Bancroft algorithm [26][27][28] to estimate the position of the tag. At this time, no work has been conducted on LPM self-calibration.…”

Section: Real Measurementsmentioning

confidence: 99%

Self-Calibration for the Time Difference of Arrival Positioning

Sidorenko

Schatz

Bulatov

et al. 2020

Sensors

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The time-difference-of-arrival (TDOA) self-calibration is an important topic for many applications, such as indoor navigation. One of the most common methods is to perform nonlinear optimization. Unfortunately, optimization often gets stuck in a local minimum. Here, we propose a method of dimension lifting by adding an additional variable into the l 2 norm of the objective function. Next to the usual numerical optimization, a partially-analytical method is suggested, which overdetermines the system of equations proportionally to the number of measurements. The effect of dimension lifting on the TDOA self-calibration is verified by experiments with synthetic and real measurements. In both cases, self-calibration is performed for two very common and often combined localization systems, the DecaWave Ultra-Wideband (UWB) and the Abatec Local Position Measurement (LPM) system. The results show that our approach significantly reduces the risk of becoming trapped in a local minimum.2 of 15 common and often combined systems are the DecaWave Ultra-Wideband (UWB) and the Abatec Local Position Measurement (LPM) system. The DecaWave UWB system is less affected by reflections, but is, due to regulations, limited in its transmitting power. The LPM system faces the opposite problem. However, here, the problem of self-localization becomes problematic, where neither the positions of the transmitters nor the receivers are known. This is analogous to the microphone-speaker problem, where systems of (sometimes redundant or self-contradicting) quadratic equations must be solved, sometimes resulting in zero and sometimes resulting in dozens of solutions for the minimum cases (see Chapter 10 in [2]). In the few related works outlined below, it is therefore commonplace to find overdetermined systems of equations. From a reliable solution and a suitable optimizer, these systems are developed to converge to the global minimum and thus successfully self-localize the sensors.The notations used in the text and in the equations are shown in Tables 1 and 2.

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Section: Real Measurementsmentioning

confidence: 99%

Self-Calibration for the Time Difference of Arrival Positioning

Sidorenko

Schatz

Bulatov

et al. 2020

Sensors

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“…An approach to outlier detection can be found in [3], were the linear Kalman filter in combination with the χ 2 test was used to detect outliers within the offset corrupted data. Nonlinear equation solving with Bancroft is analyzed in [8] [11] and compared with the least median of squares (LMS) in [2].…”

Section: Previous Workmentioning

confidence: 99%

Multilateration of the Local Position Measurement

Sidorenko

Scherer‐Negenborn

Arens

et al. 2016

2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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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.

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“…162-163] can be applied. However, to achieve a robust position estimate it is preferable to first acquire position estimates from a minimal set of measurements and then fuse the results using a least median of squares algorithm [49].…”

Section: Position Estimationmentioning

confidence: 99%

Robust Acoustic Positioning for Safety Applications in Underground Mining

Pfeil¹,

Pichler

Schuster

et al. 2015

IEEE Trans. Instrum. Meas.

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The surroundings of underground mining machines still constitute an unsafe area for miners due to bad visibility conditions. In this paper, we present a novel acoustic position estimation system for safety applications in such hazardous environments. Our system is based on a pulse compression technique and utilizes linear (LFM) and hyperbolic (HFM) frequency-modulated signals in the frequency range of 5-20 kHz. As the miners are moving, we incorporated a means of Dopplershift compensation. This system not only provides the positions of moving targets but also their velocity and direction. In stationary measurements, we evaluated LFM signals at noisy conditions both in an indoor laboratory environment and in the vicinity of a mining machine. The movement of a miner has been emulated in dynamic laboratory measurements at constant speeds up to 1 m/s using both LFM and HFM signals. Our results show that the acoustic signal can be evaluated down to a low signal-to-noise ratio of −30 dB. The results of the dynamic measurements clearly demonstrate the insensitivity of the HFM signals to Doppler-shifts both with regard to the estimated position and estimated velocity.

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A robust position estimation algorithm for a local positioning measurement system

Cited by 9 publications

References 5 publications

Self-Calibration for the Time Difference of Arrival Positioning

Self-Calibration for the Time Difference of Arrival Positioning

Multilateration of the Local Position Measurement

Robust Acoustic Positioning for Safety Applications in Underground Mining

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